U.S. patent application number 16/023877 was filed with the patent office on 2018-12-27 for robotic systems for determining a roll of a medical device in luminal networks.
The applicant listed for this patent is Auris Health, Inc.. Invention is credited to Ritwik Ummalaneni.
Application Number | 20180368920 16/023877 |
Document ID | / |
Family ID | 62837432 |
Filed Date | 2018-12-27 |
United States Patent
Application |
20180368920 |
Kind Code |
A1 |
Ummalaneni; Ritwik |
December 27, 2018 |
ROBOTIC SYSTEMS FOR DETERMINING A ROLL OF A MEDICAL DEVICE IN
LUMINAL NETWORKS
Abstract
Certain aspects relate to systems and techniques for
navigation-assisted medical devices. Some aspects relate to
correlating features of depth information generated based on
captured images of an anatomical luminal network with virtual
features of depth information generated based on virtual images of
a virtual representation of the anatomical luminal network in order
to automatically determine aspects of a roll of a medical device
within the luminal network.
Inventors: |
Ummalaneni; Ritwik; (San
Mateo, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Auris Health, Inc. |
Redwood City |
CA |
US |
|
|
Family ID: |
62837432 |
Appl. No.: |
16/023877 |
Filed: |
June 29, 2018 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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15631691 |
Jun 23, 2017 |
10022192 |
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16023877 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
A61B 2090/309 20160201;
A61B 6/102 20130101; A61B 6/488 20130101; A61B 6/466 20130101; A61B
6/0487 20200801; B25J 9/1694 20130101; A61B 6/463 20130101; A61B
6/487 20130101; A61B 5/06 20130101; A61B 34/20 20160201; A61B
2017/00477 20130101; A61B 6/032 20130101; A61B 2090/306 20160201;
A61B 6/12 20130101; A61B 6/4441 20130101; A61B 2017/00809 20130101;
A61B 34/70 20160201; A61B 2034/105 20160201; A61B 2034/2065
20160201; A61B 2034/301 20160201; A61B 34/30 20160201; A61B 5/062
20130101; A61B 2034/2051 20160201; A61B 2090/3614 20160201 |
International
Class: |
A61B 34/20 20060101
A61B034/20; B25J 9/16 20060101 B25J009/16; A61B 6/12 20060101
A61B006/12; A61B 34/00 20060101 A61B034/00; A61B 34/30 20060101
A61B034/30; A61B 5/06 20060101 A61B005/06 |
Claims
1. A method of facilitating navigation of an anatomical luminal
network of a patient, the method, executed by a set of one or more
computing devices, comprising: receiving imaging data captured by
an imaging device at a distal end of an instrument positioned
within the anatomical luminal network, wherein the anatomical
luminal network includes a right bronchus and a left bronchus;
identifying one or more features from the imaging data, the one or
more features identified from the imaging data representing a first
positioning of the right bronchus and the left bronchus in the
imaging data; accessing one or more features associated with a
virtual image, the virtual image simulated from a viewpoint of a
virtual imaging device positioned at a virtual location within a
virtual luminal network representative of the anatomical luminal
network, the virtual luminal network including a virtual right
bronchus corresponding to the right bronchus and a virtual left
bronchus corresponding to the left bronchus, wherein the one or
more features associated with the virtual image represent a second
positioning of the virtual right bronchus and the virtual left
bronchus in the virtual image; calculating a correspondence between
the first positioning and the second positioning; and determining a
roll of the distal end of the instrument within the anatomical
luminal network based on the calculated correspondence.
2. The method of claim 1, wherein the anatomical luminal network
includes a trachea leading into the right bronchus and the left
bronchus, the method further comprising determining an insertion
depth of the distal end of the instrument within the trachea based
at least partly on the virtual location of the virtual image.
3. The method of claim 2, further comprising: generating an
electromagnetic field around the anatomical luminal network; and
determining a registration between a coordinate frame of the
virtual luminal network and a coordinate frame of the
electromagnetic field based at least partly on the insertion depth
and roll of the distal end of the instrument.
4. The method of claim 1, further comprising: generating a depth
map based on the imaging data; and identifying, based on the depth
map, the first positioning of the right bronchus and the left
bronchus in the imaging data; wherein the one or more features
associated with the virtual image comprise the second positioning
derived from a virtual depth map associated with a virtual
image.
5. The method of claim 4, further comprising: generating the depth
map by calculating, for each pixel of a plurality of pixels of the
imaging data, a depth value representing an estimated distance
between the imaging device and a tissue surface within the
anatomical luminal network corresponding to the pixel; identifying
a first pixel of the plurality of pixels corresponding to a first
depth criterion in the depth map; determining a first location of
the right bronchus in the imaging data based on a position of the
first pixel; identifying a second pixel of the plurality of pixels
corresponding to a second depth criterion in the depth map; and
determining a second location of the left bronchus in the imaging
data based on a position of the second pixel.
6. The method of claim 5, further comprising: determining the first
pixel has a greater depth value than the second pixel; and
identifying that the first location corresponds to the right
bronchus based on determining the first pixel has the greater depth
value.
7. The method of claim 5, wherein the first depth criterion
represents a most distant imaged tissue within the right bronchus,
and wherein the second depth criterion represents a most distant
imaged tissue within the left bronchus.
8. The method of claim 5, wherein the first depth criterion
corresponds to a first local maximum in a first region of depth
values around the first pixel, and wherein the second depth
criterion corresponds to a second local maximum in a second region
of depth values around the second pixel.
9. The method of claim 4, further comprising determining the roll
of the instrument based on an angular distance between a first
position of the right bronchus in the imaging data and a second
position of the virtual right bronchus in the virtual image.
10. A system configured to facilitate navigation of an anatomical
luminal network of a patient, the system comprising: an imaging
device at a distal end of an instrument; at least one
computer-readable memory having stored thereon executable
instructions; and one or more processors in communication with the
at least one computer-readable memory and configured to execute the
instructions to cause the system to at least: receive imaging data
captured by the imaging device with the distal end of the
instrument positioned within the anatomical luminal network,
wherein the anatomical luminal network includes a right bronchus
and a left bronchus; identify one or more features from the imaging
data, the one or more features identified from the imaging data
representing a first positioning of the right bronchus and the left
bronchus in the imaging data; access one or more features
associated with a virtual image, the virtual image simulated from a
viewpoint of a virtual imaging device positioned at a virtual
location within a virtual luminal network representative of the
anatomical luminal network, the virtual luminal network including a
virtual right bronchus corresponding to the right bronchus and a
virtual left bronchus corresponding to the left bronchus, wherein
the one or more features associated with the virtual image
represent a second positioning of the virtual right bronchus and
the virtual left bronchus in the virtual image; calculate a
correspondence between first positioning and the second
positioning; and determine a roll of the distal end of the
instrument within the anatomical luminal network based on the
calculated correspondence.
11. The system of claim 10, wherein the anatomical luminal network
includes a trachea leading into the right bronchus and the left
bronchus, and wherein the one or more processors are configured to
execute the instructions to cause the system to at least determine
an insertion depth of the distal end of the instrument within the
trachea based at least partly on the virtual location of the
virtual image.
12. The system of claim 11, wherein the one or more processors are
configured to execute the instructions to cause the system to at
least: generate an electromagnetic field around the anatomical
luminal network; and determine a registration between a coordinate
frame of the virtual luminal network and a coordinate frame of the
electromagnetic field based at least partly on the insertion depth
and roll of the distal end of the instrument.
13. The system of claim 10, wherein the one or more processors are
configured to execute the instructions to cause the system to at
least: generate a depth map based on the imaging data; and
identify, based on the depth map, the first positioning of the
right bronchus and the left bronchus in the imaging data; wherein
the one or more features associated with the virtual image comprise
the second positioning derived from a virtual depth map associated
with a virtual image.
14. The system of claim 13, wherein the one or more processors are
configured to execute the instructions to cause the system to at
least: generate the depth map by calculating, for each pixel of a
plurality of pixels of the imaging data, a depth value representing
an estimated distance between the imaging device and a tissue
surface within the anatomical luminal network corresponding to the
pixel; identify a first pixel of the plurality of pixels
corresponding to a first depth criterion in the depth map;
determine a first location of the right bronchus in the imaging
data based on a position of the first pixel; identify a second
pixel of the plurality of pixels corresponding to a second depth
criterion in the depth map; and determine a second location of the
left bronchus in the imaging data based on a position of the second
pixel.
15. The system of claim 15, wherein the one or more processors are
configured to execute the instructions to cause the system to at
least: determine the first pixel has a greater depth value than the
second pixel; and identify that the first location corresponds to
the right bronchus based on determining the first pixel has the
greater depth value.
16. The system of claim 14, wherein the first depth criterion
represents a most distant imaged tissue within the right bronchus,
and wherein the second depth criterion represents a most distant
imaged tissue within the left bronchus.
17. The system of claim 14, wherein the first depth criterion
corresponds to a first local maximum in a first region of depth
values around the first pixel, and wherein the second depth
criterion corresponds to a second local maximum in a second region
of depth values around the second pixel.
18. The system of claim 10, wherein the one or more processors are
configured to execute the instructions to cause the system to at
least determine the roll of the instrument based on an angular
distance between a first position of the right bronchus in the
imaging data and a second position of the virtual right bronchus in
the virtual image.
19. A non-transitory computer readable storage medium having stored
thereon instructions that, when executed, cause at least one
computing device to at least: receive imaging data captured by the
imaging device with the distal end of the instrument positioned
within the anatomical luminal network, wherein the anatomical
luminal network includes a right bronchus and a left bronchus;
identify one or more features of the imaging data, the one or more
features identified from the imaging data representing a first
positioning of the right bronchus and the left bronchus in the
imaging data; access one or more features associated with a virtual
image, the virtual image simulated from a viewpoint of a virtual
imaging device positioned at a virtual location within a virtual
luminal network representative of the anatomical luminal network,
the virtual luminal network including a virtual right bronchus
corresponding to the right bronchus and a virtual left bronchus
corresponding to the left bronchus, wherein the one or more
features associated with the virtual image represent a second
positioning of the virtual right bronchus and the virtual left
bronchus in the virtual image; calculate a correspondence between
first positioning and the second positioning; and determine a roll
of the distal end of the instrument within the anatomical luminal
network based on the calculated correspondence.
20. The non-transitory computer readable storage medium of claim
19, wherein the anatomical luminal network includes a trachea
leading into the right bronchus and the left bronchus, and wherein
the instructions, when executed, cause the at least one computing
device to at least determine an insertion depth of the distal end
of the instrument within the trachea based at least partly on the
virtual location of the virtual image.
21. The non-transitory computer readable storage medium of claim
20, wherein the instructions, when executed, cause the at least one
computing device to at least: generate an electromagnetic field
around the anatomical luminal network; and determine a registration
between a coordinate frame of the virtual luminal network and a
coordinate frame of the electromagnetic field based at least partly
on the insertion depth and roll of the distal end of the
instrument.
22. The non-transitory computer readable storage medium of claim
19, wherein the instructions, when executed, cause the at least one
computing device to at least: generate a depth map based on the
imaging data; and identify, based on the depth map, the first
positioning of the right bronchus and the left bronchus in the
imaging data; wherein the one or more features associated with the
virtual image comprise the second positioning derived from a
virtual depth map associated with a virtual image.
23. The non-transitory computer readable storage medium of claim
22, wherein the instructions, when executed, cause the at least one
computing device to at least: generate the depth map by
calculating, for each pixel of a plurality of pixels of the imaging
data, a depth value representing an estimated distance between the
imaging device and a tissue surface within the anatomical luminal
network corresponding to the pixel; identify a first pixel of the
plurality of pixels corresponding to a first depth criterion in the
depth map; determine a first location of the right bronchus in the
imaging data based on a position of the first pixel; identify a
second pixel of the plurality of pixels corresponding to a second
depth criterion in the depth map; and determine a second location
of the left bronchus in the imaging data based on a position of the
second pixel.
24. The non-transitory computer readable storage medium of claim
23, wherein the instructions, when executed, cause the at least one
computing device to at least: determine the first pixel has a
greater depth value than the second pixel; and identify that the
first location corresponds to the right bronchus based on
determining the first pixel has the greater depth value.
25. The non-transitory computer readable storage medium of claim
23, wherein the first depth criterion represents a most distant
imaged tissue within the right bronchus, and wherein the second
depth criterion represents a most distant imaged tissue within the
left bronchus.
26. The non-transitory computer readable storage medium of claim
23, wherein the first depth criterion corresponds to a first local
maximum in a first region of depth values around the first pixel,
and wherein the second depth criterion corresponds to a second
local maximum in a second region of depth values around the second
pixel.
27. The non-transitory computer readable storage medium of claim
19, wherein the instructions, when executed, cause the at least one
computing device to at least determine the roll of the instrument
based on an angular distance between a first position of the right
bronchus in the imaging data and a second position of the virtual
right bronchus in the virtual image.
Description
CROSS-REFERENCE TO RELATED APPLICATION(S)
[0001] The present application is a continuation of U.S. patent
application Ser. No. 15/631,691, filed on Jun. 23, 2017, entitled
"AUTOMATICALLY-INITIALIZED ROBOTIC SYSTEMS FOR NAVIGATION OF
LUMINAL NETWORKS," the content of which is hereby incorporated by
reference herein in its entirety.
TECHNICAL FIELD
[0002] The systems and methods disclosed herein are directed to
medical procedures, and more particularly to navigation-assisted
medical devices.
BACKGROUND
[0003] Medical procedures such as endoscopy (e.g., bronchoscopy)
may involve accessing and visualizing the inside of a patient's
lumen (e.g., airways) for diagnostic and/or therapeutic purposes.
During a procedure, a flexible tubular tool such as, for example,
an endoscope, may be inserted into the patient's body and an
instrument can be passed through the endoscope to a tissue site
identified for diagnosis and/or treatment.
[0004] Bronchoscopy is a medical procedure that allows a physician
to examine the inside conditions of a patient's lung airways, such
as bronchi and bronchioles. During the medical procedure, a thin,
flexible tubular tool, known as a bronchoscope, may be inserted
into the patient's mouth and passed down the patient's throat into
his/her lung airways towards a tissue site identified for
subsequent diagnosis and treatment. The bronchoscope can have an
interior lumen (a "working channel") providing a pathway to the
tissue site, and catheters and various medical tools can be
inserted through the working channel to the tissue site.
SUMMARY
[0005] An endoscopy navigation system can use a fusion of different
sensing modalities (e.g., scope imaging data, electromagnetic (EM)
position data, robotic position data, etc.) modeled, for example,
through adaptively-adjusted probabilities. A probabilistic
navigation approach or other navigation approach may depend on an
initial estimate of "where" the tip of the endoscope is--for
example, an estimate of which airway, how deep into this airway,
and how much roll in this airway--in order to begin tracking the
tip of the endoscope. Some endoscopy techniques can involve a
three-dimensional (3D) model of a patient's anatomy, and can guide
navigation using an EM field and position sensors. At the outset of
a procedure, the precise alignment (e.g., registration) between the
virtual space of the 3D model, the physical space of the patient's
anatomy represented by the 3D model, and the EM field may be
unknown. As such, prior to generating a registration or in
situations where the accuracy of an existing registration is in
question, endoscope positions within the patient's anatomy cannot
be mapped with precision to corresponding locations within the 3D
model.
[0006] Typically, a navigation system requires the physician to
undergo a series of initialization steps in order to generate this
initial estimate. This can involve, for example, instructing the
physician to position a bronchoscope at a number of specific
positions and orientations relative to landmark(s) within the
bronchial tree (e.g., by touching the main carina, the left carina,
and the right carina). Another option requires the physician to
perform an initial airway survey, for example, starting in the
mid-trachea and entering each lobe while attempting to maintain a
centered position of the bronchoscope tip within each airway.
[0007] Such initialization steps can provide an initial estimate of
the endoscope position; however, such an approach may have several
potential drawbacks including adding additional time requirements
to the beginning of the procedure. Another potential drawback
relates to the fact that, after the initialization has been
completed and tracking is occurring, an adverse event (e.g.,
patient coughing, dynamic airway collapse) can create uncertainty
about the actual position of the endoscope. This can necessitate
determination of a new "initial" position, and accordingly the
navigation system may require the physician to navigate back to the
trachea to re-perform the initialization steps. Such backtracking
adds additional time requirements that can be particularly
burdensome if the adverse event occurs after the endoscope has been
navigated through the smaller peripheral airways toward a target
site.
[0008] The aforementioned issues, among others, are addressed by
the luminal network navigation systems and techniques described
herein. The disclosed techniques can generate a 3D model of a
virtual luminal network representing the patient's anatomical
luminal network and can determine a number of locations within the
virtual luminal network at which to position a virtual camera. The
disclosed techniques can generate a virtual depth map representing
distances between the internal surfaces of the virtual luminal
network and the virtual camera positioned at a determined location.
Features can be extracted from these virtual depth maps, for
example, peak-to-peak distance in the case of a virtual depth map
representing an airway bifurcation, and the extracted features can
be stored in association with the location of the virtual camera.
During the medical procedure, the distal end of an endoscope can be
provided with an imaging device, and the disclosed navigation
techniques can generate a depth map based on image data received
from the imaging devices. The disclosed techniques can derive
features from the generated depth map, calculate correspondence
between the extracted features with the stored features extracted
from one of the virtual depth maps, and then use the associated
virtual camera location as the initial position of the distal end
of the instrument. Beneficially, such techniques allow a
probabilistic navigation system (or other navigation systems) to
obtain an initial estimate of scope position without requiring the
manual initialization steps described above. In addition, the
disclosed techniques can be used throughout a procedure to refine
registration and, in some embodiments, can provide an additional
"initial estimate" after an adverse event without requiring
navigation back through the luminal network to a landmark
anatomical feature.
[0009] Accordingly, one aspect relates to a method of facilitating
navigation of an anatomical luminal network of a patient, the
method, executed by a set of one or more computing devices,
comprising receiving imaging data captured by an imaging device at
a distal end of an instrument positioned within the anatomical
luminal network; accessing a virtual feature derived from a virtual
image simulated from a viewpoint of a virtual imaging device
positioned at a virtual location within a virtual luminal network
representative of the anatomical luminal network; calculating a
correspondence between a feature derived from the imaging data and
the virtual feature derived from the virtual image; and determining
a pose of the distal end of the instrument within the anatomical
luminal network based on the virtual location associated with the
virtual feature.
[0010] In some embodiments, the method further comprises generating
a depth map based on the imaging data, wherein the virtual feature
is derived from a virtual depth map associated with the virtual
image, and wherein calculating the correspondence is based at least
partly on correlating one or more features of the depth map and one
or more features of the virtual depth map.
[0011] In some embodiments, the method further comprises generating
the depth map by calculating, for each pixel of a plurality of
pixels of the imaging data, a depth value representing an estimated
distance between the imaging device and a tissue surface within the
anatomical luminal network corresponding to the pixel; identifying
a first pixel of the plurality of pixels corresponding to a first
depth criterion in the depth map and a second pixel of the
plurality of pixels corresponding to a second depth criterion in
the depth map; calculating a first value representing a distance
between the first and second pixels; wherein the virtual depth map
comprises, for each virtual pixel of a plurality of virtual pixels,
a virtual depth value representing a virtual distance between the
virtual imaging device and a portion of the virtual luminal network
represented by the virtual pixel, and wherein accessing the virtual
feature derived from the virtual image comprises accessing a second
value representing a distance between first and second depth
criteria in the virtual depth map; and calculating the
correspondence based on comparing the first value to the second
value.
[0012] In some embodiments, the method further comprises accessing
a plurality of values representing distances between first and
second depth criteria in a plurality of virtual depth maps each
representing a different one of a plurality of virtual locations
within the virtual luminal network; and calculating the
correspondence based on the second value corresponding more closely
to the first value than other values of the plurality of values. In
some embodiments the anatomical luminal network comprises airways
and the imaging data depicts a bifurcation of the airways, and the
method further comprises identifying one of the first and second
depth criteria as a right bronchus in each of the depth map and the
virtual depth map; and determining a roll of the instrument based
on an angular distance between a first position of the right
bronchus in the depth map and a second position of the right
bronchus in the virtual depth map, wherein the pose of the distal
end of the instrument within the anatomical luminal network
comprises the determined roll.
[0013] In some embodiments, the method further comprises
identifying three or more depth criteria in each of the depth map
and the virtual depth map; determining a shape and location of a
polygon connecting the depth criteria in each of the depth map and
the virtual depth map; and calculating the correspondence based on
comparing the shape and location of the polygon of the depth map to
the shape and location of the polygon of the virtual depth map. In
some embodiments, generating the depth map is based on
photoclinometry.
[0014] In some embodiments, the method further comprises
calculating a probabilistic state of the instrument within the
anatomical luminal network based on a plurality of inputs
comprising the position; and guiding navigation of the instrument
through the anatomical luminal network based at least partly on the
probabilistic state. In some embodiments, the method further
comprises initializing a navigation system configured to calculate
the probabilistic state and guide the navigation of the anatomical
luminal network based on the probabilistic state, wherein the
initializing of the navigation system comprises setting a prior of
a probability calculator based on the position. In some
embodiments, the method further comprises receiving additional data
representing an updated pose of the distal end of the instrument;
setting a likelihood function of the probability calculator based
on the additional data; and determining the probabilistic state
using the probability calculator based on the prior and the
likelihood function.
[0015] In some embodiments, the method further comprises providing
the plurality of inputs to a navigation system configured to
calculate the probabilistic state, a first input comprising the
pose of the distal end of the instrument and at least one
additional input comprising one or both of robotic position data
from a robotic system actuating movement of the instrument and data
received from a position sensor at the distal end of the
instrument; and calculating the probabilistic state of the
instrument based on the first input and the at least one additional
input.
[0016] In some embodiments, the method further comprises
determining a registration between a coordinate frame of the
virtual luminal network and a coordinate frame of an
electromagnetic field generated around the anatomical luminal
network based at least partly on the pose of the distal end of the
instrument within the anatomical luminal network determined based
on the calculated correspondence. In some embodiments, determining
the position comprises determining a distance that the distal end
of the instrument is advanced within a segment of the anatomical
luminal network.
[0017] Another aspect relates to a system configured to facilitate
navigation of an anatomical luminal network of a patient, the
system comprising an imaging device at a distal end of an
instrument; at least one computer-readable memory having stored
thereon executable instructions; and one or more processors in
communication with the at least one computer-readable memory and
configured to execute the instructions to cause the system to at
least receive imaging data captured by the imaging device with the
distal end of the instrument positioned within the anatomical
luminal network; access a virtual feature derived from a virtual
image simulated from a viewpoint of a virtual imaging device
positioned at a virtual location within a virtual luminal network
representative of the anatomical luminal network; calculate a
correspondence between a feature derived from the imaging data and
the virtual feature derived from the virtual image; and determine a
pose of the distal end of the instrument relative within the
anatomical luminal network based on the virtual location associated
with the virtual feature.
[0018] In some embodiments, the one or more processors are
configured to execute the instructions to cause the system to at
least generate a depth map based on the imaging data, wherein the
virtual image represents a virtual depth map; and determine the
correspondence based at least partly on correlating one or more
features of the depth map and one or more features of the virtual
depth map. In some embodiments, the one or more processors are
configured to execute the instructions to cause the system to at
least generate the depth map by calculating, for each pixel of a
plurality of pixels of the imaging data, a depth value representing
an estimated distance between the imaging device and a tissue
surface within the anatomical luminal network corresponding to the
pixel; identify a first pixel of the plurality of pixels
corresponding to a first depth criterion in the depth map and a
second pixel of the plurality of pixels corresponding to a second
depth criterion in the depth map; calculate a first value
representing a distance between the first and second pixels;
wherein the virtual depth map comprises, for each virtual pixel of
a plurality of virtual pixels, a virtual depth value representing a
virtual distance between the virtual imaging device and a portion
of the virtual luminal network represented by the virtual pixel,
and wherein the feature derived from the virtual image comprises a
second value representing a distance between first and second depth
criteria in the virtual depth map; and determine the correspondence
based on comparing the first value to the second value.
[0019] In some embodiments, the one or more processors are
configured to execute the instructions to cause the system to at
least access a plurality of values representing distances between
first and second depth criteria in a plurality of virtual depth
maps each representing a different one of a plurality of virtual
locations within the virtual luminal network; and calculate the
correspondence based on the second value corresponding more closely
to the first value than other values of the plurality of values
identify the second value as a closest match to the first value
among the plurality of values. In some embodiments, the anatomical
luminal network comprises airways and the imaging data depicts a
bifurcation of the airways, and the one or more processors are
configured to execute the instructions to cause the system to at
least identify one of the first and second depth criteria as a
right bronchus in each of the depth map and the virtual depth map;
and determine a roll of the instrument based on an angular distance
between a first position of the right bronchus in the depth map and
a second position of the right bronchus in the virtual depth map,
wherein the pose of the distal end of the instrument within the
anatomical luminal network comprises the determined roll.
[0020] In some embodiments, the one or more processors are
configured to execute the instructions to cause the system to at
least identify three or more depth criteria in each of the depth
map and the virtual depth map; determine a shape and location of a
polygon connecting the three or more depth criteria in each of the
depth map and the virtual depth map; and calculate the
correspondence based on comparing the shape and location of the
polygon of the depth map to the shape and location of the polygon
of the virtual depth map. In some embodiments, the one or more
processors are configured to execute the instructions to cause the
system to at least generate the depth map based on
photoclinometry.
[0021] In some embodiments, the one or more processors are
configured to communicate with a navigation system, and wherein the
one or more processors are configured to execute the instructions
to cause the system to at least calculate a probabilistic state of
the instrument within the anatomical luminal network using the
navigation system based at least partly on a plurality of inputs
comprising the position; and guide navigation of the instrument
through the anatomical luminal network based at least partly on the
probabilistic state calculated by the navigation system. Some
embodiments of the system further comprise a robotic system
configured to guide movements of the instrument during the
navigation. In some embodiments, the plurality of inputs comprise
robotic position data received from the robotic system, and wherein
the one or more processors are configured to execute the
instructions to cause the system to at least calculate the
probabilistic state of the instrument using the navigation system
based at least partly on the position and on the robotic position
data. Some embodiments of the system further comprise a position
sensor at the distal end of an instrument, the plurality of inputs
comprise data received from the position sensor, and wherein the
one or more processors are configured to execute the instructions
to cause the system to at least calculate the probabilistic state
of the instrument using the navigation system based at least partly
on the position and on the data received from the position sensor.
In some embodiments, the one or more processors are configured to
execute the instructions to cause the system to at least determine
a registration between a coordinate frame of the virtual luminal
network and a coordinate frame of an electromagnetic field
generated around the anatomical luminal network based at least
partly on the position.
[0022] Another aspect relates to a non-transitory computer readable
storage medium having stored thereon instructions that, when
executed, cause at least one computing device to at least access a
virtual three-dimensional model of internal surfaces of an
anatomical luminal network of a patient; identify a plurality of
virtual locations within the virtual three-dimensional model; for
each virtual location of the plurality of virtual locations within
the virtual three-dimensional model generate a virtual depth map
representing virtual distances between a virtual imaging device
positioned at the virtual location and a portion of the internal
surfaces within a field of view of the virtual imaging device when
positioned at the virtual location, and derive at least one virtual
feature from the virtual depth map; and generate a database
associating the plurality of virtual locations with the at least
one virtual feature derived from the corresponding virtual depth
map.
[0023] In some embodiments the instructions, when executed, cause
the at least one computing device to at least provide the database
to a navigation system configured to guide navigation of an
instrument through the anatomical luminal network during a medical
procedure. In some embodiments the instructions, when executed,
cause the at least one computing device to at least access data
representing an imaging device positioned at a distal end of the
instrument; identify image capture parameters of the imaging
device; and set virtual image capture parameters of the virtual
imaging device to correspond to the image capture parameters of the
imaging device.
[0024] In some embodiments the instructions, when executed, cause
the at least one computing device to at least generate the virtual
depth maps based on the virtual image capture parameters. In some
embodiments the image capture parameters comprise one or more of
field of view, lens distortion, focal length, and brightness
shading.
[0025] In some embodiments the instructions, when executed, cause
the at least one computing device to at least for each virtual
location of the plurality of virtual locations identify first and
second depth criteria in the virtual depth map, and calculate a
value representing a distance between the first and second depth
criteria; and create the database by associating the plurality of
virtual locations with the corresponding value.
[0026] In some embodiments the instructions, when executed, cause
the at least one computing device to at least for each virtual
location of the plurality of virtual locations identify three or
more depth criteria in the virtual depth map, and determine a shape
and location of a polygon connecting the three or more depth
criteria; and create the database by associating the plurality of
virtual locations with the shape and location of the corresponding
polygon. In some embodiments the instructions, when executed, cause
the at least one computing device to at least generate a
three-dimensional volume of data from a series of two-dimensional
images representing the anatomical luminal network of the patient;
and form the virtual three-dimensional model of the internal
surfaces of the anatomical luminal network from the
three-dimensional volume of data. In some embodiments the
instructions, when executed, cause the at least one computing
device to at least control a computed tomography imaging system to
capture the series of two-dimensional images. In some embodiments
the instructions, when executed, cause the at least one computing
device to at least form the virtual three-dimensional model by
applying volume segmentation to the three-dimensional volume of
data.
[0027] Another aspect relates to a method of facilitating
navigation of an anatomical luminal network of a patient, the
method, executed by a set of one or more computing devices,
comprising receiving a stereoscopic image set representing an
interior of the anatomical luminal network; generating a depth map
based on the stereoscopic image set; accessing a virtual feature
derived from a virtual image simulated from a viewpoint of a
virtual imaging device positioned at a location within a virtual
luminal network; calculating a correspondence between a feature
derived from the depth map and the virtual feature derived from the
virtual image; and determining a pose of the distal end of the
instrument within the anatomical luminal network based on the
virtual location of associated with the virtual feature.
[0028] In some embodiments, generating the stereoscopic image set
comprises positioning an imaging device at a distal end of an
instrument at a first location within the anatomical luminal
network; capturing a first image of an interior of the anatomical
luminal network with the imaging device positioned at the first
location; robotically controlling the imaging device to move a
known distance to a second location within the anatomical luminal
network; and capturing a second image of the interior of the
anatomical luminal network with the imaging device positioned at
the second location. In some embodiments, robotically controlling
the imaging device to move the known distance comprises one or both
of retracting the imaging device and angularly rolling the imaging
device.
BRIEF DESCRIPTION OF THE DRAWINGS
[0029] The disclosed aspects will hereinafter be described in
conjunction with the appended drawings and appendices, provided to
illustrate and not to limit the disclosed aspects, wherein like
designations denote like elements.
[0030] FIG. 1 illustrates an embodiment of a cart-based robotic
system arranged for diagnostic and/or therapeutic bronchoscopy
procedure(s).
[0031] FIG. 2 depicts further aspects of the robotic system of FIG.
1.
[0032] FIG. 3 illustrates an embodiment of the robotic system of
FIG. 1 arranged for ureteroscopy.
[0033] FIG. 4 illustrates an embodiment of the robotic system of
FIG. 1 arranged for a vascular procedure.
[0034] FIG. 5 illustrates an embodiment of a table-based robotic
system arranged for a bronchoscopy procedure.
[0035] FIG. 6 provides an alternative view of the robotic system of
FIG. 5.
[0036] FIG. 7 illustrates an example system configured to stow
robotic arm(s).
[0037] FIG. 8 illustrates an embodiment of a table-based robotic
system configured for a ureteroscopy procedure.
[0038] FIG. 9 illustrates an embodiment of a table-based robotic
system configured for a laparoscopic procedure.
[0039] FIG. 10 illustrates an embodiment of the table-based robotic
system of FIGS. 5-9 with pitch or tilt adjustment.
[0040] FIG. 11 provides a detailed illustration of the interface
between the table and the column of the table-based robotic system
of FIGS. 5-10.
[0041] FIG. 12 illustrates an exemplary instrument driver.
[0042] FIG. 13 illustrates an exemplary medical instrument with a
paired instrument driver.
[0043] FIG. 14 illustrates an alternative design for an instrument
driver and instrument where the axes of the drive units are
parallel to the axis of the elongated shaft of the instrument.
[0044] FIG. 15 depicts a block diagram illustrating a localization
system that estimates a location of one or more elements of the
robotic systems of FIGS. 1-10, such as the location of the
instrument of FIGS. 13-14, in accordance to an example
embodiment.
[0045] FIG. 16A illustrates an example operating environment
implementing the disclosed navigation systems and techniques.
[0046] FIG. 16B illustrates an example luminal network navigated in
the environment of FIG. 16A.
[0047] FIG. 16C illustrates an example robotic arm for guiding
instrument movement in through the luminal network of FIG. 16B.
[0048] FIG. 17 illustrates an example command console for the
example medical robotic system, according to one embodiment.
[0049] FIG. 18 illustrates an example endoscope having imaging and
EM sensing capabilities as described herein.
[0050] FIG. 19 depicts a schematic block diagram of a navigation
system as described herein.
[0051] FIG. 20 depicts a flowchart of an example process for
generating an extracted virtual feature data set.
[0052] FIG. 21 depicts a flowchart of an example intra-operative
process for generating depth information based on captured
endoscopic images and calculated correspondence between features of
the depth information with the extracted virtual feature data set
of FIG. 20.
DETAILED DESCRIPTION
1. Overview.
[0053] Aspects of the present disclosure may be integrated into a
robotically-enabled medical system capable of performing a variety
of medical procedures, including both minimally invasive, such as
laparoscopy, and non-invasive, such as endoscopy, procedures. Among
endoscopy procedures, the system may be capable of performing
bronchoscopy, ureteroscopy, gastroenterology, etc.
[0054] In addition to performing the breadth of procedures, the
system may provide additional benefits, such as enhanced imaging
and guidance to assist the physician. Additionally, the system may
provide the physician with the ability to perform the procedure
from an ergonomic position without the need for awkward arm motions
and positions. Still further, the system may provide the physician
with the ability to perform the procedure with improved ease of use
such that one or more of the instruments of the system can be
controlled by a single user.
[0055] Various embodiments will be described below in conjunction
with the drawings for purposes of illustration. It should be
appreciated that many other implementations of the disclosed
concepts are possible, and various advantages can be achieved with
the disclosed implementations. Headings are included herein for
reference and to aid in locating various sections. These headings
are not intended to limit the scope of the concepts described with
respect thereto. Such concepts may have applicability throughout
the entire specification.
A. Robotic System--Cart.
[0056] The robotically-enabled medical system may be configured in
a variety of ways depending on the particular procedure. FIG. 1
illustrates an embodiment of a cart-based robotically-enabled
system 10 arranged for a diagnostic and/or therapeutic bronchoscopy
procedure. During a bronchoscopy, the system 10 may comprise a cart
11 having one or more robotic arms 12 to deliver a medical
instrument, such as a steerable endoscope 13, which may be a
procedure-specific bronchoscope for bronchoscopy, to a natural
orifice access point (i.e., the mouth of the patient positioned on
a table in the present example) to deliver diagnostic and/or
therapeutic tools. As shown, the cart 11 may be positioned
proximate to the patient's upper torso in order to provide access
to the access point. Similarly, the robotic arms 12 may be actuated
to position the bronchoscope relative to the access point. The
arrangement in FIG. 1 may also be utilized when performing a
gastro-intestinal (GI) procedure with a gastroscope, a specialized
endoscope for GI procedures. FIG. 2 depicts an example embodiment
of the cart in greater detail.
[0057] With continued reference to FIG. 1, once the cart 11 is
properly positioned, the robotic arms 12 may insert the steerable
endoscope 13 into the patient robotically, manually, or a
combination thereof. As shown, the steerable endoscope 13 may
comprise at least two telescoping parts, such as an inner leader
portion and an outer sheath portion, each portion coupled to a
separate instrument driver from the set of instrument drivers 28,
each instrument driver coupled to the distal end of an individual
robotic arm. This linear arrangement of the instrument drivers 28,
which facilitates coaxially aligning the leader portion with the
sheath portion, creates a "virtual rail" 29 that may be
repositioned in space by manipulating the one or more robotic arms
12 into different angles and/or positions. The virtual rails
described herein are depicted in the Figures using dashed lines,
and accordingly the dashed lines do not depict any physical
structure of the system. Translation of the instrument drivers 28
along the virtual rail 29 telescopes the inner leader portion
relative to the outer sheath portion or advances or retracts the
endoscope 13 from the patient. The angle of the virtual rail 29 may
be adjusted, translated, and pivoted based on clinical application
or physician preference. For example, in bronchoscopy, the angle
and position of the virtual rail 29 as shown represents a
compromise between providing physician access to the endoscope 13
while minimizing friction that results from bending the endoscope
13 into the patient's mouth.
[0058] The endoscope 13 may be directed down the patient's trachea
and lungs after insertion using precise commands from the robotic
system until reaching the target destination or operative site. In
order to enhance navigation through the patient's lung network
and/or reach the desired target, the endoscope 13 may be
manipulated to telescopically extend the inner leader portion from
the outer sheath portion to obtain enhanced articulation and
greater bend radius. The use of separate instrument drivers 28 also
allows the leader portion and sheath portion to be driven
independent of each other.
[0059] For example, the endoscope 13 may be directed to deliver a
biopsy needle to a target, such as, for example, a lesion or nodule
within the lungs of a patient. The needle may be deployed down a
working channel that runs the length of the endoscope to obtain a
tissue sample to be analyzed by a pathologist. Depending on the
pathology results, additional tools may be deployed down the
working channel of the endoscope for additional biopsies. After
identifying a nodule to be malignant, the endoscope 13 may
endoscopically deliver tools to resect the potentially cancerous
tissue. In some instances, diagnostic and therapeutic treatments
may need to be delivered in separate procedures. In those
circumstances, the endoscope 13 may also be used to deliver a
fiducial to "mark" the location of the target nodule as well. In
other instances, diagnostic and therapeutic treatments may be
delivered during the same procedure.
[0060] The system 10 may also include a movable tower 30, which may
be connected via support cables to the cart 11 to provide support
for controls, electronics, fluidics, optics, sensors, and/or power
to the cart 11. Placing such functionality in the tower 30 allows
for a smaller form factor cart 11 that may be more easily adjusted
and/or re-positioned by an operating physician and his/her staff.
Additionally, the division of functionality between the cart/table
and the support tower 30 reduces operating room clutter and
facilitates improving clinical workflow. While the cart 11 may be
positioned close to the patient, the tower 30 may be stowed in a
remote location to stay out of the way during a procedure.
[0061] In support of the robotic systems described above, the tower
30 may include component(s) of a computer-based control system that
stores computer program instructions, for example, within a
non-transitory computer-readable storage medium such as a
persistent magnetic storage drive, solid state drive, etc. The
execution of those instructions, whether the execution occurs in
the tower 30 or the cart 11, may control the entire system or
sub-system(s) thereof. For example, when executed by a processor of
the computer system, the instructions may cause the components of
the robotics system to actuate the relevant carriages and arm
mounts, actuate the robotics arms, and control the medical
instruments. For example, in response to receiving the control
signal, the motors in the joints of the robotics arms may position
the arms into a certain posture.
[0062] The tower 30 may also include a pump, flow meter, valve
control, and/or fluid access in order to provide controlled
irrigation and aspiration capabilities to system that may be
deployed through the endoscope 13. These components may also be
controlled using the computer system of tower 30. In some
embodiments, irrigation and aspiration capabilities may be
delivered directly to the endoscope 13 through separate
cable(s).
[0063] The tower 30 may include a voltage and surge protector
designed to provide filtered and protected electrical power to the
cart 11, thereby avoiding placement of a power transformer and
other auxiliary power components in the cart 11, resulting in a
smaller, more moveable cart 11.
[0064] The tower 30 may also include support equipment for the
sensors deployed throughout the robotic system 10. For example, the
tower 30 may include opto-electronics equipment for detecting,
receiving, and processing data received from the optical sensors or
cameras throughout the robotic system 10. In combination with the
control system, such opto-electronics equipment may be used to
generate real-time images for display in any number of consoles
deployed throughout the system, including in the tower 30.
Similarly, the tower 30 may also include an electronic subsystem
for receiving and processing signals received from deployed
electromagnetic (EM) sensors. The tower 30 may also be used to
house and position an EM field generator for detection by EM
sensors in or on the medical instrument.
[0065] The tower 30 may also include a console 31 in addition to
other consoles available in the rest of the system, e.g., console
mounted on top of the cart. The console 31 may include a user
interface and a display screen, such as a touchscreen, for the
physician operator. Consoles in system 10 are generally designed to
provide both robotic controls as well as pre-operative and
real-time information of the procedure, such as navigational and
localization information of the endoscope 13. When the console 31
is not the only console available to the physician, it may be used
by a second operator, such as a nurse, to monitor the health or
vitals of the patient and the operation of system, as well as
provide procedure-specific data, such as navigational and
localization information.
[0066] The tower 30 may be coupled to the cart 11 and endoscope 13
through one or more cables or connections (not shown). In some
embodiments, the support functionality from the tower 30 may be
provided through a single cable to the cart 11, simplifying and
de-cluttering the operating room. In other embodiments, specific
functionality may be coupled in separate cabling and connections.
For example, while power may be provided through a single power
cable to the cart, the support for controls, optics, fluidics,
and/or navigation may be provided through a separate cable.
[0067] FIG. 2 provides a detailed illustration of an embodiment of
the cart from the cart-based robotically-enabled system shown in
FIG. 1. The cart 11 generally includes an elongated support
structure 14 (often referred to as a "column"), a cart base 15, and
a console 16 at the top of the column 14. The column 14 may include
one or more carriages, such as a carriage 17 (alternatively "arm
support") for supporting the deployment of one or more robotic arms
12 (three shown in FIG. 2). The carriage 17 may include
individually configurable arm mounts that rotate along a
perpendicular axis to adjust the base of the robotic arms 12 for
better positioning relative to the patient. The carriage 17 also
includes a carriage interface 19 that allows the carriage 17 to
vertically translate along the column 14.
[0068] The carriage interface 19 is connected to the column 14
through slots, such as slot 20, that are positioned on opposite
sides of the column 14 to guide the vertical translation of the
carriage 17. The slot 20 contains a vertical translation interface
to position and hold the carriage at various vertical heights
relative to the cart base 15. Vertical translation of the carriage
17 allows the cart 11 to adjust the reach of the robotic arms 12 to
meet a variety of table heights, patient sizes, and physician
preferences. Similarly, the individually configurable arm mounts on
the carriage 17 allow the robotic arm base 21 of robotic arms 12 to
be angled in a variety of configurations.
[0069] In some embodiments, the slot 20 may be supplemented with
slot covers that are flush and parallel to the slot surface to
prevent dirt and fluid ingress into the internal chambers of the
column 14 and the vertical translation interface as the carriage 17
vertically translates. The slot covers may be deployed through
pairs of spring spools positioned near the vertical top and bottom
of the slot 20. The covers are coiled within the spools until
deployed to extend and retract from their coiled state as the
carriage 17 vertically translates up and down. The spring-loading
of the spools provides force to retract the cover into a spool when
carriage 17 translates towards the spool, while also maintaining a
tight seal when the carriage 17 translates away from the spool. The
covers may be connected to the carriage 17 using, for example,
brackets in the carriage interface 19 to ensure proper extension
and retraction of the cover as the carriage 17 translates.
[0070] The column 14 may internally comprise mechanisms, such as
gears and motors, that are designed to use a vertically aligned
lead screw to translate the carriage 17 in a mechanized fashion in
response to control signals generated in response to user inputs,
e.g., inputs from the console 16.
[0071] The robotic arms 12 may generally comprise robotic arm bases
21 and end effectors 22, separated by a series of linkages 23 that
are connected by a series of joints 24, each joint comprising an
independent actuator, each actuator comprising an independently
controllable motor. Each independently controllable joint
represents an independent degree of freedom available to the
robotic arm. Each of the arms 12 have seven joints, and thus
provide seven degrees of freedom. A multitude of joints result in a
multitude of degrees of freedom, allowing for "redundant" degrees
of freedom. Redundant degrees of freedom allow the robotic arms 12
to position their respective end effectors 22 at a specific
position, orientation, and trajectory in space using different
linkage positions and joint angles. This allows for the system to
position and direct a medical instrument from a desired point in
space while allowing the physician to move the arm joints into a
clinically advantageous position away from the patient to create
greater access, while avoiding arm collisions.
[0072] The cart base 15 balances the weight of the column 14,
carriage 17, and arms 12 over the floor. Accordingly, the cart base
15 houses heavier components, such as electronics, motors, power
supply, as well as components that either enable movement and/or
immobilize the cart. For example, the cart base 15 includes
rollable wheel-shaped casters 25 that allow for the cart to easily
move around the room prior to a procedure. After reaching the
appropriate position, the casters 25 may be immobilized using wheel
locks to hold the cart 11 in place during the procedure.
[0073] Positioned at the vertical end of column 14, the console 16
allows for both a user interface for receiving user input and a
display screen (or a dual-purpose device such as, for example, a
touchscreen 26) to provide the physician user with both
pre-operative and intra-operative data. Potential pre-operative
data on the touchscreen 26 may include pre-operative plans,
navigation and mapping data derived from pre-operative computerized
tomography (CT) scans, and/or notes from pre-operative patient
interviews. Intra-operative data on display may include optical
information provided from the tool, sensor and coordinate
information from sensors, as well as vital patient statistics, such
as respiration, heart rate, and/or pulse. The console 16 may be
positioned and tilted to allow a physician to access the console
from the side of the column 14 opposite carriage 17. From this
position the physician may view the console 16, robotic arms 12,
and patient while operating the console 16 from behind the cart 11.
As shown, the console 16 also includes a handle 27 to assist with
maneuvering and stabilizing cart 11.
[0074] FIG. 3 illustrates an embodiment of a robotically-enabled
system 10 arranged for ureteroscopy. In a ureteroscopic procedure,
the cart 11 may be positioned to deliver a ureteroscope 32, a
procedure-specific endoscope designed to traverse a patient's
urethra and ureter, to the lower abdominal area of the patient. In
a ureteroscopy, it may be desirable for the ureteroscope 32 to be
directly aligned with the patient's urethra to reduce friction and
forces on the sensitive anatomy in the area. As shown, the cart 11
may be aligned at the foot of the table to allow the robotic arms
12 to position the ureteroscope 32 for direct linear access to the
patient's urethra. From the foot of the table, the robotic arms 12
may insert ureteroscope 32 along the virtual rail 33 directly into
the patient's lower abdomen through the urethra.
[0075] After insertion into the urethra, using similar control
techniques as in bronchoscopy, the ureteroscope 32 may be navigated
into the bladder, ureters, and/or kidneys for diagnostic and/or
therapeutic applications. For example, the ureteroscope 32 may be
directed into the ureter and kidneys to break up kidney stone build
up using laser or ultrasonic lithotripsy device deployed down the
working channel of the ureteroscope 32. After lithotripsy is
complete, the resulting stone fragments may be removed using
baskets deployed down the ureteroscope 32.
[0076] FIG. 4 illustrates an embodiment of a robotically-enabled
system similarly arranged for a vascular procedure. In a vascular
procedure, the system 10 may be configured such the cart 11 may
deliver a medical instrument 34, such as a steerable catheter, to
an access point in the femoral artery in the patient's leg. The
femoral artery presents both a larger diameter for navigation as
well as relatively less circuitous and tortuous path to the
patient's heart, which simplifies navigation. As in a ureteroscopic
procedure, the cart 11 may be positioned towards the patient's legs
and lower abdomen to allow the robotic arms 12 to provide a virtual
rail 35 with direct linear access to the femoral artery access
point in the patient's thigh/hip region. After insertion into the
artery, the medical instrument 34 may be directed and inserted by
translating the instrument drivers 28. Alternatively, the cart may
be positioned around the patient's upper abdomen in order to reach
alternative vascular access points, such as, for example, the
carotid and brachial arteries near the shoulder and wrist.
B. Robotic System--Table.
[0077] Embodiments of the robotically-enabled medical system may
also incorporate the patient's table. Incorporation of the table
reduces the amount of capital equipment within the operating room
by removing the cart, which allows greater access to the patient.
FIG. 5 illustrates an embodiment of such a robotically-enabled
system arranged for a bronchoscopy procedure. System 36 includes a
support structure or column 37 for supporting platform 38 (shown as
a "table" or "bed") over the floor. Much like in the cart-based
systems, the end effectors of the robotic arms 39 of the system 36
comprise instrument drivers 42 that are designed to manipulate an
elongated medical instrument, such as a bronchoscope 40 in FIG. 5,
through or along a virtual rail 41 formed from the linear alignment
of the instrument drivers 42. In practice, a C-arm for providing
fluoroscopic imaging may be positioned over the patient's upper
abdominal area by placing the emitter and detector around table
38.
[0078] FIG. 6 provides an alternative view of the system 36 without
the patient and medical instrument for discussion purposes. As
shown, the column 37 may include one or more carriages 43 shown as
ring-shaped in the system 36, from which the one or more robotic
arms 39 may be based. The carriages 43 may translate along a
vertical column interface 44 that runs the length of the column 37
to provide different vantage points from which the robotic arms 39
may be positioned to reach the patient. The carriage(s) 43 may
rotate around the column 37 using a mechanical motor positioned
within the column 37 to allow the robotic arms 39 to have access to
multiples sides of the table 38, such as, for example, both sides
of the patient. In embodiments with multiple carriages, the
carriages may be individually positioned on the column and may
translate and/or rotate independent of the other carriages. While
carriages 43 need not surround the column 37 or even be circular,
the ring-shape as shown facilitates rotation of the carriages 43
around the column 37 while maintaining structural balance. Rotation
and translation of the carriages 43 allows the system to align the
medical instruments, such as endoscopes and laparoscopes, into
different access points on the patient.
[0079] The arms 39 may be mounted on the carriages through a set of
arm mounts 45 comprising a series of joints that may individually
rotate and/or telescopically extend to provide additional
configurability to the robotic arms 39. Additionally, the arm
mounts 45 may be positioned on the carriages 43 such that, when the
carriages 43 are appropriately rotated, the arm mounts 45 may be
positioned on either the same side of table 38 (as shown in FIG.
6), on opposite sides of table 38 (as shown in FIG. 9), or on
adjacent sides of the table 38 (not shown).
[0080] The column 37 structurally provides support for the table
38, and a path for vertical translation of the carriages.
Internally, the column 37 may be equipped with lead screws for
guiding vertical translation of the carriages, and motors to
mechanize the translation of said carriages based the lead screws.
The column 37 may also convey power and control signals to the
carriage 43 and robotic arms 39 mounted thereon.
[0081] The table base 46 serves a similar function as the cart base
15 in cart 11 shown in FIG. 2, housing heavier components to
balance the table/bed 38, the column 37, the carriages 43, and the
robotic arms 39. The table base 46 may also incorporate rigid
casters to provide stability during procedures. Deployed from the
bottom of the table base 46, the casters may extend in opposite
directions on both sides of the base 46 and retract when the system
36 needs to be moved.
[0082] Continuing with FIG. 6, the system 36 may also include a
tower (not shown) that divides the functionality of system 36
between table and tower to reduce the form factor and bulk of the
table. As in earlier disclosed embodiments, the tower may be
provide a variety of support functionalities to table, such as
processing, computing, and control capabilities, power, fluidics,
and/or optical and sensor processing. The tower may also be movable
to be positioned away from the patient to improve physician access
and de-clutter the operating room. Additionally, placing components
in the tower allows for more storage space in the table base for
potential stowage of the robotic arms. The tower may also include a
console that provides both a user interface for user input, such as
keyboard and/or pendant, as well as a display screen (or
touchscreen) for pre-operative and intra-operative information,
such as real-time imaging, navigation, and tracking
information.
[0083] In some embodiments, a table base may stow and store the
robotic arms when not in use. FIG. 7 illustrates a system 47 that
stows robotic arms in an embodiment of the table-based system. In
system 47, carriages 48 may be vertically translated into base 49
to stow robotic arms 50, arm mounts 51, and the carriages 48 within
the base 49. Base covers 52 may be translated and retracted open to
deploy the carriages 48, arm mounts 51, and arms 50 around column
53, and closed to stow to protect them when not in use. The base
covers 52 may be sealed with a membrane 54 along the edges of its
opening to prevent dirt and fluid ingress when closed.
[0084] FIG. 8 illustrates an embodiment of a robotically-enabled
table-based system configured for a ureteroscopy procedure. In a
ureteroscopy, the table 38 may include a swivel portion 55 for
positioning a patient off-angle from the column 37 and table base
46. The swivel portion 55 may rotate or pivot around a pivot point
(e.g., located below the patient's head) in order to position the
bottom portion of the swivel portion 55 away from the column 37.
For example, the pivoting of the swivel portion 55 allows a C-arm
(not shown) to be positioned over the patient's lower abdomen
without competing for space with the column (not shown) below table
38. By rotating the carriage 35 (not shown) around the column 37,
the robotic arms 39 may directly insert a ureteroscope 56 along a
virtual rail 57 into the patient's groin area to reach the urethra.
In a ureteroscopy, stirrups 58 may also be fixed to the swivel
portion 55 of the table 38 to support the position of the patient's
legs during the procedure and allow clear access to the patient's
groin area.
[0085] In a laparoscopic procedure, through small incision(s) in
the patient's abdominal wall, minimally invasive instruments
(elongated in shape to accommodate the size of the one or more
incisions) may be inserted into the patient's anatomy. After
inflation of the patient's abdominal cavity, the instruments, often
referred to as laparoscopes, may be directed to perform surgical
tasks, such as grasping, cutting, ablating, suturing, etc. FIG. 9
illustrates an embodiment of a robotically-enabled table-based
system configured for a laparoscopic procedure. As shown in FIG. 9,
the carriages 43 of the system 36 may be rotated and vertically
adjusted to position pairs of the robotic arms 39 on opposite sides
of the table 38, such that laparoscopes 59 may be positioned using
the arm mounts 45 to be passed through minimal incisions on both
sides of the patient to reach his/her abdominal cavity.
[0086] To accommodate laparoscopic procedures, the
robotically-enabled table system may also tilt the platform to a
desired angle. FIG. 10 illustrates an embodiment of the
robotically-enabled medical system with pitch or tilt adjustment.
As shown in FIG. 10, the system 36 may accommodate tilt of the
table 38 to position one portion of the table at a greater distance
from the floor than the other. Additionally, the arm mounts 45 may
rotate to match the tilt such that the arms 39 maintain the same
planar relationship with table 38. To accommodate steeper angles,
the column 37 may also include telescoping portions 60 that allow
vertical extension of column 37 to keep the table 38 from touching
the floor or colliding with base 46.
[0087] FIG. 11 provides a detailed illustration of the interface
between the table 38 and the column 37. Pitch rotation mechanism 61
may be configured to alter the pitch angle of the table 38 relative
to the column 37 in multiple degrees of freedom. The pitch rotation
mechanism 61 may be enabled by the positioning of orthogonal axes
1, 2 at the column-table interface, each axis actuated by a
separate motor 2, 4 responsive to an electrical pitch angle
command. Rotation along one screw 5 would enable tilt adjustments
in one axis 1, while rotation along the other screw 6 would enable
tilt adjustments along the other axis 2.
[0088] For example, pitch adjustments are particularly useful when
trying to position the table in a Trendelenburg position, i.e.,
position the patient's lower abdomen at a higher position from the
floor than the patient's lower abdomen, for lower abdominal
surgery. The Trendelenburg position causes the patient's internal
organs to slide towards his/her upper abdomen through the force of
gravity, clearing out the abdominal cavity for minimally invasive
tools to enter and perform lower abdominal surgical procedures,
such as laparoscopic prostatectomy.
C. Instrument Driver & Interface.
[0089] The end effectors of the system's robotic arms comprise (i)
an instrument driver (alternatively referred to as "instrument
drive mechanism" or "instrument device manipulator") that
incorporate electro-mechanical means for actuating the medical
instrument and (ii) a removable or detachable medical instrument
which may be devoid of any electro-mechanical components, such as
motors. This dichotomy may be driven by the need to sterilize
medical instruments used in medical procedures, and the inability
to adequately sterilize expensive capital equipment due to their
intricate mechanical assemblies and sensitive electronics.
Accordingly, the medical instruments may be designed to be
detached, removed, and interchanged from the instrument driver (and
thus the system) for individual sterilization or disposal by the
physician or the physician's staff. In contrast, the instrument
drivers need not be changed or sterilized, and may be draped for
protection.
[0090] FIG. 12 illustrates an example instrument driver. Positioned
at the distal end of a robotic arm, instrument driver 62 comprises
of one or more drive units 63 arranged with parallel axes to
provide controlled torque to a medical instrument via drive shafts
64. Each drive unit 63 comprises an individual drive shaft 64 for
interacting with the instrument, a gear head 65 for converting the
motor shaft rotation to a desired torque, a motor 66 for generating
the drive torque, an encoder 67 to measure the speed of the motor
shaft and provide feedback to the control circuitry, and control
circuitry 68 for receiving control signals and actuating the drive
unit. Each drive unit 63 being independent controlled and
motorized, the instrument driver 62 may provide multiple (four as
shown in FIG. 12) independent drive outputs to the medical
instrument. In operation, the control circuitry 68 would receive a
control signal, transmit a motor signal to the motor 66, compare
the resulting motor speed as measured by the encoder 67 with the
desired speed, and modulate the motor signal to generate the
desired torque.
[0091] For procedures that require a sterile environment, the
robotic system may incorporate a drive interface, such as a sterile
adapter connected to a sterile drape, that sits between the
instrument driver and the medical instrument. The chief purpose of
the sterile adapter is to transfer angular motion from the drive
shafts of the instrument driver to the drive inputs of the
instrument while maintaining physical separation, and thus
sterility, between the drive shafts and drive inputs. Accordingly,
an example sterile adapter may comprise of a series of rotational
inputs and outputs intended to be mated with the drive shafts of
the instrument driver and drive inputs on the instrument. Connected
to the sterile adapter, the sterile drape, comprised of a thin,
flexible material such as transparent or translucent plastic, is
designed to cover the capital equipment, such as the instrument
driver, robotic arm, and cart (in a cart-based system) or table (in
a table-based system). Use of the drape would allow the capital
equipment to be positioned proximate to the patient while still
being located in an area not requiring sterilization (i.e.,
non-sterile field). On the other side of the sterile drape, the
medical instrument may interface with the patient in an area
requiring sterilization (i.e., sterile field).
D. Medical Instrument.
[0092] FIG. 13 illustrates an example medical instrument with a
paired instrument driver. Like other instruments designed for use
with a robotic system, medical instrument 70 comprises an elongated
shaft 71 (or elongate body) and an instrument base 72. The
instrument base 72, also referred to as an "instrument handle" due
to its intended design for manual interaction by the physician, may
generally comprise rotatable drive inputs 73, e.g., receptacles,
pulleys or spools, that are designed to be mated with drive outputs
74 that extend through a drive interface on instrument driver 75 at
the distal end of robotic arm 76. When physically connected,
latched, and/or coupled, the mated drive inputs 73 of instrument
base 72 may share axes of rotation with the drive outputs 74 in the
instrument driver 75 to allow the transfer of torque from drive
outputs 74 to drive inputs 73. In some embodiments, the drive
outputs 74 may comprise splines that are designed to mate with
receptacles on the drive inputs 73.
[0093] The elongated shaft 71 is designed to be delivered through
either an anatomical opening or lumen, e.g., as in endoscopy, or a
minimally invasive incision, e.g., as in laparoscopy. The elongated
shaft 66 may be either flexible (e.g., having properties similar to
an endoscope) or rigid (e.g., having properties similar to a
laparoscope) or contain a customized combination of both flexible
and rigid portions. When designed for laparoscopy, the distal end
of a rigid elongated shaft may be connected to an end effector
comprising a jointed wrist formed from a clevis with an axis of
rotation and a surgical tool, such as, for example, a grasper or
scissors, that may be actuated based on force from the tendons as
the drive inputs rotate in response to torque received from the
drive outputs 74 of the instrument driver 75. When designed for
endoscopy, the distal end of a flexible elongated shaft may include
a steerable or controllable bending section that may be articulated
and bent based on torque received from the drive outputs 74 of the
instrument driver 75.
[0094] Torque from the instrument driver 75 is transmitted down the
elongated shaft 71 using tendons within the shaft 71. These
individual tendons, such as pull wires, may be individually
anchored to individual drive inputs 73 within the instrument handle
72. From the handle 72, the tendons are directed down one or more
pull lumens within the elongated shaft 71 and anchored at the
distal portion of the elongated shaft 71. In laparoscopy, these
tendons may be coupled to a distally mounted end effector, such as
a wrist, grasper, or scissor. Under such an arrangement, torque
exerted on drive inputs 73 would transfer tension to the tendon,
thereby causing the end effector to actuate in some way. In
laparoscopy, the tendon may cause a joint to rotate about an axis,
thereby causing the end effector to move in one direction or
another. Alternatively, the tendon may be connected to one or more
jaws of a grasper at distal end of the elongated shaft 71, where
tension from the tendon cause the grasper to close.
[0095] In endoscopy, the tendons may be coupled to a bending or
articulating section positioned along the elongated shaft 71 (e.g.,
at the distal end) via adhesive, control ring, or other mechanical
fixation. When fixedly attached to the distal end of a bending
section, torque exerted on drive inputs 73 would be transmitted
down the tendons, causing the softer, bending section (sometimes
referred to as the articulable section or region) to bend or
articulate. Along the non-bending sections, it may be advantageous
to spiral or helix the individual pull lumens that direct the
individual tendons along (or inside) the walls of the endoscope
shaft to balance the radial forces that result from tension in the
pull wires. The angle of the spiraling and/or spacing there between
may be altered or engineered for specific purposes, wherein tighter
spiraling exhibits lesser shaft compression under load forces,
while lower amounts of spiraling results in greater shaft
compression under load forces, but also exhibits limits bending. On
the other end of the spectrum, the pull lumens may be directed
parallel to the longitudinal axis of the elongated shaft 71 to
allow for controlled articulation in the desired bending or
articulable sections.
[0096] In endoscopy, the elongated shaft 71 houses a number of
components to assist with the robotic procedure. The shaft may
comprise of a working channel for deploying surgical tools,
irrigation, and/or aspiration to the operative region at the distal
end of the shaft 71. The shaft 71 may also accommodate wires and/or
optical fibers to transfer signals to/from an optical assembly at
the distal tip, which may include of an optical camera. The shaft
71 may also accommodate optical fibers to carry light from
proximally-located light sources, such as light emitting diodes, to
the distal end of the shaft.
[0097] At the distal end of the instrument 70, the distal tip may
also comprise the opening of a working channel for delivering tools
for diagnostic and/or therapy, irrigation, and aspiration to an
operative site. The distal tip may also include a port for a
camera, such as a fiberscope or a digital camera, to capture images
of an internal anatomical space. Relatedly, the distal tip may also
include ports for light sources for illuminating the anatomical
space when using the camera.
[0098] In the example of FIG. 13, the drive shaft axes, and thus
the drive input axes, are orthogonal to the axis of the elongated
shaft. This arrangement, however, complicates roll capabilities for
the elongated shaft 71. Rolling the elongated shaft 71 along its
axis while keeping the drive inputs 73 static results in
undesirable tangling of the tendons as they extend off the drive
inputs 73 and enter pull lumens within the elongate shaft 71. The
resulting entanglement of such tendons may disrupt any control
algorithms intended to predict movement of the flexible elongate
shaft during an endoscopic procedure.
[0099] FIG. 14 illustrates an alternative design for an instrument
driver and instrument where the axes of the drive units are
parallel to the axis of the elongated shaft of the instrument. As
shown, a circular instrument driver 80 comprises four drive units
with their drive outputs 81 aligned in parallel at the end of a
robotic arm 82. The drive units, and their respective drive outputs
81, are housed in a rotational assembly 83 of the instrument driver
80 that is driven by one of the drive units within the assembly 83.
In response to torque provided by the rotational drive unit, the
rotational assembly 83 rotates along a circular bearing that
connects the rotational assembly 83 to the non-rotational portion
84 of the instrument driver. Power and controls signals may be
communicated from the non-rotational portion 84 of the instrument
driver 80 to the rotational assembly 83 through electrical contacts
may be maintained through rotation by a brushed slip ring
connection (not shown). In other embodiments, the rotational
assembly 83 may be responsive to a separate drive unit that is
integrated into the non-rotatable portion 84, and thus not in
parallel to the other drive units. The rotational mechanism 83
allows the instrument driver 80 to rotate the drive units, and
their respective drive outputs 81, as a single unit around an
instrument driver axis 85.
[0100] Like earlier disclosed embodiments, an instrument 86 may
comprise of an elongated shaft portion 88 and an instrument base 87
(shown with a transparent external skin for discussion purposes)
comprising a plurality of drive inputs 89 (such as receptacles,
pulleys, and spools) that are configured to receive the drive
outputs 81 in the instrument driver 80. Unlike prior disclosed
embodiments, instrument shaft 88 extends from the center of
instrument base 87 with an axis substantially parallel to the axes
of the drive inputs 89, rather than orthogonal as in the design of
FIG. 13.
[0101] When coupled to the rotational assembly 83 of the instrument
driver 80, the medical instrument 86, comprising instrument base 87
and instrument shaft 88, rotates in combination with the rotational
assembly 83 about the instrument driver axis 85. Since the
instrument shaft 88 is positioned at the center of instrument base
87, the instrument shaft 88 is coaxial with instrument driver axis
85 when attached. Thus, rotation of the rotational assembly 83
causes the instrument shaft 88 to rotate about its own longitudinal
axis. Moreover, as the instrument base 87 rotates with the
instrument shaft 88, any tendons connected to the drive inputs 89
in the instrument base 87 are not tangled during rotation.
Accordingly, the parallelism of the axes of the drive outputs 81,
drive inputs 89, and instrument shaft 88 allows for the shaft
rotation without tangling any control tendons.
E. Navigation and Control.
[0102] Traditional endoscopy may involve the use of fluoroscopy
(e.g., as may be delivered through a C-arm) and other forms of
radiation-based imaging modalities to provide endoluminal guidance
to an operator physician. In contrast, the robotic systems
contemplated by this disclosure can provide for non-radiation-based
navigational and localization means to reduce physician exposure to
radiation and reduce the amount of equipment within the operating
room. As used herein, the term "localization" may refer to
determining and/or monitoring the position of objects in a
reference coordinate system. Technologies such as pre-operative
mapping, computer vision, real-time EM tracking, and robot command
data may be used individually or in combination to achieve a
radiation-free operating environment. In other cases, where
radiation-based imaging modalities are still used, the
pre-operative mapping, computer vision, real-time EM tracking, and
robot command data may be used individually or in combination to
improve upon the information obtained solely through
radiation-based imaging modalities.
[0103] FIG. 15 is a block diagram illustrating a localization
system 90 that estimates a location of one or more elements of the
robotic system, such as the location of the instrument, in
accordance to an example embodiment. The localization system 90 may
be a set of one or more computer devices configured to execute one
or more instructions. The computer devices may be embodied by a
processor (or processors) and computer-readable memory in one or
more components discussed above. By way of example and not
limitation, the computer devices may be in the tower 30 shown in
FIG. 1, the cart shown in FIGS. 1-4, the beds shown in FIGS. 5-10,
etc.
[0104] As shown in FIG. 15, the localization system 90 may include
a localization module 95 that processes input data 91-94 to
generate location data 96 for the distal tip of a medical
instrument. The location data 96 may be data or logic that
represents a location and/or orientation of the distal end of the
instrument relative to a frame of reference. The frame of reference
can be a frame of reference relative to the anatomy of the patient
or to a known object, such as an EM field generator (see discussion
below for the EM field generator).
[0105] The various input data 91-94 are now described in greater
detail. Pre-operative mapping may be accomplished through the use
of the collection of low dose CT scans. Pre-operative CT scans
generate two-dimensional images, each representing a "slice" of a
cutaway view of the patient's internal anatomy. When analyzed in
the aggregate, image-based models for anatomical cavities, spaces
and structures of the patient's anatomy, such as a patient lung
network, may be generated. Techniques such as center-line geometry
may be determined and approximated from the CT images to develop a
three-dimensional volume of the patient's anatomy, referred to as
preoperative model data 91. The use of center-line geometry is
discussed in U.S. patent application Ser. No. 14/523,760, the
contents of which are herein incorporated in its entirety. Network
topological models may also be derived from the CT-images, and are
particularly appropriate for bronchoscopy.
[0106] In some embodiments, the instrument may be equipped with a
camera to provide vision data 92. The localization module 95 may
process the vision data to enable one or more vision-based location
tracking. For example, the preoperative model data may be used in
conjunction with the vision data 92 to enable computer vision-based
tracking of the medical instrument (e.g., an endoscope or an
instrument advance through a working channel of the endoscope). For
example, using the preoperative model data 91, the robotic system
may generate a library of expected endoscopic images from the model
based on the expected path of travel of the endoscope, each image
linked to a location within the model. Intra-operatively, this
library may be referenced by the robotic system in order to compare
real-time images captured at the camera (e.g., a camera at a distal
end of the endoscope) to those in the image library to assist
localization.
[0107] Other computer vision-based tracking techniques use feature
tracking to determine motion of the camera, and thus the endoscope.
Some feature of the localization module 95 may identify circular
geometries in the preoperative model data 91 that correspond to
anatomical lumens and track the change of those geometries to
determine which anatomical lumen was selected, as well as the
relative rotational and/or translational motion of the camera. Use
of a topological map may further enhance vision-based algorithms or
techniques.
[0108] Optical flow, another computer vision-based technique, may
analyze the displacement and translation of image pixels in a video
sequence in the vision data 92 to infer camera movement. Through
the comparison of multiple frames over multiple iterations,
movement and location of the camera (and thus the endoscope) may be
determined.
[0109] The localization module 95 may use real-time EM tracking to
generate a real-time location of the endoscope in a global
coordinate system that may be registered to the patient's anatomy,
represented by the preoperative model. In EM tracking, an EM sensor
(or tracker) comprising of one or more sensor coils embedded in one
or more locations and orientations in a medical instrument (e.g.,
an endoscopic tool) measures the variation in the EM field created
by one or more static EM field generators positioned at a known
location. The location information detected by the EM sensors is
stored as EM data 93. The EM field generator (or transmitter), may
be placed close to the patient to create a low intensity magnetic
field that the embedded sensor may detect. The magnetic field
induces small currents in the sensor coils of the EM sensor, which
may be analyzed to determine the distance and angle between the EM
sensor and the EM field generator. These distances and orientations
may be intra-operatively "registered" to the patient anatomy (e.g.,
the preoperative model) in order to determine the geometric
transformation that aligns a single location in the coordinate
system with a position in the pre-operative model of the patient's
anatomy. Once registered, an embedded EM tracker in one or more
positions of the medical instrument (e.g., the distal tip of an
endoscope) may provide real-time indications of the progression of
the medical instrument through the patient's anatomy.
[0110] Robotic command and kinematics data 94 may also be used by
the localization module 95 to provide localization data 96 for the
robotic system. Device pitch and yaw resulting from articulation
commands may be determined during pre-operative calibration.
Intra-operatively, these calibration measurements may be used in
combination with known insertion depth information to estimate the
position of the instrument. Alternatively, these calculations may
be analyzed in combination with EM, vision, and/or topological
modeling to estimate the position of the medical instrument within
the network.
[0111] As FIG. 15 shows, a number of other input data can be used
by the localization module 95. For example, although not shown in
FIG. 15, an instrument utilizing shape-sensing fiber can provide
shape data that the localization module 95 can use to determine the
location and shape of the instrument.
[0112] The localization module 95 may use the input data 91-94 in
combination(s). In some cases, such a combination may use a
probabilistic approach where the localization module 95 assigns a
confidence weight to the location determined from each of the input
data 91-94. Thus, where the EM data may not be reliable (as may be
the case where there is EM interference) the confidence of the
location determined by the EM data 93 can be decrease and the
localization module 95 may rely more heavily on the vision data 92
and/or the robotic command and kinematics data 94.
[0113] As discussed above, the robotic systems discussed herein may
be designed to incorporate a combination of one or more of the
technologies above. The robotic system's computer-based control
system, based in the tower, bed and/or cart, may store computer
program instructions, for example, within a non-transitory
computer-readable storage medium such as a persistent magnetic
storage drive, solid state drive, or the like, that, upon
execution, cause the system to receive and analyze sensor data and
user commands, generate control signals throughout the system, and
display the navigational and localization data, such as the
position of the instrument within the global coordinate system,
anatomical map, etc.
2. Introduction to Automatically-Initialized Navigation Systems
[0114] Embodiments of the disclosure relate to systems and
techniques that facilitate navigation of a medical instrument
through luminal networks, for example, lung airways or other
anatomical structures having interior open space, by generating and
using depth information from endoscope images to determine an
initial endoscope position, by analyzing multiple
navigation-related data sources to increase accuracy in estimation
of location and orientation of a medical instrument within the
luminal network, and by generating and using additional depth
information to re-initialize the navigation system after an adverse
event.
[0115] A bronchoscope can include a light source and a small camera
that allows a physician to inspect a patient's windpipe and
airways. Patient trauma can occur if the precise location of the
bronchoscope within the patient airways is not known. To ascertain
the location of the bronchoscope, image-based bronchoscopy guidance
systems can use data from the bronchoscope camera to perform local
registrations (e.g., registrations at a particular location within
a luminal network) at bifurcations of patient airways and so
beneficially can be less susceptible to position errors due to
patient breathing motion. However, as image-based guidance methods
rely on the bronchoscope video, they can be affected by artifacts
in bronchoscope video caused by patient coughing or mucous
obstruction, etc.
[0116] Electromagnetic navigation-guided bronchoscopy (EMN
bronchoscopy) is a type of bronchoscopic procedure that implements
EM technology to localize and guide endoscopic tools or catheters
through the bronchial pathways of the lung. EMN bronchoscopy
systems can use an EM field generator that emits a low-intensity,
varying EM field and establishes the position of the tracking
volume around the luminal network of the patient. The EM field is a
physical field produced by electrically charged objects that
affects the behavior of charged objects in the vicinity of the
field. EM sensors attached to objects positioned within the
generated field can be used to track locations and orientations of
these objects within the EM field. Small currents are induced in
the EM sensors by the varying electromagnetic field. The
characteristics of these electrical signals are dependent on the
distance and angle between a sensor and the EM field generator.
Accordingly, an EMN bronchoscopy system can include an EM field
generator, a steerable medical instrument having an EM sensor at or
near its distal tip, and a guidance computing system. The EM field
generator generates an EM field around the luminal network of the
patient to be navigated, for example, airways, gastrointestinal
tract, or a circulatory pathway. The steerable channel is inserted
through the working channel of the bronchoscope and tracked in the
EM field via the EM sensor.
[0117] Prior to the start of an EMN bronchoscopy procedure, a
virtual, three-dimensional (3D) bronchial map can be obtained for
the patient's specific airway structure, for example, from a
preoperative CT chest scan. Using the map and an EMN bronchoscopy
system, physicians can navigate to a desired location within the
lung to biopsy lesions, stage lymph nodes, insert markers to guide
radiotherapy or guide brachytherapy catheters. For example, a
registration can be performed at the beginning of a procedure to
generate a mapping between the coordinate system of the EM field
and the model coordinate system. Thus, as the steerable channel is
tracked during bronchoscopy, the steerable channel's position in
the model coordinate system becomes nominally known based on
position data from the EM sensor.
[0118] As used herein, a coordinate frame is the frame of reference
of a particular sensing modality. For example, for EM data the EM
coordinate frame is the frame of reference defined by the source of
the EM field (e.g., the field generator). For CT images and for a
segmented 3D model, this frame of reference is based on the frame
defined by the scanner. The present navigation systems address the
problem of navigation of representing (register) these different
sources of data (which are in their own frames of reference) to the
3D model (i.e. the CT frame), for example, in order to display the
location of the instrument inside the model.
[0119] Accordingly, as described in more detail below, the
disclosed luminal network navigation systems and techniques can
combine input from both image-based navigation systems, robotic
systems, and EM navigation systems, as well as input from other
patient sensors, in order to mitigate navigational problems and
enable more effective endoscopy procedures. For example, a
navigation fusion system can analyze image information received
from an instrument camera, position information from an EM sensor
on the instrument tip, and robotic position information from a
robotic system guiding movement of the instrument. Based on the
analysis, the navigation fusion framework can base instrument
position estimates and/or navigation decisions on one or more of
these types of navigation data. Some implementations of the
navigation fusion framework can further determine instrument
position relative to a 3D model of the luminal network. In some
embodiments, the initial instrument position used to initialize
tracking via the navigation fusion system can be generated based on
depth information as described herein.
[0120] The disclosed systems and techniques can provide advantages
for bronchoscopy guidance systems and other applications, including
other types of endoscopic procedures for navigation of luminal
networks. In anatomy, a "lumen" may refer to the inner open space
or cavity of an organ, as of an airway, a blood vessel, a kidney, a
heart, an intestine, or any other suitable organ in which a medical
procedure is being performed. As used herein, a "luminal network"
refers to an anatomical structure having at least one lumen leading
towards a target tissue site, for example, the airways of the
lungs, the circulatory system, calyx, and the gastrointestinal
system. Thus, although the present disclosure provides examples of
navigation systems relating to bronchoscopy, it will be appreciated
that the disclosed position estimation aspects are applicable to
other medical systems for navigation of a luminal network of a
patient. As such, the disclosed systems and techniques can be used
with bronchoscopes, ureteroscopes, gastrointestinal endoscopes, and
other suitable medical instruments.
3. Overview of Example Navigation Systems
[0121] FIG. 16A illustrates an example operating environment 100
implementing one or more aspects of the disclosed navigation
systems and techniques. The operating environment 100 includes
patient 101, a platform 102 supporting the patient 101, a medical
robotic system 110 guiding movement of endoscope 115, command
center 105 for controlling operations of the medical robotic system
110, EM controller 135, EM field generator 120, and EM sensors 125,
130. FIG. 16A also illustrates an outline of a region of a luminal
network 140 within the patient 101, shown in more detail in FIG.
16B.
[0122] The medical robotic system 110 can include one or more
robotic arms for positioning and guiding movement of endoscope 115
through the luminal network 140 of the patient 101. Command center
105 can be communicatively coupled to the medical robotic system
110 for receiving position data and/or providing control signals
from a user. As used herein, "communicatively coupled" refers to
any wired and/or wireless data transfer mediums, including but not
limited to a wireless wide area network (WWAN) (e.g., one or more
cellular networks), a wireless local area network (WLAN) (e.g.,
configured for one or more standards, such as the IEEE 802.11
(Wi-Fi)), Bluetooth, data transfer cables, and/or the like. The
medical robotic system 110 can be any of the systems described
above with respect to FIGS. 1-15. An embodiment of the medical
robotic system 110 is discussed in more detail with respect to FIG.
16C, and the command center 105 is discussed in more detail with
respect to FIG. 17.
[0123] The endoscope 115 may be a tubular and flexible surgical
instrument that is inserted into the anatomy of a patient to
capture images of the anatomy (e.g., body tissue) and provide a
working channel for insertion of other medical instruments to a
target tissue site. As described above, the endoscope 115 can be a
procedure-specific endoscope, for example a bronchoscope,
gastroscope, or ureteroscope, or may be a laparoscope or vascular
steerable catheter. The endoscope 115 can include one or more
imaging devices (e.g., cameras or other types of optical sensors)
at its distal end. The imaging devices may include one or more
optical components such as an optical fiber, fiber array,
photosensitive substrate, and/or lens(es). The optical components
move along with the tip of the endoscope 115 such that movement of
the tip of the endoscope 115 results in corresponding changes to
the field of view of the images captured by the imaging devices.
The distal end of the endoscope 115 can be provided with one or
more EM sensors 125 for tracking the position of the distal end
within an EM field generated around the luminal network 140. The
distal end of the endoscope 115 is further described with reference
to FIG. 18 below.
[0124] EM controller 135 can control EM field generator 120 to
produce a varying EM field. The EM field can be time-varying and/or
spatially varying, depending upon the embodiment. The EM field
generator 120 can be an EM field generating board in some
embodiments. Some embodiments of the disclosed patient navigation
systems can use an EM field generator board positioned between the
patient and the platform 102 supporting the patient, and the EM
field generator board can incorporate a thin barrier that minimizes
any tracking distortions caused by conductive or magnetic materials
located below it. In other embodiments, an EM field generator board
can be mounted on a robotic arm, for example, similar to those
shown in medical robotic system 110, which can offer flexible setup
options around the patient.
[0125] An EM spatial measurement system incorporated into the
command center 105, medical robotic system 110, and/or EM
controller 135 can determine the location of objects within the EM
field that are embedded or provided with EM sensor coils, for
example, EM sensors 125, 130. When an EM sensor is placed inside a
controlled, varying EM field as described herein, voltages are
induced in the sensor coils. These induced voltages can be used by
the EM spatial measurement system to calculate the position and
orientation of the EM sensor and thus the object having the EM
sensor. As the magnetic fields are of a low field strength and can
safely pass through human tissue, location measurement of an object
is possible without the line-of-sight constraints of an optical
spatial measurement system.
[0126] EM sensor 125 can be coupled to a distal end of the
endoscope 115 in order to track its location within the EM field.
The EM field is stationary relative to the EM field generator, and
a coordinate frame of a 3D model of the luminal network can be
mapped to a coordinate frame of the EM field. A number of
additional EM sensors 130 can be provided on the body surface of
the patient (e.g., in the region of the luminal network 140) in
order to aid in tracking the location of the EM sensor 125, for
example, by enabling compensation for patient movement including
displacement caused by respiration. A number of different EM
sensors 130 can be spaced apart on the body surface.
[0127] FIG. 16B illustrates an example luminal network 140 that can
be navigated in the operating environment 100 of FIG. 16A. The
luminal network 140 includes the branched structure of the airways
150 of the patient, the trachea 154 leading to the main carina 156
(the first bifurcation encountered during bronchoscopy navigation),
and a nodule (or lesion) 155 that can be accessed as described
herein for diagnosis and/or treatment. As illustrated, the nodule
155 is located at the periphery of the airways 150. The endoscope
115 has a first diameter and thus its distal end is not able to be
positioned through the smaller-diameter airways around the nodule
155. Accordingly, a steerable catheter 155 extends from the working
channel of the endoscope 115 the remaining distance to the nodule
155. The steerable catheter 145 may have a lumen through which
instruments, for example, biopsy needles, cytology brushes, and/or
tissue sampling forceps, can be passed to the target tissue site of
nodule 155. In such implementations, both the distal end of the
endoscope 115 and the distal end of the steerable catheter 145 can
be provided with EM sensors for tracking their position within the
airways 150. In other embodiments, the overall diameter of the
endoscope 115 may be small enough to reach the periphery without
the steerable catheter 155, or may be small enough to get close to
the periphery (e.g., within 2.5-3 cm) to deploy medical instruments
through a non-steerable catheter. The medical instruments deployed
through the endoscope 115 may be equipped with EM sensors, and the
position estimation techniques described below can be applied to
such medical instruments when they are deployed beyond the distal
tip of the endoscope 115.
[0128] In some embodiments, a 2D display of a 3D luminal network
model as described herein, or a cross-section of a 3D model, can
resemble FIG. 16B. Estimated position information can be overlaid
onto such a representation.
[0129] FIG. 16C illustrates an example robotic arm 175 of a medical
robotic system 110 for guiding instrument movement in through the
luminal network 140 of FIG. 16B. The robotic arm 175 can be robotic
arms 12, 39 described above in some embodiments, and is coupled to
base 180, which can be cart base 15, column 37 of patient platform
38, or a ceiling-based mount in various embodiments. As described
above, the robotic arm 175 includes multiple arm segments 170
coupled at joints 165, which provides the robotic arm 175 multiple
degrees of freedom.
[0130] The robotic arm 175 may be coupled to an instrument driver
190, for example instrument driver 62 described above, using a
mechanism changer interface (MCI) 160. The instrument driver 190
can be removed and replaced with a different type of instrument
driver, for example, a first type of instrument driver configured
to manipulate an endoscope or a second type of instrument driver
configured to manipulate a laparoscope. The MCI 160 includes
connectors to transfer pneumatic pressure, electrical power,
electrical signals, and optical signals from the robotic arm 175 to
the instrument driver 190. The MCI 160 can be a set screw or base
plate connector. The instrument driver 190 manipulates surgical
instruments, for example, the endoscope 115 using techniques
including direct drive, harmonic drive, geared drives, belts and
pulleys, magnetic drives, and the like. The MCI 160 is
interchangeable based on the type of instrument driver 190 and can
be customized for a certain type of surgical procedure. The robotic
175 arm can include a joint level torque sensing and a wrist at a
distal end.
[0131] Robotic arm 175 of the medical robotic system 110 can
manipulate the endoscope 115 using tendons as described above to
deflect the tip of the endoscope 115. The endoscope 115 may exhibit
nonlinear behavior in response to forces applied by the elongate
movement members. The nonlinear behavior may be based on stiffness
and compressibility of the endoscope 115, as well as variability in
slack or stiffness between different elongate movement members.
[0132] The base 180 can be positioned such that the robotic arm 175
has access to perform or assist with a surgical procedure on a
patient, while a user such as a physician may control the medical
robotic system 110 from the comfort of the command console. The
base 180 can be communicatively coupled to the command console 105
shown in FIG. 16A.
[0133] The base 180 can include a source of power 182, pneumatic
pressure 186, and control and sensor electronics 184--including
components such as a central processing unit, data bus, control
circuitry, and memory--and related actuators such as motors to move
the robotic arm 175. The electronics 184 can implement the
navigation control techniques described herein. The electronics 184
in the base 180 may also process and transmit control signals
communicated from the command console. In some embodiments, the
base 180 includes wheels 188 to transport the medical robotic
system 110 and wheel locks/brakes (not shown) for the wheels 188.
Mobility of the medical robotic system 110 helps accommodate space
constraints in a surgical operating room as well as facilitate
appropriate positioning and movement of surgical equipment.
Further, the mobility allows the robotic arm 175 to be configured
such that the robotic arm 175 does not interfere with the patient,
physician, anesthesiologist, or any other equipment. During
procedures, a user may control the robotic arm 175 using control
devices, for example, the command console.
[0134] FIG. 17 illustrates an example command console 200 that can
be used, for example, as the command console 105 in the example
operating environment 100. The command console 200 includes a
console base 201, display modules 202, e.g., monitors, and control
modules, e.g., a keyboard 203 and joystick 204. In some
embodiments, one or more of the command console 200 functionality
may be integrated into a base 180 of the medical robotic system 110
or another system communicatively coupled to the medical robotic
system 110. A user 205, e.g., a physician, remotely controls the
medical robotic system 110 from an ergonomic position using the
command console 200.
[0135] The console base 201 may include a central processing unit,
a memory unit, a data bus, and associated data communication ports
that are responsible for interpreting and processing signals such
as camera imagery and tracking sensor data, e.g., from the
endoscope 115 shown in FIGS. 16A-16C. In some embodiments, both the
console base 201 and the base 180 perform signal processing for
load-balancing. The console base 201 may also process commands and
instructions provided by the user 205 through the control modules
203 and 204. In addition to the keyboard 203 and joystick 204 shown
in FIG. 17, the control modules may include other devices, for
example, computer mice, trackpads, trackballs, control pads,
controllers such as handheld remote controllers, and sensors (e.g.,
motion sensors or cameras) that capture hand gestures and finger
gestures. A controller can include a set of user inputs (e.g.,
buttons, joysticks, directional pads, etc.) mapped to an operation
of the instrument (e.g., articulation, driving, water irrigation,
etc.).
[0136] The user 205 can control a surgical instrument such as the
endoscope 115 using the command console 200 in a velocity mode or
position control mode. In velocity mode, the user 205 directly
controls pitch and yaw motion of a distal end of the endoscope 115
based on direct manual control using the control modules. For
example, movement on the joystick 204 may be mapped to yaw and
pitch movement in the distal end of the endoscope 115. The joystick
204 can provide haptic feedback to the user 205. For example, the
joystick 204 may vibrate to indicate that the endoscope 115 cannot
further translate or rotate in a certain direction. The command
console 200 can also provide visual feedback (e.g., pop-up
messages) and/or audio feedback (e.g., beeping) to indicate that
the endoscope 115 has reached maximum translation or rotation.
[0137] In position control mode, the command console 200 uses a 3D
map of a patient luminal network and input from navigational
sensors as described herein to control a surgical instrument, e.g.,
the endoscope 115. The command console 200 provides control signals
to robotic arms 175 of the medical robotic system 110 to manipulate
the endoscope 115 to a target location. Due to the reliance on the
3D map, position control mode may require accurate mapping of the
anatomy of the patient.
[0138] In some embodiments, users 205 can manually manipulate
robotic arms 175 of the medical robotic system 110 without using
the command console 200. During setup in a surgical operating room,
the users 205 may move the robotic arms 175, endoscope 115 (or
endoscopes), and other surgical equipment to access a patient. The
medical robotic system 110 may rely on force feedback and inertia
control from the users 205 to determine appropriate configuration
of the robotic arms 175 and equipment.
[0139] The displays 202 may include electronic monitors (e.g., LCD
displays, LED displays, touch-sensitive displays), virtual reality
viewing devices, e.g., goggles or glasses, and/or other display
devices. In some embodiments, the display modules 202 are
integrated with the control modules, for example, as a tablet
device with a touchscreen. In some embodiments, one of the displays
202 can display a 3D model of the patient's luminal network and
virtual navigation information (e.g., a virtual representation of
the end of the endoscope within the model based on EM sensor
position) while the other of the displays 202 can display image
information received from the camera or another sensing device at
the end of the endoscope 115. In some implementations, the user 205
can both view data and input commands to the medical robotic system
110 using the integrated displays 202 and control modules. The
displays 202 can display 2D renderings of 3D images and/or 3D
images using a stereoscopic device, e.g., a visor or goggles. The
3D images provide an "endo view" (i.e., endoscopic view), which is
a computer 3D model illustrating the anatomy of a patient. The
"endo view" provides a virtual environment of the patient's
interior and an expected location of an endoscope 115 inside the
patient. A user 205 compares the "endo view" model to actual images
captured by a camera to help mentally orient and confirm that the
endoscope 115 is in the correct--or approximately correct--location
within the patient. The "endo view" provides information about
anatomical structures, e.g., the shape of airways, circulatory
vessels, or an intestine or colon of the patient, around the distal
end of the endoscope 115. The display modules 202 can
simultaneously display the 3D model and CT scans of the anatomy the
around distal end of the endoscope 115. Further, the display
modules 202 may overlay the already determined navigation paths of
the endoscope 115 on the 3D model and CT scans.
[0140] In some embodiments, a model of the endoscope 115 is
displayed with the 3D models to help indicate a status of a
surgical procedure. For example, the CT scans identify a lesion in
the anatomy where a biopsy may be necessary. During operation, the
display modules 202 may show a reference image captured by the
endoscope 115 corresponding to the current location of the
endoscope 115. The display modules 202 may automatically display
different views of the model of the endoscope 115 depending on user
settings and a particular surgical procedure. For example, the
display modules 202 show an overhead fluoroscopic view of the
endoscope 115 during a navigation step as the endoscope 115
approaches an operative region of a patient.
[0141] FIG. 18 illustrates the distal end 300 of an example
endoscope having imaging and EM sensing capabilities as described
herein, for example, the endoscope 115 of FIGS. 16A-16C. In FIG.
18, the distal end 300 of the endoscope includes an imaging device
315, illumination sources 310, and ends of EM sensor coils 305. The
distal end 300 further includes an opening to a working channel 320
of the endoscope through which surgical instruments, such as biopsy
needles, cytology brushes, and forceps, may be inserted along the
endoscope shaft, allowing access to the area near the endoscope
tip.
[0142] The illumination sources 310 provide light to illuminate a
portion of an anatomical space. The illumination sources can each
be one or more light-emitting devices configured to emit light at a
selected wavelength or range of wavelengths. The wavelengths can be
any suitable wavelength, for example, visible spectrum light,
infrared light, x-ray (e.g., for fluoroscopy), to name a few
examples. In some embodiments, illumination sources 310 can include
light-emitting diodes (LEDs) located at the distal end 300. In some
embodiments, illumination sources 310 can include one or more fiber
optic fibers extending through a length of the endoscope to
transmit light through the distal end 300 from a remote light
source, for example, an x-ray generator. Where the distal end 300
includes multiple illumination sources 310 these can each be
configured to emit the same or different wavelengths of light as
one another.
[0143] The imaging device 315 can include any photosensitive
substrate or structure configured to convert energy representing
received light into electric signals, for example, a charge-coupled
device (CCD) or complementary metal-oxide semiconductor (CMOS)
image sensor. Some examples of imaging device 315 can include one
or more optical fibers, for example, a fiber optic bundle,
configured to transmit light representing an image from the distal
end 300 of the endoscope to an eyepiece and/or image sensor near
the proximal end of the endoscope. Imaging device 315 can
additionally include one or more lenses and/or wavelength pass or
cutoff filters as required for various optical designs. The light
emitted from the illumination sources 310 allows the imaging device
315 to capture images of the interior of a patient's luminal
network. These images can then be transmitted as individual frames
or series of successive frames (e.g., a video) to a computer system
such as command console 200 for processing as described herein.
[0144] Electromagnetic coils 305 located on the distal end 300 may
be used with an electromagnetic tracking system to detect the
position and orientation of the distal end 300 of the endoscope
while it is disposed within an anatomical system. In some
embodiments, the coils 305 may be angled to provide sensitivity to
electromagnetic fields along different axes, giving the disclosed
navigational systems the ability to measure a full 6 degrees of
freedom: three positional and three angular. In other embodiments,
only a single coil may be disposed on or within the distal end 300
with its axis oriented along the endoscope shaft of the endoscope.
Due to the rotational symmetry of such a system, it is insensitive
to roll about its axis, so only 5 degrees of freedom may be
detected in such an implementation.
[0145] FIG. 19 illustrates a schematic block diagram of an example
navigation fusion system 400 as described herein. As described in
more detail below, using the system 400, data from a number of
different sources is combined and repeatedly analyzed during a
surgical procedure to provide an estimation of the real-time
movement information and location/orientation information of a
surgical instrument (e.g., the endoscope) within the luminal
network of the patient and to make navigation decisions.
[0146] The navigation fusion system 400 includes a number of data
repositories including depth features data repository 405,
endoscope EM sensor data repository 415, registration data
repository 475, model data repository 425, endoscope imaging data
repository 480, navigation path data repository 445, and robotic
position data repository 470. Though shown separately in FIG. 19
for purposes of clarity in the discussion below, it will be
appreciated that some or all of the data repositories can be stored
together in a single memory or set of memories. The system 400 also
includes a number of processing modules including a registration
calculator 465, depth-based position estimator 410, location
calculator 430, image analyzer 435, state estimator 440, and
navigation controller 460. Each module can represent a set of
computer-readable instructions, stored in a memory, and one or more
processors configured by the instructions for performing the
features described below together. The navigation fusion system 400
can be implemented as one or more data storage devices and one or
more hardware processors, for example, in the control and sensor
electronics 184 and/or console base 201 described above. The
navigation fusion system 400 can be an embodiment of the
localization system 90 in some implementations.
[0147] FIG. 19 also illustrates modeling system 420 in
communication with the navigation fusion system 400. As described
in more detail below, using the modeling system 420, data
representing a number of images of a patient's anatomical luminal
network can be analyzed to build a three-dimensional model of a
virtual representation of the anatomical luminal network, and this
virtual anatomical luminal network can be used to build the depth
features data repository 405. Though illustrated separately, in
some embodiments the modeling system 420 and navigation fusion
system 400 can be combined into a single system. The modeling
system 420 includes a number of processing modules including model
generator 440 and feature extractor 450. Although the model data
repository 425 and depth features data repository 405 are
illustrated within the navigation fusion system 400, these data
repositories can in some implementations be located alternatively
or additionally within the modeling system 420.
[0148] The model generator 440 is a module configured to receive
data from a medical imaging system (not illustrated), for example,
a CT imaging system or magnetic resonance imaging system. The
received data can include a series of two-dimensional images
representing the anatomical luminal network of the patient. The
model generator 440 can generate a three-dimensional volume of data
from the series of two-dimensional images, and can form the virtual
three-dimensional model of the internal surfaces of the anatomical
luminal network from the three-dimensional volume of data. For
example, the model generator can apply segmentation to identify
portions of the data corresponding to the tissue of the anatomical
luminal network. As such, the resulting model can represent the
interior surfaces of the tissue of the anatomical luminal
network.
[0149] The model data repository 425 is a data storage device that
stores data representing a model of the luminal network of the
patient, for example, the model generated by the model generator
440. Such a model can provide 3D information about the structure
and connectivity of the luminal network, including the topography
and/or diameters of patient airways in some examples. Some CT scans
of patient lungs are performed at breath-hold so that the patient's
airways are expanded to their full diameter in the model.
[0150] The endoscope imaging data repository 480 is a data storage
device that stores image data received from a camera at a distal
end of an endoscope, for example, the imaging device 315. The image
data can be discrete images or series of image frames in a video
sequence in various embodiments.
[0151] The feature extractor 450 is a module configured to receive
the model from the model generator 440 and build a database of
depth features corresponding to a number of different location
within the model. For example, the feature extractor 450 can
identify a number of different locations within the model,
computationally position a virtual imaging device at each of the
locations, generate a virtual image at each location, and then
derive specified features from the virtual image. A "virtual
imaging device" as described herein is not a physical imaging
device, but rather a computational simulation of an image capture
device. The simulation can generate virtual images based on virtual
imaging device parameters including field of view, lens distortion,
focal length, and brightness shading, which can in turn be based on
parameters of an actual imaging device.
[0152] Each generated virtual image can correspond to a virtual
depth map representing the distance between the location of the
virtual imaging device and tissue of the virtual luminal network
within the virtual field of view of the virtual imaging device. The
feature extractor 450 can match the virtual imaging device
parameters to the parameters of an actual imaging device that has
been identified for use in a medical procedure involving the
patient's luminal network. An example process for building the
database is described in more detail below with respect to FIG.
20.
[0153] The feature extractor 450 can also receive data from the
endoscope imaging data repository 480, generate a depth map
representing the distance between the endoscope imaging device and
the imaged tissue represented by pixels of the image, and derive
features from the generated depth map. In some embodiments, the
feature extractor 450 can use photoclinometry (e.g., shape by
shading) processing to generate a depth map based on a single
image. In some embodiments, the feature extractor 450 can use a
stereoscopic image set depicting the imaged region to generate a
depth map.
[0154] Depth features data repository 405 is a data storage device
that stores a database of features derived from depth maps and/or
virtual depth maps, as generated by the feature extractor 450. The
features can vary based on the nature of the luminal network and/or
the use of the features during the navigation procedure. The
features can include, for example, positions of local maxima within
the depth map (e.g., representing the farthest virtual tissue
visible down a branch of an airway), positions along a curve peak
surrounding a local maxima, a value representing the distance
(e.g., number of pixels between) separating two local maxima,
and/or the size, shape, and orientation of a line or polygon
connecting a number of local maxima. A curve peak represents a
region in the depth map at which depth values of pixels on one side
of the curve peak are increasing while depth values on the other
side of the curve peak are decreasing. The curve peak can include a
local maximum where the depth associated with a pixel is greater
than depths associated with pixels on either side of the pixel. The
depth features data repository 405 can store the features and
associated locations within the virtual luminal network as a tuple,
for example, in the following form--{location.sub.n, feature
value}--for each identified location. As an example, when the
location relates to position within an airway and the feature
relates to the distance between two identified local maxima, the
tuple can be generated as {location.sub.n (airway segment, depth
within airway segment), feature value (distance)}. As such, the
extracted features in the database can be quickly programmatically
evaluated in comparison to features extracted in real-time from
images of a navigated anatomical luminal network, and a location
corresponding to an identified best or close feature match can be
quickly ascertained.
[0155] The depth-based position estimator 410 is a module
configured to compare the feature(s) extracted in real-time from
images of the anatomical luminal network to the pre-computed
feature(s) extracted from virtual images. The depth-based position
estimator 410 can scan the depth features data repository 405 for a
match of a virtual feature to the feature extracted from an actual
image, and can use the location corresponding to the match as the
position of the instrument (e.g., an endoscope) within the
anatomical luminal network. The match can be an exact match, the
best match among the available features in the depth features data
repository 405, a match within a threshold difference from the
extracted feature. The depth-based position estimator 410 can
output the position to the state estimator 440, for example, for
use as an initial position (a "prior") in a probabilistic
evaluation of the position of the instrument, or for use as a prior
after occurrence of an adverse event (e.g., coughing) in which the
precise location of the instrument becomes unknown. The depth-based
position estimator 410 can output the position to the registration
calculator 465 for use in generating an initial registration
between the model and an EM field disposed around the patient
and/or an updated registration.
[0156] The endoscope EM sensor data repository 415 is a data
storage device that stores data derived from an EM sensor at the
distal end of an endoscope. As described above, such a sensor could
include EM sensor 125, and EM sensor coils 305 and the resulting
data can be used to identify position and orientation of the sensor
within the EM field. Similar to the data from EM respiration
sensors, data for an endoscope EM sensor can be stored as a tuple
in the form of {x, y, z, t.sub.n} where x, y, and z represent the
coordinates of the sensor in the EM field at time t.sub.n. Some
embodiments may further include roll, pitch, and yaw of the
instrument in the EM sensor tuple. The endoscope EM sensor data
repository 415 can store a number of such tuples for each
endoscope-based sensor corresponding to a number of different
times.
[0157] The registration calculator 465 is a module that can
identify a registration or mapping between the coordinate frame of
the 3D model (e.g., a coordinate frame of the CT scanner used to
generate the model) and the coordinate frame of the EM field (e.g.,
of the EM field generator 120). In order to track a sensor through
the patient's anatomy, the navigation fusion system 400 may require
a process known as "registration," by which the registration
calculator 465 finds the geometric transformation that aligns a
single object between different coordinate systems. For instance, a
specific anatomical site on a patient may have a representation in
the 3D model coordinates and also in the EM sensor coordinates. In
order to calculate an initial registration, one implementation of
the registration calculator 465 can perform registration as
described in U.S. application Ser. No. 15/268,238, filed Sep. 17,
2016, titled "Navigation of Tubular Networks," the disclosure of
which is hereby incorporated by reference. As an example of one
possible registration technique, the registration calculator 465
can receive data from the endoscope imaging data repository 480 and
the EM sensor data repository 415 at a number of different points
as the endoscope is inserted into the airways of the patient, for
example, as the endoscope reaches various bifurcations. The image
data can be used to identify when the distal end of the endoscope
has reached a bifurcation, for example, via automated feature
analysis. The registration calculator 465 can receive data from the
endoscope EM sensor data repository 415 and identify a location of
the EM sensor at the distal end of the endoscope as the endoscope
is positioned at the bifurcation. Some examples can use not only
bifurcations but other points in the patient's airway, and may map
such points to corresponding points in a "skeleton" model of the
airway. The registration calculator 465 can use data linking at
least three of EM positions to points in the model in order to
identify the geometric transformation between the EM field and the
model. Another embodiment can involve manual registration, for
example, by taking at least 3 from a first bifurcation of the
patient's airway and from two more bifurcations in the left and
right lungs, and can use the corresponding points to calculate the
registration. This data to perform the geometric transformation
(also referred to as registration data) can be stored in the
registration data repository 475 as registration data.
[0158] After the initial registration is determined, the
registration calculator 465 may update its estimate of the
registration transform based on received data so as to increase
transform accuracy as well as to compensate for changes to the
navigation system, e.g., changes due to movement of the patient. In
some aspects, the registration calculator 465 may update the
estimate of the registration transform continually, at defined
intervals, and/or based on the position of the endoscope (or
component(s) thereof) in the luminal network.
[0159] Registration data repository 475 is a data storage device
that stores the registration data that, as just discussed, is
usable to perform a geometric transformation from the coordinate
frame of the EM field to the coordinate frame of the model. Also
discussed above, the registration data may be generated by the
registration calculator 465 and may be updated continually or
periodically in some implementations.
[0160] The location calculator 430 is a module that receives data
from the model data repository 425, registration data repository
475, and the scope position estimator 420 to translate EM sensor
coordinates into 3D model coordinates. The scope position estimator
420 calculates an initial position of the EM sensor relative to the
position of the EM field generator, as described above. This
position also corresponds to a location within the 3D model. In
order to translate the initial position of the EM sensor from the
EM coordinate frame into the model coordinate frame, the location
calculator 430 can access the mapping between the EM coordinate
frame and the model coordinate frame (e.g., registration data) as
stored in the registration data repository 475. In order to
translate the scope position into the 3D model coordinate frame,
the location calculator 430 receives, as input, data representing
the topography of the 3D model from the model data repository 425,
data representing the registration between the EM field and the
coordinate frame of the 3D model from the registration data
repository 475, and the position of the scope in the EM field from
the scope position estimator 420. Some embodiments can also receive
prior estimated state data from the state estimator 440. Based on
the received data, the location calculator 430 may perform, e.g.,
on-the-fly transformation of the EM sensor position data to a
position in the 3D model. This can represent a preliminary estimate
of the position of the distal end of the scope within the
topography of the 3D model and can be provided as one input to the
state estimator 440 for generating a final estimate of the scope
position, as described in more detail below.
[0161] The image analyzer 435 is a module that receives data from
the endoscope imaging data repository 480 and model data repository
425 and can compare this data to determine endoscope positioning.
For example, the image analyzer 435 can access volume-rendered or
surface-rendered endoluminal images of the airway tree from the
model scans and can compare the rendered images with the real-time
image or video frames from the imaging device 315. For example, the
images can be registered (e.g., using Powell's optimization,
simplex or gradient methods, gradient descent algorithms with
normalized cross correlation or mutual information as costs), and
then weighted normalized sum of square difference errors and
normalized mutual information can be used for comparing the
registered images obtained from the two sources. Similarity between
a 2D image from the scan and a 2D image received from the endoscope
can indicate that the endoscope is positioned near the location of
the image from the scan. Such image-based navigation can perform
local registrations at bifurcations of patient airways and so can
be less susceptible to noise due to patient breathing motion than
EM tracking systems. However, as the image analyzer 435 relies on
the endoscope video, the analysis can be affected by artifacts in
the images caused by patient coughing or mucous obstruction.
[0162] The image analyzer 435 can implement object recognition
techniques in some embodiments, by which the image analyzer 435 can
detect objects present in the field of view of the image data, such
as branch openings, lesions, or particles. Using object
recognition, the image analyzer can output object data indicating
information about what objects were identified, as well as
positions, orientations, and/or sizes of objects represented as
probabilities. As one example, object recognition can be used to
detect objects that may indicate branch points in a luminal network
and then determine their position, size, and/or orientation. In one
embodiment, in a given image within a luminal network, each branch
will typically appear as a dark, approximately elliptical region,
and these regions may be detected automatically by a processor,
using region-detection algorithms such as maximally stable extremal
regions (MSER) as objects. The image analyzer 435 can use light
reflective intensity combined with other techniques to identify
airways. Further, image analyzer 435 can further track detected
objects across a set of sequential image frames to detect which
branch has been entered from among a set of possible branches in
the luminal network.
[0163] The robotic position data repository 470 is a data storage
device that stores robotic position data received from medical
robotic system 110, for example, data related to physical movement
of the medical instrument or part of the medical instrument (e.g.,
the instrument tip or distal end) by the medical robotic system 110
within the luminal network. Example robotic position data may
include, e.g., command data instructing the instrument tip to reach
a specific anatomical site and/or change its orientation (e.g.,
with a specific pitch, roll, yaw, insertion, and retraction for one
or both of a leader and a sheath of an endoscopic instrument)
within the luminal network, insertion data representing insertion
movement of the part of the medical instrument (e.g., the
instrument tip or sheath), instrument driver data, and mechanical
data representing mechanical movement of an elongate member of the
medical instrument, such as, for example, motion of one or more
pull wires, tendons or shafts of the endoscope that drive the
actual movement of the endoscope within the luminal network.
[0164] The navigation path data repository 445 is a data storage
device that stores data representing a pre-planned navigation path
through the luminal network to a target tissue site. Navigating to
a particular point in a luminal network of a patient's body may
require certain steps to be taken pre-operatively in order to
generate the information needed to create the 3D model of the
tubular network and to determine a navigation path within it. As
described above, a 3D model may be generated of the topography and
structure of the specific patient's airways. A target can be
selected, for example, a lesion to biopsy or a portion of organ
tissue to repair surgically. In one embodiment, the user is capable
of selecting the location of the target by interfacing with a
computer display that can show the 3D model, such as by clicking
with a mouse or touching a touchscreen. In some embodiments, the
navigation path may be identified programmatically by analysis of
the model and an identified lesion site to derive a shortest
navigation path to the lesion. In some embodiments the path may be
identified by a physician, or an automatically-identified path may
be modified by a physician. The navigation path can identify a
sequence of branches within the luminal network to travel through
so as to reach the identified target.
[0165] The state estimator 440 is a module that receives inputs and
performs analysis of the inputs to determine a state of the medical
instrument. For example, the state estimator 440 can receive, as
inputs, data from the depth-based position estimator 410, location
calculator 430, image analyzer 435, navigation path data repository
445, and robotic position data repository 470. The state estimator
440 can implement a probabilistic analysis to determine a state and
corresponding probability of the medical instrument within the
luminal network given the provided inputs. Estimated state can
refer to one or more of (1) the x,y,z position of the instrument
relative to a coordinate frame of a model of the luminal network,
(2) whether the instrument is located in a certain region of the
model, for example, a particular airway branch, (3) pitch, roll,
yaw, insertion, and/or retraction of the instrument, and (4)
distance to target. The state estimator 440 can provide the
estimated state of the instrument (or the distal tip of the
instrument) as a function of time.
[0166] In some embodiments, the state estimator 440 can implement a
Bayesian framework to determine the state and corresponding
probability. Bayesian statistical analysis starts with a belief,
called a prior, and then update that belief with observed data. The
prior represents an estimate of what the Bayesian model parameters
might be and can be represented as a parameterized distribution.
The observed data can be gathered to obtain evidence about actual
values of the parameters. The outcome of Bayesian analysis is
called a posterior, and represents a probabilistic distribution
expressing events in terms of confidence. If further data is
obtained the posterior can be treated as the prior and updated with
the new data. This process employs the Bayes rule, which indicates
a conditional probability, for example, how likely is event A if
event B happens.
[0167] With respect to the disclosed navigation fusion system 400,
the state estimator 440 can use previously estimated state data as
the prior and can use the inputs from the respiration frequency
and/or phase identifier 410, scope position estimator 420, location
calculator 430, image analyzer 435, navigation path data repository
445, and/or robotic position data repository 470 as observed data.
At the outset of a procedure, the described vision-based
initialization techniques can be used to estimate the initial depth
and roll in the trachea, and this estimate output from the
depth-based position estimator 410 can be used as the prior. The
state estimator 440 can perform Bayesian statistical analysis of
the prior and observed data to generate a posterior distribution
representing a probability and confidence value of each of a number
of possible states.
[0168] The "probability" of the "probability distribution", as used
herein, refers to a likelihood of an estimation of a possible
location and/or orientation of the medical instrument being
correct. For example, different probabilities may be calculated by
one of the algorithm modules indicating the relative likelihood
that the medical instrument is in one of several different possible
branches within the luminal network. In one embodiment, the type of
probability distribution (e.g., discrete distribution or continuous
distribution) is chosen to match features of an estimated state
(e.g., type of the estimated state, for example, continuous
position information vs. discrete branch choice). As one example,
estimated states for identifying which segment the medical
instrument is in for a trifurcation may be represented by a
discrete probability distribution, and may include three discrete
values of 20%, 30% and 50% representing chance as being in the
location inside each of the three branches as determined by one of
the algorithm modules. As another example, the estimated state may
include a roll angle of the medical instrument of 40.+-.5 degrees
and a segment depth of the instrument tip within a branch may be is
4.+-.1 mm, each represented by a Gaussian distribution which is a
type of continuous probability distribution.
[0169] In contrast, the "confidence value," as used herein,
reflects a measure of confidence in the estimation of the state
provided by one of the modules of FIG. 19 based one or more
factors. For the EM-based modules, factors such as distortion to EM
Field, inaccuracy in EM registration, shift or movement of the
patient, and respiration of the patient may affect the confidence
in estimation of the state. Particularly, the confidence value in
estimation of the state provided by the EM-based modules may depend
on the particular respiration cycle of the patient, movement of the
patient or the EM field generators, and the location within the
anatomy where the instrument tip locates. For the image analyzer
435, examples factors that may affect the confidence value in
estimation of the state include illumination condition for the
location within the anatomy where the images are captured, presence
of fluid, tissue, or other obstructions against or in front of the
optical sensor capturing the images, respiration of the patient,
condition of the tubular network of the patient itself (e.g., lung)
such as the general fluid inside the tubular network and occlusion
of the tubular network, and specific operating techniques used in,
e.g., navigating or image capturing.
[0170] For example, one factor may be that a particular algorithm
has differing levels of accuracy at different depths in a patient's
lungs, such that relatively close to the airway opening, a
particular algorithm may have a high confidence in its estimations
of medical instrument location and orientation, but the further
into the bottom of the lung the medical instrument travels that
confidence value may drop. Generally, the confidence value is based
on one or more systemic factors relating to the process by which a
result is determined, whereas probability is a relative measure
that arises when trying to determine the correct result from
multiple possibilities with a single algorithm based on underlying
data.
[0171] As one example, a mathematical equation for calculating
results of an estimated state represented by a discrete probability
distribution (e.g., branch/segment identification for a
trifurcation with three values of an estimated state involved) can
be as follows:
S.sub.1=C.sub.EM*P.sub.1,EM+C.sub.Image*P.sub.1,Image+C.sub.Robot*P.sub.-
1,Robot;
S.sub.2=C.sub.EM*P.sub.2,EM+C.sub.Image*P.sub.2,Image+C.sub.Robot*P.sub.-
2,Robot;
S.sub.3=C.sub.EM*P.sub.3,EM+C.sub.Image*P.sub.3,Image+C.sub.Robot*P.sub.-
3,Robot.
[0172] In the example mathematical equation above, S.sub.i (i=1, 2,
3) represents possible example values of an estimated state in a
case where 3 possible segments are identified or present in the 3D
model, C.sub.EM, C.sub.image, and C.sub.Robot represents confidence
value corresponding to EM-based algorithm, image-based algorithm,
and robot-based algorithm and P.sub.i,EM, P.sub.i,image, and
P.sub.i,Robot represent the probabilities for segment i. Because of
the probabilistic nature of such a fusion algorithm, respiration
can be tracked over time and even predicted to overcome latency and
outlier disturbances.
[0173] In some embodiments, confidence values for data from the
robotic position data 470, location calculator 435, and image
analyzer 435 can be adaptively determined based on the respiration
phase from the respiration frequency and/or phase identifier 410.
For example, robotic position data and image data can be affected
differently than EM sensor data by respiration motion. In some
embodiments, vision data obtained from the endoscope imaging data
repository 430 can be used to detect certain kinds of respiratory
motion that are not detectable via sensors external to the luminal
network, for example, movement of an airway in a cranial-caudal
(backward-forward) motion that can be detected through vision
processing.
[0174] The navigation controller 460 is a module that receives data
from the state estimator 440 and the navigation path data
repository 445 and uses this data to guide further operation of the
medical robotic system 110. For example, the navigation controller
460 can plot the estimated state along a predetermined navigation
path and can determine a next movement (e.g., extension/retraction
distance, roll, actuation of pull wires or other actuating
mechanisms) for the instrument to advance along the navigation
path. The navigation controller 460 can automatically control the
instrument according to the determined next movement in some
embodiments. In some embodiments the navigation controller 460 can
output specific instrument movement instructions and/or instrument
driver operation instructions for display to the user, such as by
the workstation 200. The navigation controller 460 can cause
display of side-by-side views of a slice of the 3D model at the
estimated position and of the real-time images received from the
scope imaging data repository 480 in some embodiments in order to
facilitate user-guided navigation.
4. Overview of Example Navigation Techniques
[0175] In accordance with one or more aspects of the present
disclosure, FIG. 20 depicts a flowchart of an example process 500
for generating an extracted virtual feature data set. In some
embodiments, the process 500 can be performed pre-operatively, that
is, before the start of a medical procedure that uses the models
generated by, and features extracted by, the process 500. The
process 500 can be implemented in the modeling system 420 FIG. 19,
the control and sensor electronics 184 of FIG. 16C, and/or the
console base 201 of FIG. 17, or component(s) thereof. The graphical
depictions within the flowchart of FIG. 20 are provided to
illustrate and not limit the described blocks, and it will be
appreciated that the visual representations of the depicted model
515, depth maps 532, 534, and associated features may or may not be
generated and displayed during the course of the process 500.
[0176] At block 510, model generator 440 can access image data
representative of a patient's anatomical luminal network and
generate a three-dimensional model 515. For example, CT scans or
MRI scans can generate a number of images depicting two-dimensional
cross-sections of the anatomical luminal network. The model
generator 440 can segment these two-dimensional images to isolate
or segment the tissue of the anatomical luminal network, and can
then build a three-dimensional point cloud of data based on the
isolated tissue positions in the various images and based on the
spatial relationship of the cross-sections depicted in the images.
The model generator 440 can generate the model based on this
three-dimensional point cloud. The three-dimensional model can
model the interior surfaces of the anatomical luminal network as a
virtual luminal network. For example, the model 515 can be a
segmented map of a patient's airways generated from CT scans in
some implementations. The model can be any two or three dimensional
representation of the actual luminal network (or a portion of the
luminal network) of the patient.
[0177] At block 520, the feature extractor 450 can identify a
number of virtual locations 525 within the model 515. As one
example, the feature extractor 450 can identify a number of
locations within a trachea segment of a model representing patient
airways, for example, a hundred and twenty locations, or greater or
fewer locations depending on the parameters of the navigation
system 400. In other examples the feature extractor 450 can
identify locations within other segments of an airway model, for
instance locations along a planned navigation path through the
airway model, along the planned navigation path and branches within
a predetermined proximity to the planned navigation path, or
throughout some or all of the airway segments.
[0178] At block 530, the feature extractor 450 can generate a
number of virtual depth maps 532, 534 corresponding to the
identified locations. For example, the feature extractor 450 can
use the identified locations to set locations of a virtual imaging
device within the virtual anatomical luminal network, and can
generate a virtual depth map 532, 534 for each identified location.
The depicted example virtual depth map 532 and virtual depth map
534 depict different representations of the same depth information
relating to a virtual representation of a main carina 156 in an
airway. Each virtual pixel in the two-dimensional representation of
virtual depth map 532 is depicted with a color corresponding to its
depth value, while the three-dimensional representation of virtual
depth map 534 depicts a dual-peak shape where each virtual pixel is
shown at a height along a z-axis corresponding to its depth value.
The depicted virtual depth maps are provided to illustrated the
concepts of block 530, however in some implementations of the
process 500 no such visual representations may be generated, as the
process 500 may only require the data representing such depth maps
in order to derive features as described below.
[0179] In some implementations, at block 530 the feature extractor
450 can access parameters of an imaging device identified for use
during a medical procedure during which the luminal network will be
navigated, for example, imaging device 315 at the distal end of an
endoscope. The feature extractor 450 can set virtual parameters of
the virtual imaging device to match the parameters of the imaging
device. Such parameters can include field of view, lens distortion,
focal length, and brightness shading, and can be based on
calibration data or data obtained by testing the imaging device.
Brightness shading, also known as vignetting, is a position
dependent variation in the amount of light transmitted by an
optical system causing darkening of an image near the edges.
Vignetting results in a decrease in the amount of light transmitted
by an optical system near the periphery of the lens field-of-view
(FOV), causing gradual darkening of an image at the edges.
Vignetting can be corrected after image capture by calibrating a
lens roll off distortion function of the camera. By matching the
virtual parameters to the actual parameters, the resulting virtual
depth maps 532, 534 may more closely correspond to actual depth
maps generated based on images captured by the imaging device.
[0180] At block 540, the feature extractor 450 analyzes the values
of the virtual depth maps in order to identify one or more depth
criteria. A depth criterion can be, for example, the position of a
local maxima within the depth map (e.g., a pixel representing the
farthest virtual tissue visible down a branch of an virtual airway
model) or any position within a threshold distance from the local
maxima along a curve peak surrounding the local maxima. The
described depth criterion positions can be virtual pixel locations
within the virtual depth map.
[0181] Block 540 provides a visual illustration of example depth
criterion 522 and 544 as local maxima, corresponding to the most
distant virtual tissue visible by the virtual camera within the
virtual left bronchus and virtual right bronchus. As a general
rule, due to the typical shape of the human lungs, a camera or
virtual camera positioned near the main carina will be able to see
farther into the right bronchus than the left bronchus.
Accordingly, the depth criterion 544 corresponds to the most
distant depicted virtual tissue within the right bronchus as it has
a greater value than depth criterion 542, and the depth criterion
542 corresponds to the most distant depicted virtual tissue within
the left bronchus. Such information can assist in identifying roll
as described herein.
[0182] At block 550, the feature extractor 450 derives a
pre-identified virtual feature from the identified depth criteria.
For example, as shown the feature extractor 450 can identify the
value of the distance 555 separating the depth criteria 542, 544.
The distance value can be represented as a number of pixels in an
(x,y) space corresponding to a two-dimensional depth map 532 or an
(x,y,z) vector corresponding to the three-dimensional depth map
544. The feature extractor 450 can additionally or alternatively
derive the identification and positioning of the right and left
bronchus as the feature(s). In other implementations, for example,
involving depth maps at locations that view branchings of three or
more airways, the feature can include the size, shape, and
orientation of a polygon connecting three or more local maxima.
[0183] At block 560, the feature extractor 450 can generate a
database of the virtual location(s) and associated extracted
virtual feature(s). This database can be provided to navigation
system 400 for use in calculating real-time instrument position
determinations, for example, to automatically initialize a
probabilistic state estimation, calculate registrations, and
perform other navigation-related calculations.
[0184] FIG. 21 depicts a flowchart of an example intra-operative
process 600 for generating depth information based on captured
endoscopic images and calculated correspondence between features of
the depth information with the extracted virtual feature data set
of FIG. 20. The process 600 can be implemented by the modeling
system 420 and/or navigation fusion system 400 FIG. 19, the control
and sensor electronics 184 of FIG. 16C, and/or the console base 201
of FIG. 17, or component(s) thereof.
[0185] At block 610, the feature extractor 450 receives imaging
data captured by an imaging device at the distal end of an
instrument positioned within a patient's anatomical luminal
network. For example, the imaging device can be imaging device 315
described above. An example visual representation of the imaging
data is shown by image 615 depicting the main carina of patient
airways. The image 615 depicts the anatomical main carina
corresponding to the virtual main carina represented by the virtual
depth map 532 of FIG. 20. Image 615 depicts specific features using
a specific visual representation, and image 615 is provided to
illustrate and not limit the process 600. Image 615 is
representative of endoluminal image data suitable for use in the
process 600, and other suitable image data can represent other
anatomical structures and/or be depicted as images using different
visual representations. Further, some embodiments of the process
600 may operate on imaging data (e.g., values of pixels received
from an image sensor of the imaging device) without generating a
corresponding visible representation of the image data (e.g., image
615).
[0186] At block 620, the feature extractor 450 generates a depth
map 620 corresponding to the imaging data represented by image 615.
Feature extractor 450 can calculate, for each pixel of the imaging
data, a depth value representing an estimated distance between the
imaging device and a tissue surface within the anatomical luminal
network represented that is corresponding to the pixel.
Specifically, the depth value can represent an estimate of the
physical distance between an entrance pupil of the imaging device's
optical system and the imaged tissue depicted by the pixel. In some
embodiments, the feature extractor 450 can use photoclinometry
(e.g., shape by shading) processing to generate a depth map based
on a single image 615. By using photoclinometry, the feature
extractor 450 can be robust to outliers due to reflectance
differences between portions of the tissue that may be covered in
fluid (e.g. mucous). In some embodiments, the feature extractor 450
can use a stereoscopic image set depicting the imaged region to
generate a depth map. For example, a robotically-controlled
endoscope can capture a first image at a first location, be
robotically retracted, extended, and/or turned a known distance to
a second location, and can capture second image at the second
location. The feature extractor 450 can use the known translation
of the robotically-controlled endoscope and the disparity between
the first and second images to generate the depth map.
[0187] At block 630, the feature extractor 450 identifies one or
more depth criteria in the depth map. As described above with
respect to the virtual depth maps, a depth criterion in a depth map
generated based on real image data can be, for example, the
position of a local maxima within the depth map (e.g., a pixel
representing the farthest anatomical tissue visible down a branch
of an airway of the patient) or any position within a threshold
distance from the local maxima along a curve peak surrounding the
local maxima. The described depth criterion positions can be pixel
locations within the image 615. The depth criterion selected for
identification at block 630 preferably corresponds to the depth
criterion identified at block 540.
[0188] For example, feature extractor 450 can identify a first
pixel of the plurality of pixels corresponding to a first depth
criterion in the depth map and a second pixel of the plurality of
pixels corresponding to a second depth criterion in the depth map,
and in some embodiments each depth criterion can correspond to a
local maximum in a region of depth values around the identified
pixel. Block 630 provides a visual illustration of example depth
criterion 632 and 634 as local maxima, corresponding to the most
distant tissue within the left bronchus and right bronchus that is
visible by the imaging device 315. Specifically, depth criterion
634 corresponds to a pixel representing the most distant imaged
tissue within the right bronchus as it has a greater value than
depth criterion 632, and the depth criterion 632 corresponds a
pixel representing to the most distant imaged tissue within the
left bronchus. Other airway bifurcations can have similar known
depth relationships between different branches.
[0189] At block 640, the feature extractor 450 derives a
pre-identified feature from the identified depth criteria. For
example, as shown the feature extractor 450 can calculate the value
of the distance 645 (e.g., quantity of separation) between the
pixels corresponding to depth criteria 632 and 634. The distance
value can be represented as a number of pixels in an (x,y) space
corresponding to a two-dimensional depth map or an (x,y,z) vector
corresponding to the three-dimensional depth map 625, preferably in
the same format as the feature identified at block 550 of process
500. The feature extractor 450 can additionally or alternatively
derive the identification and positioning of the right and left
bronchus as the feature(s). In other implementations, for example,
involving depth maps at locations that view branchings of three or
more airways, the feature can include the size, shape, and
orientation of a polygon connecting three or more local maxima.
[0190] At block 650, the depth-based position estimator 410
calculates a correspondence between the feature(s) derived from the
imaging data and a number of features in depth features data
repository 405. For example, the feature derived from the imaging
data can be the value of distance 645 calculated based on the
identified depth criteria of the depth map 625, as described with
respect to block 640. The depth-based position estimator 410 can
compare the value of distance 645 to distance values associated
with a number of locations in the trachea to identify one of the
distance values that corresponds to the value of distance 645.
These distance values can be pre-computed and stored in data
repository 405 as described with respect to FIG. 20 above, or can
be computed in real-time as the patient's anatomy is being
navigated. Values computed in real time may be stored in a working
memory during correspondence calculations, or may be added to the
data repository 405 and then later accessed if the location
corresponding to the value is involved in additional correspondence
calculations.
[0191] To determine the correspondence, the depth-based position
estimator 410 can identify the value of distance 555 (discussed
above with respect to block 550 of process 500) as an exact match
to the value of distance 645, as the best match (e.g., closest
value) to the value of distance 645 among the options in the depth
features data repository 405, or as the first match within a
predetermined threshold of the value of distance 645. It will be
appreciated that the navigation system 400 can be preconfigured to
look for an exact match, best match, or first within-threshold
match, or to dynamically look for one of these options based on
current navigation conditions, based on a tradeoff between
computation speed and accuracy of the position output.
[0192] At block 660, the depth-based position estimator 410
determines an estimated pose of the distal end of the instrument
within the anatomical luminal network based on the virtual location
associated with the virtual feature that was identified in the
correspondence calculations of block 650. The pose can include the
position of the instrument (e.g., insertion depth within a segment
of an airway or other luminal network portion), the roll, pitch,
and/or yaw of the instrument, or other degrees of freedom. As
described above, the depth features data repository 405 can store a
database of tuples or associated values including locations and the
feature(s) extracted from virtual images generated at the
locations. Accordingly, at block 660 the depth-based position
estimator 410 can access the location information stored in
association with the feature identified at block 650 and output
this location as the position of the instrument. In some
embodiments, block 660 can include identifying an angular
transformation between the positions of the right and left bronchus
in image 615 and the virtual positions of the virtual right and
left bronchus in virtual depth map 532. The angular transformation
can be used to determine the roll of the instrument within the
airway.
[0193] At block 670, the depth-based position estimator 410 outputs
the identified pose for use in the navigation system 400. As
described above, the pose can be output to the state estimator 440
and used as an automatically-determined Bayesian prior during
initialization, as opposed to an initialization process that
requires the user to reposition the endoscope at a number of
specified locations in order to enable the initialization. In some
embodiments, the pose can be output to the registration calculator
465 for use in calculating a registration between the model
coordinate frame and the EM coordinate frame. Beneficially, the
processes 500 and 600 enable such calculations without requiring
the physician to deviate from a pre-determined navigation path
through the patient's airways to the target tissue site.
5. Alternatives
[0194] Several alternatives of the subject matter described herein
are provided below.
1. A method of facilitating navigation of an anatomical luminal
network of a patient, the method, executed by a set of one or more
computing devices, comprising: [0195] receiving imaging data
captured by an imaging device at a distal end of an instrument
positioned within the anatomical luminal network; [0196] accessing
a virtual feature derived from a virtual image simulated from a
viewpoint of a virtual imaging device positioned at a virtual
location within a virtual luminal network representative of the
anatomical luminal network; [0197] calculating a correspondence
between a feature derived from the imaging data and the virtual
feature derived from the virtual image; and [0198] determining a
pose of the distal end of the instrument within the anatomical
luminal network based on the virtual location associated with the
virtual feature. 2. The method of alternative 1, further comprising
generating a depth map based on the imaging data, wherein the
virtual feature is derived from a virtual depth map associated with
the virtual image, and wherein calculating the correspondence is
based at least partly on correlating one or more features of the
depth map and one or more features of the virtual depth map. 3. The
method of alternative 2, further comprising: [0199] generating the
depth map by calculating, for each pixel of a plurality of pixels
of the imaging data, a depth value representing an estimated
distance between the imaging device and a tissue surface within the
anatomical luminal network corresponding to the pixel; [0200]
identifying a first pixel of the plurality of pixels corresponding
to a first depth criterion in the depth map and a second pixel of
the plurality of pixels corresponding to a second depth criterion
in the depth map; [0201] calculating a first value representing a
distance between the first and second pixels; [0202] wherein the
virtual depth map comprises, for each virtual pixel of a plurality
of virtual pixels, a virtual depth value representing a virtual
distance between the virtual imaging device and a portion of the
virtual luminal network represented by the virtual pixel, and
wherein accessing the virtual feature derived from the virtual
image comprises accessing a second value representing a distance
between first and second depth criteria in the virtual depth map;
and [0203] calculating the correspondence based on comparing the
first value to the second value. 4. The method of alternative 3,
further comprising: [0204] accessing a plurality of values
representing distances between first and second depth criteria in a
plurality of virtual depth maps each representing a different one
of a plurality of virtual locations within the virtual luminal
network; and [0205] calculating the correspondence based on the
second value corresponding more closely to the first value than
other values of the plurality of values. 5. The method of any one
of alternatives 3 or 4, wherein the anatomical luminal network
comprises airways and the imaging data depicts a bifurcation of the
airways, the method further comprising: [0206] identifying one of
the first and second depth criteria as a right bronchus in each of
the depth map and the virtual depth map; and [0207] determining a
roll of the instrument based on an angular distance between a first
position of the right bronchus in the depth map and a second
position of the right bronchus in the virtual depth map, wherein
the pose of the distal end of the instrument within the anatomical
luminal network comprises the determined roll. 6. The method of any
of alternatives 2-5, further comprising: [0208] identifying three
or more depth criteria in each of the depth map and the virtual
depth map; [0209] determining a shape and location of a polygon
connecting the depth criteria in each of the depth map and the
virtual depth map; and [0210] calculating the correspondence based
on comparing the shape and location of the polygon of the depth map
to the shape and location of the polygon of the virtual depth map.
7. The method of any of alternatives 2-6, wherein generating the
depth map is based on photoclinometry. 8. The method of any of
alternatives 1-7, further comprising: [0211] calculating a
probabilistic state of the instrument within the anatomical luminal
network based on a plurality of inputs comprising the position; and
[0212] guiding navigation of the instrument through the anatomical
luminal network based at least partly on the probabilistic state.
9. The method of alternative 8, further comprising initializing a
navigation system configured to calculate the probabilistic state
and guide the navigation of the anatomical luminal network based on
the probabilistic state, wherein the initializing of the navigation
system comprises setting a prior of a probability calculator based
on the position. 10. The method of alternative 9, further
comprising: [0213] receiving additional data representing an
updated pose of the distal end of the instrument; [0214] setting a
likelihood function of the probability calculator based on the
additional data; and [0215] determining the probabilistic state
using the probability calculator based on the prior and the
likelihood function. 11. The method of any one of alternatives
8-10, further comprising: [0216] providing the plurality of inputs
to a navigation system configured to calculate the probabilistic
state, a first input comprising the pose of the distal end of the
instrument and at least one additional input comprising one or both
of robotic position data from a robotic system actuating movement
of the instrument and data received from a position sensor at the
distal end of the instrument; and [0217] calculating the
probabilistic state of the instrument based on the first input and
the at least one additional input. 12. The method of any one of
alternatives 1-11, further comprising determining a registration
between a coordinate frame of the virtual luminal network and a
coordinate frame of an electromagnetic field generated around the
anatomical luminal network based at least partly on the pose of the
distal end of the instrument within the anatomical luminal network
determined based on the calculated correspondence. 13. The method
of any one of alternatives 1-12, wherein determining the position
comprises determining a distance that the distal end of the
instrument is advanced within a segment of the anatomical luminal
network. 14. A system configured to facilitate navigation of an
anatomical luminal network of a patient, the system comprising:
[0218] an imaging device at a distal end of an instrument; [0219]
at least one computer-readable memory having stored thereon
executable instructions; and [0220] one or more processors in
communication with the at least one computer-readable memory and
configured to execute the instructions to cause the system to at
least: [0221] receive imaging data captured by the imaging device
with the distal end of the instrument positioned within the
anatomical luminal network; [0222] access a virtual feature derived
from a virtual image simulated from a viewpoint of a virtual
imaging device positioned at a virtual location within a virtual
luminal network representative of the anatomical luminal network;
[0223] calculate a correspondence between a feature derived from
the imaging data and the virtual feature derived from the virtual
image; and [0224] determine a pose of the distal end of the
instrument relative within the anatomical luminal network based on
the virtual location associated with the virtual feature. 15. The
system of alternative 14, wherein the one or more processors are
configured to execute the instructions to cause the system to at
least: [0225] generate a depth map based on the imaging data,
wherein the virtual image represents a virtual depth map; and
[0226] determine the correspondence based at least partly on
correlating one or more features of the depth map and one or more
features of the virtual depth map. 16. The system of alternative
15, wherein the one or more processors are configured to execute
the instructions to cause the system to at least: [0227] generate
the depth map by calculating, for each pixel of a plurality of
pixels of the imaging data, a depth value representing an estimated
distance between the imaging device and a tissue surface within the
anatomical luminal network corresponding to the pixel; [0228]
identify a first pixel of the plurality of pixels corresponding to
a first depth criterion in the depth map and a second pixel of the
plurality of pixels corresponding to a second depth criterion in
the depth map; [0229] calculate a first value representing a
distance between the first and second pixels; [0230] wherein the
virtual depth map comprises, for each virtual pixel of a plurality
of virtual pixels, a virtual depth value representing a virtual
distance between the virtual imaging device and a portion of the
virtual luminal network represented by the virtual pixel, and
wherein the feature derived from the virtual image comprises a
second value representing a distance between first and second depth
criteria in the virtual depth map; and [0231] determine the
correspondence based on comparing the first value to the second
value. 17. The system of alternative 16, wherein the one or more
processors are configured to execute the instructions to cause the
system to at least: [0232] access a plurality of values
representing distances between first and second depth criteria in a
plurality of virtual depth maps each representing a different one
of a plurality of virtual locations within the virtual luminal
network; and [0233] calculate the correspondence based on the
second value corresponding more closely to the first value than
other values of the plurality of values identify the second value
as a closest match to the first value among the plurality of
values. 18. The system of any one of alternatives 16-17, wherein
the anatomical luminal network comprises airways and the imaging
data depicts a bifurcation of the airways, wherein the one or more
processors are configured to execute the instructions to cause the
system to at least: [0234] identify one of the first and second
depth criteria as a right bronchus in each of the depth map and the
virtual depth map; and [0235] determine a roll of the instrument
based on an angular distance between a first position of the right
bronchus in the depth map and a second position of the right
bronchus in the virtual depth map, wherein the pose of the distal
end of the instrument within the anatomical luminal network
comprises the determined roll. 19. The system of any one of
alternatives 15-18, wherein the one or more processors are
configured to execute the instructions to cause the system to at
least: [0236] identify three or more depth criteria in each of the
depth map and the virtual depth map; [0237] determine a shape and
location of a polygon connecting the three or more depth criteria
in each of the depth map and the virtual depth map; and [0238]
calculate the correspondence based on comparing the shape and
location of the polygon of the depth map to the shape and location
of the polygon of the virtual depth map. 20. The system of any one
of alternatives 15-19, wherein the one or more processors are
configured to execute the instructions to cause the system to at
least generate the depth map based on photoclinometry. 21. The
system of any one of alternatives 14-20, wherein the one or more
processors are configured to communicate with a navigation system,
and wherein the one or more processors are configured to execute
the instructions to cause the system to at least: [0239] calculate
a probabilistic state of the instrument within the anatomical
luminal network using the navigation system based at least partly
on a plurality of inputs comprising the position; and [0240] guide
navigation of the instrument through the anatomical luminal network
based at least partly on the probabilistic state calculated by the
navigation system. 22. The system of alternative 21, further
comprising a robotic system configured to guide movements of the
instrument during the navigation. 23. The system of alternative 22,
wherein the plurality of inputs comprise robotic position data
received from the robotic system, and wherein the one or more
processors are configured to execute the instructions to cause the
system to at least calculate the probabilistic state of the
instrument using the navigation system based at least partly on the
position and on the robotic position data. 24. The system of any
one of alternatives 21-23, further comprising a position sensor at
the distal end of an instrument, the plurality of inputs comprise
data received from the position sensor, and wherein the one or more
processors are configured to execute the instructions to cause the
system to at least calculate the probabilistic state of the
instrument using the navigation system based at least partly on the
position and on the data received from the position sensor. 25. The
system of any one of alternatives 14-24, wherein the one or more
processors are configured to execute the instructions to cause the
system to at least determine a registration between a coordinate
frame of the virtual luminal network and a coordinate frame of an
electromagnetic field generated around the anatomical luminal
network based at least partly on the position. 26. A non-transitory
computer readable storage medium having stored thereon instructions
that, when executed, cause at least one computing device to at
least: [0241] access a virtual three-dimensional model of internal
surfaces of an anatomical luminal network of a patient; [0242]
identify a plurality of virtual locations within the virtual
three-dimensional model; [0243] for each virtual location of the
plurality of virtual locations within the virtual three-dimensional
model: [0244] generate a virtual depth map representing virtual
distances between a virtual imaging device positioned at the
virtual location and a portion of the internal surfaces within a
field of view of the virtual imaging device when positioned at the
virtual location, and [0245] derive at least one virtual feature
from the virtual depth map; and [0246] generate a database
associating the plurality of virtual locations with the at least
one virtual feature derived from the corresponding virtual depth
map. 27. The non-transitory computer readable storage medium of
alternative 26, wherein the instructions, when executed, cause the
at least one computing device to at least provide the database to a
navigation system configured to guide navigation of an instrument
through the anatomical luminal network during a medical procedure.
28. The non-transitory computer readable storage medium of
alternative 27, wherein the instructions, when executed, cause the
at least one computing device to at least: [0247] access data
representing an imaging device positioned at a distal end of the
instrument;
[0248] identify image capture parameters of the imaging device; and
[0249] set virtual image capture parameters of the virtual imaging
device to correspond to the image capture parameters of the imaging
device. 29. The non-transitory computer readable storage medium of
alternative 28, wherein the instructions, when executed, cause the
at least one computing device to at least generate the virtual
depth maps based on the virtual image capture parameters. 30. The
non-transitory computer readable storage medium of any one of
alternatives 28-29, wherein the image capture parameters comprise
one or more of field of view, lens distortion, focal length, and
brightness shading. 31. The non-transitory computer readable
storage medium of any one of alternatives 26-30, wherein the
instructions, when executed, cause the at least one computing
device to at least: [0250] for each virtual location of the
plurality of virtual locations: [0251] identify first and second
depth criteria in the virtual depth map, and [0252] calculate a
value representing a distance between the first and second depth
criteria; and [0253] create the database by associating the
plurality of virtual locations with the corresponding value. 32.
The non-transitory computer readable storage medium of any one of
alternatives 26-31, wherein the instructions, when executed, cause
the at least one computing device to at least: [0254] for each
virtual location of the plurality of virtual locations: [0255]
identify three or more depth criteria in the virtual depth map, and
[0256] determine a shape and location of a polygon connecting the
three or more depth criteria; and [0257] create the database by
associating the plurality of virtual locations with the shape and
location of the corresponding polygon. 33. The non-transitory
computer readable storage medium of any one of alternatives 26-32,
wherein the instructions, when executed, cause the at least one
computing device to at least: [0258] generate a three-dimensional
volume of data from a series of two-dimensional images representing
the anatomical luminal network of the patient; and [0259] form the
virtual three-dimensional model of the internal surfaces of the
anatomical luminal network from the three-dimensional volume of
data. 34. The non-transitory computer readable storage medium of
alternative 33, wherein the instructions, when executed, cause the
at least one computing device to at least control a computed
tomography imaging system to capture the series of two-dimensional
images. 35. The non-transitory computer readable storage medium of
any one of alternatives 33-34, wherein the instructions, when
executed, cause the at least one computing device to at least form
the virtual three-dimensional model by applying volume segmentation
to the three-dimensional volume of data. 36. A method of
facilitating navigation of an anatomical luminal network of a
patient, the method, executed by a set of one or more computing
devices, comprising: [0260] receiving a stereoscopic image set
representing an interior of the anatomical luminal network; [0261]
generating a depth map based on the stereoscopic image set; [0262]
accessing a virtual feature derived from a virtual image simulated
from a viewpoint of a virtual imaging device positioned at a
location within a virtual luminal network; [0263] calculating a
correspondence between a feature derived from the depth map and the
virtual feature derived from the virtual image; and [0264]
determining a pose of the distal end of the instrument within the
anatomical luminal network based on the virtual location of
associated with the virtual feature. 37. The method of alternative
36, wherein generating the stereoscopic image set comprises: [0265]
positioning an imaging device at a distal end of an instrument at a
first location within the anatomical luminal network; [0266]
capturing a first image of an interior of the anatomical luminal
network with the imaging device positioned at the first location;
[0267] robotically controlling the imaging device to move a known
distance to a second location within the anatomical luminal
network; and [0268] capturing a second image of the interior of the
anatomical luminal network with the imaging device positioned at
the second location. 38. The method of alternative 37, wherein
robotically controlling the imaging device to move the known
distance comprises one or both of retracting the imaging device and
angularly rolling the imaging device.
6. Implementing Systems and Terminology
[0269] Implementations disclosed herein provide systems, methods
and apparatus for improved navigation of luminal networks.
[0270] It should be noted that the terms "couple," "coupling,"
"coupled" or other variations of the word couple as used herein may
indicate either an indirect connection or a direct connection. For
example, if a first component is "coupled" to a second component,
the first component may be either indirectly connected to the
second component via another component or directly connected to the
second component.
[0271] The feature correspondence calculations, position
estimation, and robotic motion actuation functions described herein
may be stored as one or more instructions on a processor-readable
or computer-readable medium. The term "computer-readable medium"
refers to any available medium that can be accessed by a computer
or processor. By way of example, and not limitation, such a medium
may comprise RAM, ROM, EEPROM, flash memory, CD-ROM or other
optical disk storage, magnetic disk storage or other magnetic
storage devices, or any other medium that can be used to store
desired program code in the form of instructions or data structures
and that can be accessed by a computer. It should be noted that a
computer-readable medium may be tangible and non-transitory. As
used herein, the term "code" may refer to software, instructions,
code or data that is/are executable by a computing device or
processor.
[0272] The methods disclosed herein comprise one or more steps or
actions for achieving the described method. The method steps and/or
actions may be interchanged with one another without departing from
the scope of the claims. In other words, unless a specific order of
steps or actions is required for proper operation of the method
that is being described, the order and/or use of specific steps
and/or actions may be modified without departing from the scope of
the claims.
[0273] As used herein, the term "plurality" denotes two or more.
For example, a plurality of components indicates two or more
components. The term "determining" encompasses a wide variety of
actions and, therefore, "determining" can include calculating,
computing, processing, deriving, investigating, looking up (e.g.,
looking up in a table, a database or another data structure),
ascertaining and the like. Also, "determining" can include
receiving (e.g., receiving information), accessing (e.g., accessing
data in a memory) and the like. Also, "determining" can include
resolving, selecting, choosing, establishing and the like.
[0274] The phrase "based on" does not mean "based only on," unless
expressly specified otherwise. In other words, the phrase "based
on" describes both "based only on" and "based at least on."
[0275] The previous description of the disclosed implementations is
provided to enable any person skilled in the art to make or use the
present invention. Various modifications to these implementations
will be readily apparent to those skilled in the art, and the
generic principles defined herein may be applied to other
implementations without departing from the scope of the invention.
For example, it will be appreciated that one of ordinary skill in
the art will be able to employ a number corresponding alternative
and equivalent structural details, such as equivalent ways of
fastening, mounting, coupling, or engaging tool components,
equivalent mechanisms for producing particular actuation motions,
and equivalent mechanisms for delivering electrical energy. Thus,
the present invention is not intended to be limited to the
implementations shown herein but is to be accorded the widest scope
consistent with the principles and novel features disclosed
herein.
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