U.S. patent application number 11/786599 was filed with the patent office on 2007-08-30 for system and method for vascular border detection.
Invention is credited to Jon D. Klingensmith, Barry D. Kuban, Anuja Nair, D. Geoffrey Vince.
Application Number | 20070201736 11/786599 |
Document ID | / |
Family ID | 34915696 |
Filed Date | 2007-08-30 |
United States Patent
Application |
20070201736 |
Kind Code |
A1 |
Klingensmith; Jon D. ; et
al. |
August 30, 2007 |
System and method for vascular border detection
Abstract
The present invention uses a radio frequency (RF) signal
backscattered from vascular tissue to identify a border on a
vascular image. Embodiments of the invention operate in accordance
with a data gathering device connected to a computing device and a
transducer via a catheter. The transducer is used to gather RF data
backscattered from vascular tissue. The RF data is provided to the
computing device via the data-gathering device. In one embodiment
of the present invention, the computing device includes (i) a data
storage device for storing tissue types and related parameters and
(ii) an application. The application is used to convert the RF data
into the frequency domain and to identify associated parameters.
The parameters are compared to the parameters stored in the data
storage device to identify the corresponding tissue type. This
information is used, possibly with other border-related
information, to determine a border on a vascular image.
Inventors: |
Klingensmith; Jon D.;
(Shaker Heights, OH) ; Nair; Anuja; (Cleveland
Heights, OH) ; Kuban; Barry D.; (Avon Lake, OH)
; Vince; D. Geoffrey; (Avon Lake, OH) |
Correspondence
Address: |
O'MELVENY & MYERS LLP
610 NEWPORT CENTER DRIVE
17TH FLOOR
NEWPORT BEACH
CA
92660
US
|
Family ID: |
34915696 |
Appl. No.: |
11/786599 |
Filed: |
April 12, 2007 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
10837352 |
Apr 29, 2004 |
7215802 |
|
|
11786599 |
Apr 12, 2007 |
|
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|
60550620 |
Mar 4, 2004 |
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Current U.S.
Class: |
382/128 |
Current CPC
Class: |
A61B 5/02007 20130101;
A61B 8/12 20130101; A61B 8/0858 20130101 |
Class at
Publication: |
382/128 |
International
Class: |
G06K 9/00 20060101
G06K009/00 |
Claims
1. A method of identifying a border on an image of a vascular
object, comprising: acquiring RF data backscattered from vascular
tissue; transforming said RF data into the frequency domain;
identifying a plurality of parameters of said transformed RF data;
using said plurality of parameters and previously stored data to
identify at least a first and second portion of said transformed RF
data that corresponds to a blood and non-blood portion of said
vascular tissue, respectively; and determining said border on said
image of said vascular object in accordance with said first and
second portions of said transformed RF data.
2. The method of claim 1, wherein said second portion of said
vascular tissue corresponds to a plaque portion of said vascular
tissue.
3. The method of claim 1, wherein said step of determining said
border further comprises determining the luminal border on said
image of said vascular object in accordance with said first and
second portions of said transformed RF data.
4. The method of claim 3, wherein said steps of (i) using said
plurality of parameters and previously stored data to identify a
least a first and second portion of said transformed RF data and
(ii) determining said border further comprises: using said
plurality of parameters and previously stored data to identify at
least a third portion of said transformed RF data that corresponds
to at least a third tissue type of said vascular tissue, said third
tissue type being selected from a list of tissue types consisting
of medial tissue and adventitial tissue; and determining the
medial-adventitial border on said image of said vascular object in
accordance with said second and third portion of said transformed
RF data.
5. The method of claim 4, wherein said third tissue type
corresponds to both medial and adventitial tissues.
6. The method of claim 1, wherein said step of determining said
border further comprises determining said border on said image of
said vascular object in accordance with gradient information, said
gradient information being derived from said RF data.
7. The method of claim 1, further comprising the step of using
other-border data from at least one other image of said vascular
object to approximate said border on said image of said vascular
object.
8. The method of claim 7, wherein said step of determining said
border further comprises determining said border on said image of
said vascular object in accordance with gradient information, said
gradient information being derived from said RF data.
9. The method of claim 6, wherein said gradient information further
comprises gradient-force data and gradient-border data.
10. The method of claim 1, further comprising the step of filtering
said first and second portions of said transformed RF data before
they are used to determine said border.
11. The method of claim 10, wherein said step of filtering further
comprises filtering said second portion of said transformed RF data
to reduce the amount of non-blood particles visible in an image of
said second portion of said transformed RF data.
12. The method of claim 11, wherein said step of filtering further
comprises filtering said first portion of said transformed RF data
to reduce the number of tissue types visible in an image of said
first portion of said transformed RF data.
13. The method of claim 1, wherein said step of determining said
border further comprises determining said border on said image of
said vascular object in accordance with spectral-force data and
spectral-border data, said spectral-force data and said
spectral-border data being derived from said transformed RF
data.
14. The method of claim 1, wherein said step of determining said
border further comprises determining said border on said image of
said vascular object in accordance with at least one algorithm,
said at least one algorithm being selected from a list of
algorithms consisting of a continuity algorithm, a curvature
algorithm, and a relatedness algorithm.
15. The method of claim 1, wherein said step of transforming said
RF data further comprises transforming said RF data into the
autoregressive (AR) frequency power spectrum.
16. The method of claim 1, wherein said step of identifying a
plurality of parameters further comprises identifying at least one
parameter of said transformed RF data, said at least one parameter
being selected from a list of parameters consisting of maximum
power, minimum power, frequency at maximum power, frequency at
minimum power, y intercept, slope, mid-band fit, and integrated
backscatter.
17. The method of claim 1, wherein said step of acquiring RF data
further comprises acquiring RF data that is both backscattered from
said vascular tissue and gated to electrocardiogram (ECG)
information.
18. The method of claim 1, wherein said step of identifying a
plurality of parameters of said transformed RF data further
comprises identifying at least one parameter of said RF data.
19. The method of claim 18, wherein said at least one parameter of
said RF data comprises tissue depth.
20. A method of identifying at least one boundary on a vascular
image, comprising: acquiring RF data backscattered from vascular
tissue; transforming said RF data into the frequency domain;
identifying at least one parameter of said transformed RF data;
using said at least one parameter and previously stored data to
identify at least one tissue type and transformed RF data
corresponding thereto (corresponding RF data); using said RF data
to determine gradient information pertaining to said at least one
boundary on said vascular image; and using at least said gradient
information and said corresponding RF data to determine said at
least one boundary on said vascular image.
21. The method of claim 20, wherein said step of using at least
said gradient information and said corresponding RF data further
comprises using at least said gradient information and said
transformed RF data to determine said at least one boundary on said
vascular image.
22. The method of claim 20, wherein said at least one parameter is
selected from a list of parameters consisting of maximum power,
minimum power, frequency at maximum power, frequency at minimum
power, y intercept, slope, mid-band fit, and integrated
backscatter.
23. The method of claim 20, wherein said step of using said
gradient information and said corresponding RF data further
comprises using other-boundary data to determine said at least one
boundary on said vascular image.
24. The method of claim 23, further comprising the step of using
said corresponding. RF data to determine spectral-force data and
spectral-boundary data, wherein at least said spectral-force data,
said spectral-boundary data, and said gradient information are used
to determine said at least one boundary on said vascular image.
25. The method of claim 23, further comprising the step of using
said gradient information to determine gradient-force data and
gradient-border data, wherein at least said gradient-force data,
said gradient-boundary data and corresponding RF data are used to
determine said at least one boundary on said vascular image.
26. The method of claim 24, further comprising the step of using
said gradient information to determine gradient-force data and
gradient-border data, wherein at least said gradient-force data,
said spectral-force data, said gradient-border data, and said
spectral-border data are used to determine said at least one
boundary on said vascular image.
27. The method of claim 24, further comprising the step of
filtering said corresponding RF data before it is used to determine
said spectral-force data and said spectral-boundary data.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation of U.S. patent
application Ser. No. 10/837,352, filed Apr. 29, 2004, which claimed
the benefit pursuant to 35 U.S.C. .sctn. 119(e) of U.S. Provisional
Patent Application No. 60/550,620, filed Mar. 4, 2004, all of which
are hereby expressly incorporated by reference in their entirety
for all purposes.
BACKGROUND OF THE INVENTION
[0002] 1. Field of the Invention
[0003] The present invention relates to vascular images, or more
particularly, to a system and method of using the frequency
spectrum of a radio frequency (RF) signal backscattered from
vascular tissue to identify at least one border on a corresponding
vascular image.
[0004] 2. Description of Related Art
[0005] The present invention relates to medical imaging arts. It
finds particular application to a system and method of identifying
a border on a vascular image (e.g., intra-vascular ultrasound
(IVUS) image, Virtual Histology.TM. (VH) image, etc.). It should be
appreciated that while the present invention is described in terms
of identifying a luminal and medial-adventitial border on an IVUS
or VH image, the present invention is not so limited. Thus, for
example, identifying any border (or boundary) on any vascular image
is within the spirit and scope of the present invention.
[0006] Ultrasonic imaging of portions of a patient's body provides
a useful tool in various areas of medical practice for determining
the best type and course of treatment. Imaging of the coronary
vessels of a patient by ultrasonic techniques can provide
physicians with valuable information. For example, the image data
may show the extent of a stenosis in a patient, reveal progression
of disease, help determine whether procedures such as angioplasty
or atherectomy are indicated or whether more invasive procedures
may be warranted.
[0007] In a typical ultrasound imaging system, an ultrasonic
transducer is attached to the end of a catheter that is carefully
maneuvered through a patient's body to a point of interest such as
within a blood vessel. The transducer may be a single-element
crystal or probe that is mechanically scanned or rotated back and
forth to cover a sector over a selected angular range. Acoustic
signals are then transmitted and echoes (or backscatter) from these
acoustic signals are received. The backscatter data can be used to
identify the type of a scanned tissue. As the probe is swept
through the sector, many acoustic lines are processed building up a
sector-shaped image of the patient. After the data is collected, an
image of the blood vessel (e.g., an IVUS image) can be
reconstructed using well-known techniques. This image is then
visually analyzed by a cardiologist to assess the vessel components
and plaque content.
[0008] A typical analysis includes determining the size of the
lumen and amount of plaque in the vessel. This is performed by
generating an image of the vessel (e.g., an IVUS image) and
manually drawing contoured boundaries on the image where the
clinician believes the luminal and the medial-adventitial borders
are located. In other words, the luminal border, which demarcates
the blood-intima interface, and the medial-adventitial border,
which demarcates the external elastic membrane or the boundary
between the media and the adventitia, are manually drawn to
identify the plaque-media complex that is located there between.
This is a very time consuming process. Furthermore, this process is
made more difficult when multiple images are being analyzed (e.g.,
to recreate a 3D vascular image, etc.) or the images are of poor
quality (e.g., making the boundaries more difficult to see). Thus,
it would be advantageous to have a system and method of identifying
a border on a vascular image that overcomes at least one of these
drawbacks.
SUMMARY OF THE INVENTION
[0009] The present invention provides a system and method of using
the frequency spectrum of a radio frequency (RF) signal
backscattered from vascular tissue to identify at least one border
on a vascular image. Embodiments of the present invention operate
in accordance with a data gathering device (e.g., an intra-vascular
ultrasound (IVUS) device, etc.) electrically connected to a
computing device and a transducer via a catheter. The transducer is
inserted into a blood vessel of a patient and used to gather radio
frequency (RF) data backscattered from vascular tissue. The RF data
is then provided to (or acquired by) the computing device via the
data-gathering device.
[0010] In a preferred embodiment of the present invention, the
computing device includes at least one data storage device (e.g.,
database, memory, etc.) and at least one application (e.g., a
characterization application, a gradient-border application, a
frequency-border application and/or an active-contour application).
A data storage device is used (at least primarily) to store a
plurality of tissue types and related parameters. Preferably, the
information is stored so that each tissue type is linked to at
least one corresponding parameter.
[0011] In one embodiment of the present invention, the RF data
(which is typically in the time domain) is provided to the
characterization application, where it is converted (or
transformed) into the frequency domain. The characterization
application is then used to identify a plurality of parameters
associated with the transformed RF data (or a portion thereof. The
identified parameters are then compared to the parameters stored in
the data storage device to identify the corresponding tissue type
(or the type of tissue that backscattered the analyzed RF data).
Such a process can be used, for example, to identify portions of RF
data (or sets thereof) that are related to at least border-related
tissue types (e.g., medial, adventitial, plaque, blood, etc.).
[0012] In another embodiment of the present invention, the
characterization application is further used to identify parameters
from the RF data (which is typically in the time domain).
Parameters associated with the RF data (or a portion thereof can be
used, for example, to spatially identify certain frequencies (or
parameters related thereto). Thus, for example, if a vascular wall
comprises multiple tissue layers, corresponding RF data can be used
to identify the location of these tissues and the related frequency
spectrum can be used to identify tissue types.
[0013] The identified tissue types and corresponding RF data (or
transformations thereof (i.e., identified information) are provided
to the active-contour application. The data is then used to
identify at least one border on an image of a vascular object
(e.g., intra-vascular ultrasound (IVUS) image, Virtual
Histology.TM. (VH) image, etc.). For example, RF data corresponding
to blood and plaque tissue can be used to identify (or
substantially approximate) the luminal border on a vascular image.
Similarly, RF data corresponding to plaque, medial and/or
adventitial tissue can be used to identify (or substantially
approximate) the medial-adventitial border on a vascular image.
[0014] In another embodiment of the present invention, the
computing device further includes a frequency-border application.
In this embodiment, the characterization application is adapted to
provide the identified information to the frequency-border
application, where it is used to determine spectral information.
The spectral information is then provided to the active-contour
application and used to determine at least one border on a vascular
image (i.e., image-border data). In one embodiment of the present
invention, the spectral information comprises spectral-force data,
or data representing a frequency-based force that is applicable to
(or a component on the image-border data. In another embodiment of
the present invention, the spectral-information comprises
spectral-border data, or data representing an estimation of at
least one border on a vascular image.
[0015] In one embodiment of the present invention, the computing
device further includes a gradient-border application. The
gradient-border application is adapted to use the acquired RF data
to determine gradient information, which can then be used to
identify a border or boundary. This is because a change in pixel
color (e.g., light-to-dark, dark-to-light, shade1-to-shade2, etc)
can indicate the presence of a border. In another embodiment of the
present invention, the gradient information comprises
gradient-force data, or data that represents a gradient-based force
that is applicable to (or a component of) the image-border data. In
another embodiment of the present invention, the gradient
information comprises gradient-border data, or data that represents
an estimation of at least one border on a vascular image (e.g., the
IVUS image, a VH image, etc.). The gradient-border data can be
used, for example, either alone or together with other
border-related information (e.g., spectral information, etc.), to
determine at least one border on a vascular image.
[0016] In another embodiment of the present invention, the
active-contour application is further adapted to use other-border
information to determine a border on a vascular image. In other
words, information related to at least one border on another
image(s) (e.g., a previous image, a subsequent image, multiple
images, etc.) is used to determine (or approximate) at least one
border on the vascular image at issue (i.e., the current vascular
image). In another embodiment of the present invention, the
active-contour application can be used to adjust the border to more
closely match the actual border of the vascular object. This is
done by considering, or taking into account continuity data,
curvature data, and/or relatedness data.
[0017] In other embodiments of the present invention, the
frequency-border application is further adapted to filter the
transformed RF data before it is used to generate spectral
information and the gradient-border application is further adapted
to process the acquired RF data using traditional IVUS imaging
techniques. A more complete understanding of the system and method
of identifying a border on an IVUS image will be afforded to those
skilled in the art, as well as a realization of additional
advantages and objects thereof, by a consideration of the following
detailed description of the preferred embodiment. Reference will be
made to the appended sheets of drawings which will first be
described briefly.
BRIEF DESCRIPTION OF THE DRAWINGS
[0018] FIG. 1 illustrates a vascular-border-identification system
in accordance with one embodiment of the present invention.
[0019] FIG. 2 illustrates an exemplary intra-vascular ultrasound
(IVUS) image.
[0020] FIG. 3 illustrates a plurality of borders that are common to
IVUS images.
[0021] FIG. 4 illustrates a plurality of control points on one
border of an IVUS image.
[0022] FIG. 5 illustrates how a plurality of 2D vascular images can
be used to generate a 3D vascular image.
[0023] FIG. 6 illustrates how the control points from the image
depicted in FIG. 4 can be extrapolated onto another image.
[0024] FIG. 7 illustrates a vascular image including a luminal
boundary, a medial-adventitial boundary, and a plaque component
located therebetween.
[0025] FIG. 8 illustrates a method of identifying a border of a
vascular object in accordance with one embodiment of the present
invention.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT
[0026] The present invention provides a system and method of using
the frequency spectrum of a radio frequency (RF) signal
backscattered from vascular tissue to identify at least one border
on a vascular image. In the detailed description that follows, like
element numerals are used to describe like elements illustrated in
one or more figures.
[0027] Embodiments of the present invention operate in accordance
with a data-gathering device and a computing device electrically
connected thereto. FIG. 1 illustrates a
vascular-border-identification system 10 in accordance with one
embodiment of the present invention. Specifically, a data-gathering
console 200 is electrically connected to a computing device 100 and
a transducer 220 via a catheter 210. The transducer 220 is inserted
into a blood vessel of a patient (not shown) and used to gather
radio frequency (RF) data backscattered from vascular tissue. The
RF data is then provided to (or acquired by) the data-gathering
device 200, where it is used (or can be used) to produce an image
of the vessel (e.g., intra-vascular ultrasound (IVUS) image,
etc.).
[0028] More particularly, RF data is typically gathered in
segments, either through a rotating transducer or an array of
circumferentially positioned transducers, where each segment
represents an angular portion of the resultant image. Thus, it
takes a plurality of segments (or a set of RF data) to image an
entire cross-section of a vascular object. Furthermore, multiple
sets of RF data are typically gathered from multiple locations
within a vascular object (e.g., by moving the transducer linearly
through the vessel). These multiple sets of data can then be used
to create a plurality of two-dimensional (2D) images or one
three-dimensional (3D) image. It should be appreciated that the
data-gathering device 200 includes, but is not limited to, an IVUS
console, thermographic device, optical device (e.g., an optical
coherence tomography (OCT) console), MRI device, or any vascular
imaging device generally known to those skilled in the art. It
should further be appreciated that the computing device 100
depicted in FIG. 1 includes, but its not limited to, a personal
computer or any other data-processing device (general purpose or
application specific) that is generally known to those skilled in
the art.
[0029] The RF data (or multiple sets thereof is provided to (or
acquired by) the computing device 100. In one embodiment of the
present invention, the computing device 100 includes at least one
data storage device (e.g., database 130, memory 150) and a
plurality of applications (e.g., a characterization application
110, a gradient-border application 120, a frequency-border
application 140 and/or an active-contour application 160). In a
preferred embodiment of the present application, the RF data
provided to (or acquire by) the computing device 100 is gated to
electrocardiogram (ECG) information. The concept of gating RF data
is in discussed in detail in U.S. patent application Ser. No.
10/647,977, which was filed Aug. 25, 2003, and is incorporated
herein, in its entirety, by reference.
[0030] In a first embodiment of the present invention, a plurality
of tissue types (e.g., medial, adventitial, plaque, blood, etc.)
and related parameters are stored in a database 130. Preferably,
the information is stored so that each tissue type is linked to its
corresponding parameters. By doing so, each tissue type can be
identified (or defined) by the parameters that are linked thereto.
It should be appreciated that the term parameter, as that term is
used herein, includes, but is not limited to maximum power, minimum
power, frequencies at maximum and/or minimum power, y intercepts
(estimated or actual), slope, mid-band fit, integrated backscatter,
tissue depth, and all parameters (either time or frequency based)
generally known to (or discernable by) those skilled in the art. It
should further be appreciated that the methods used to acquire the
parameters or their relationship to the tissue types (e.g., using
scientific theory, experimentation, computer simulation, etc.) are
not a limitation of the present invention. Is should also be
appreciated that the term tissue type, as that term is used herein,
includes, but is not limited to, blood tissue, plaque tissue (e.g.,
calcified tissues, fibrous tissues, calcified-necrotic tissues and
fibro-lipidic tissues), medial tissue, adventitial tissue, and all
other vascular tissues, or combinations thereof (e.g.,
medial-adventitial tissue), generally known to those skilled in the
art. It should also be appreciated that the data storage devices
depicted herein (e.g., database 130, memory 150) include, but are
not limited to, RAM, cache memory, flash memory, magnetic disks,
optical disks, removable disks, SCSI disks, IDE hard drives, tape
drives and all other types of data storage devices (and
combinations thereof, such as RAID devices) generally known to
those skilled in the art.
[0031] As shown in FIG. 1, the RF data (which is typically in the
time domain) is provided to the characterization application 110,
where it is converted (or transformed) into the frequency domain.
The characterization application 110 is then used to identify a
plurality of parameters associated with the transformed RF data (or
a portion thereof. The identified parameters are then compared to
the parameters stored in the database 130 to identify the
corresponding tissue type (or the type of tissue that backscattered
the analyzed RF data). Such a process can be used (e.g., once or
repeatedly) to identify the portions of RF data (or sets thereof)
that are associated with each stored tissue type (e.g., medial,
adventitial, plaque, blood, etc.).
[0032] It should be appreciated that the frequency conversion (or
transformation) discussed herein includes, but is not limited to,
the use of a fast Fourier transformation (FFT), the Welch
periodogram, autoregressive power spectrum (AR) analysis, or any
other frequency transformation or spectral analysis generally known
to those skilled in the art. It should further be appreciated that
the RF data may either be received in real-time (e.g., while the
patient is in the operating room) or after a period of delay (e.g.,
via CD-ROM, etc.). It should also be appreciated that the
identified parameters should be related (generally) to the stored
parameters. Thus, for example, an estimated Y intercept parameter
should be identified if data related to a signal's estimated Y
intercept is stored in the database 130 and linked to at least one
tissue type.
[0033] In a second embodiment of the present invention, the
characterization application 110 is further used to identify
parameters from the RF data (which is typically in the time
domain). Parameters associated with the RF data (or a portion
thereof can be used, for example, to spatially identify certain
frequencies (or parameters related thereto). Thus, for example, if
a vascular wall comprises multiple tissue layers, corresponding RF
data can be used to identify the location of these tissues and the
related frequency spectrum can be used to identify tissue types.
The use of parameters to identify tissue types is discussed in
detail in U.S. Pat. No. 6,200,268, which was issued on Mar. 13,
2001, and U.S. patent application Ser. No. 10/647,971, which was
filed on Aug. 25, 2003, and are incorporated herein, in their
entireties, by reference.
[0034] In either embodiment of the present invention, the RF data
(or a transformation thereof and the identified tissue types
(including the association therebetween) are provided to the
active-contour application 160. This data is then used to identify
at least one border on an image of a vascular object (e.g.,
intra-vascular ultrasound (IVUS) image, Virtual Histology.TM., (VH)
image, etc.). For example, RF data corresponding to blood and
plaque tissue can be used to identify (or substantially
approximate) the luminal border on a vascular image. Similarly, RF
data corresponding to plaque, medial, and/or adventitial tissue can
be used to identify (or substantially approximate) the
medial-adventitial border on a vascular image.
[0035] It should be appreciated that the present invention is not
limited to the identification of any particular border (or
boundary) on a vascular image, and includes all borders generally
known to those skilled in the art. It should further be appreciated
that the applications depicted and discussed herein (e.g.,
characterization application 110, gradient-border application 120,
frequency-border application 140, active-contour application 160),
may exist as a single application or as multiple applications,
locally and/or remotely stored. It should also be appreciated that
the number and location of the components depicted in FIG. 1 are
not intended to limit the present invention, and are merely
provided to illustrate the environment in which the present
invention may operate. Thus, for example, a computing device having
a single data storage device and/or a remotely located
characterization application (either in part or in whole) is within
the spirit-and scope of the present invention.
[0036] In another embodiment of the present invention, the
computing device 100 further includes a frequency-border
application 140. In this embodiment, the characterization
application 110 is adapted to provide the RF data (or a
transformation thereof and the identified tissue types (including
the association therebetween) to the frequency-border application
140, where it is used to determine spectral information. The
spectral information is then provided to the active-contour
application 160 and used to determine at least one border on a
vascular image (i.e., image-border data).
[0037] In one embodiment of the present invention, the spectral
information comprises spectral-force data. Spectral-force data is
based (either directly or indirectly) on the information provided
by the characterization application 110 and represents a
frequency-based force that is applicable to (or a component of the
image-border data. The spectral-force data can be used, for
example, to determine or refine the image-border data (e.g., as
determined by the active-contour application 160). In other words,
spectral-force data can be used together with other border-related
information to determine a border on a vascular image, or applied
to a border determined using border-related information (e.g., as
an external force). Thus, the resultant border is based, at least
in part, on the spectral-force data.
[0038] In one embodiment of the present invention, spectral-force
data can be analogized to a gravitation field, in which the force
(or field) is used to attract a border in a given direction, or to
have a given shape. In this embodiment, the strength of the force
(or gravitational pull) is directly proportional to the relatedness
of the corresponding RF data to a border-related tissue
type(s).
[0039] In another embodiment of the present invention, the spectral
information comprises spectral-border data. Spectral-border data is
based (either directly or indirectly) on the information provided
by the characterization application 110 and represents an
estimation of at least one border on a vascular image. The
spectral-border data can be used, for example, either alone or
together with other border-related information, to determine
image-border data (e.g., as determined by the active-contour
application 160). It should be appreciated that the term spectral
information, as that term is used herein, is not limited to
spectral-force data and/or spectral-border data, but further
includes any data resulting from a spectral analysis of the
acquired RF data.
[0040] In another embodiment of the present invention, the
frequency-border application 140 is further adapted to filter the
transformed RF data before it is used to generate spectral
information. Specifically, in one embodiment of the present
invention, a portion of the vascular object is selected. The
corresponding transformed RF data and the information stored in the
database 130 are then used to identify the tissue types that are
related thereto. The minority tissue type is then filtered out.
This can be accomplished, for example, by reassigning the RF data
associated with the minority tissue type, so that it is associated
with the majority tissue types. For example, if a first portion (or
ninety-five percent) of the corresponding RF data is associate with
blood tissue and a second portion (or five percent) of the
corresponding RF data is associated with plaque tissue, then the
second portion would be reassigned so that it is associate with
blood tissue (i.e., making a homogenous composition). It should be
appreciated, however, that the present invention is not limited to
such a filtering algorithm, and includes any other filtering
algorithms generally known to those skilled in the art.
[0041] Referring back to FIG. 1, in one embodiment of the present
invention, the computing device 100 further includes a
gradient-border application 120. The gradient-border application
120 is adapted to use the acquired RF data to determine gradient
information, which can then be used to identify a border or
boundary (e.g., used by the gradient-border application 120 to
estimate at least one border, provided to the active-contour
application and used together with spectral information to
determine image-border data, etc.). This is because a change in
pixel color (e.g., light-to-dark, dark-to-light, shade1-to-shade2,
etc) can indicate the presence of a border.
[0042] For example, FIG. 2 illustrates an exemplary IVUS image 20
of a vascular object. Starting from the center and working outward,
the catheter can be identified by the first light-to-dark
transition (or gradient). The catheter border is further identified
in FIG. 3 (i.e., 330). Referring back to FIG. 2, and continuing
outward, the next dark-to-light transition (or gradient) identifies
the luminal border (see FIG. 3, 320). The medial-adventitial border
can then be identified by going outward from the luminal border
until the next dark-to-light transition (or gradient) is found (see
FIG. 3, 310). It should be appreciated that because the IVUS image
is constructed using gray-scales, it may be necessary to utilize an
algorithm and/or at least one threshold value to identify precisely
where the image changes from light to dark (or vice versa).
However, it should further be appreciated that the present
invention is not limited to any particular algorithm for
identifying the aforementioned transitions, and includes all
algorithms (and/or threshold values) generally known to those
skilled in the art.
[0043] In another embodiment of the present invention, the
gradient-border application 120 is further adapted to process the
acquired RF data using traditional IVUS imaging techniques. For
example, the gradient-border application 120 may be adapted to
filter the RF data (e.g., using a highpass filter, etc.), detect
relevant portions (e.g., envelope detection, etc.), and/or modulate
any portion thereof (e.g., smoothing, log compressing, etc.). Such
techniques are all well known to those skilled in the art. The
resultant data can then be used to produce an IVUS image or
determine gradient information.
[0044] In one embodiment of the present invention, the gradient
information comprises gradient-force data. Gradient-force data is
based (either directly or indirectly) on the gradients in an IVUS
image and represents a gradient-based force that is applicable to
(or a component on the image-border data. The gradient-force data
can be used, for example, to determine or refine the image-border
data (e.g., as determined by the active-contour application 160).
In other words, gradient-force data can be used together with other
border-related information to determine a border on a vascular
image, or applied to a border determined using border-related
information (e.g., as an external force). Thus, the resultant
border is based, at least in part, on the gradient-force data.
[0045] In one embodiment of the present invention, gradient-force
data can be analogized to a gravitation field, in which the force
(or field) is used to attract a border in a given direction, or to
have a given shape. In this embodiment, the strength of the force
(or gravitational pull), is directly proportional to the gradients
in the IVUS image (or transitions therein).
[0046] In another embodiment of the present invention, the gradient
information comprises gradient-border data. Gradient-border data is
based (either directly or indirectly) on the gradients in an IVUS
image and represents an estimation of at least one border on a
vascular image (e.g., the IVUS image, a VH image, etc.). The
gradient-border data can be used, for example, either alone or
together with other border-related information (e.g., spectral
information, etc.), to determine at least one border on a vascular
image. It should be appreciated that the term gradient information,
as that term is used herein, is not limited to gradient-force data
and/or gradient-border data, but further includes any data
resulting from an analysis of the gradients in an IVUS image.
[0047] In another embodiment of the present invention, the
active-contour application 160 is further adapted to use
other-border information to determine a border on a vascular image.
In other words, information related to a border on another image(s)
(e.g., a previous image, a subsequent image, multiple images, etc.)
is used to determine (or approximate) at least one border on the
vascular image at issue (i.e., the current vascular image).
[0048] For example, in one embodiment of the present invention, the
border-detection algorithm 160 is adapted to identify at least one
control point on the border of another image. With reference to
FIGS. 3 and 4, the border-detection algorithm can be used, for
example, to identify a plurality of control points 22 on the
luminal border 320. It should be appreciated that the location and
number of control points depicted in FIG. 4 are not intended to
limit the present invention, and are merely provided to illustrate
the environment in which the present invention may operate. In an
alternate embodiment, the active-contour application 160 is adapted
to identify a border using user-identified control points. Such an
embodiment is discussed in detail in U.S. Pat. No. 6,381,350, which
was issued on Apr. 30, 2002, and is incorporated herein, in its
entirety, by reference.
[0049] With reference to FIG. 1, once the border and control points
are identified on another image, the active-contour application 160
can be used to identify at least one control point on the current
vascular image. In one embodiment of the present invention, this is
done by extrapolating the previously identified control points to
the current vascular image. By doing this, multiple 2D images (or
at least one 3D image) can be produced. For example, as illustrated
in FIG. 5, multiple 2D images (e.g., 20, 52a-52d, etc.) are used to
produce a 3D image of a tubular (e.g., vascular) object 50. It
should be appreciated that a memory device 150 (see FIG. 1) can be
used to store information related to this embodiment (e.g., a
border on another image, control points on such a border,
extrapolated control points, resulting image-border data,
etc.).
[0050] FIG. 6 illustrates one method as to how an identified
control point can be extrapolated to the current vascular image.
Specifically, the control points that were illustrated in FIG. 4
(i.e., 22) are extrapolated (or copied) to the current image (e.g.,
52d), thus creating a second set of control points 62. In one
embodiment of the present invention, the control points are
extrapolated using Cartesian coordinates. It should be appreciated
that, while FIG. 6 illustrates control points being extrapolated to
an adjacent image, the present invention is not so limited. Thus,
extracting control points to (or from) additional images (e.g.,
52c, 52b, etc.) is within the spirit and scope of the present
invention.
[0051] Once the control points are extrapolated, the active-contour
application 160 is further adapted to identify (or approximate) a
border based on the extrapolated points. For example, as shown in
FIG. 6, the extrapolated points 62 may be connected using a
plurality of lines 64, where the lines are either straight or
curved (not shown). In another embodiment of the present invention,
the extrapolating application is adapted to use an algorithm (e.g.,
a cubic-interpolation algorithm, etc.) to identify line shape.
Border extrapolation is discussed in detail in U.S. patent
application Ser. No. 10/647,473, which was filed Aug. 26, 2003, and
is incorporated herein, in its entirety, by reference.
[0052] Referring back to FIG. 1, the active-contour application 160
is then used to adjust the border to more closely match the actual
border of the vascular object. In doing so, the active-contour
application 160 may consider, or take into account, at least (i)
the proximity of the border to each extrapolated point (i.e.,
continuity or control-point factor), (ii) border curvature or
smoothness (i.e., curvature or boundary factor), and/or (iii) the
relationship between multiple borders (i.e., relatedness factor).
For example, by considering a continuity or control-point factor,
the border can be adjusted so that it passes through each
extrapolated point. By considering a curvature or boundary factor,
the border can be adjusted to prevent sharp transitions (e.g.,
corners, etc.). Furthermore, by considering a relatedness factor,
multiple borders can be adjusted in relation to one another (e.g.,
the luminal border can always be located inside the
medial-adventitial border, etc.).
[0053] In one embodiment of the present invention, these three
factors are also used to determine related borders on adjacent
images. It should be appreciated that if multiple factors are being
considered, then individual factors may be weighted more heavily
than others. This becomes important if the factors produce
different results. It should further be appreciated that the
present invention is not limited to the use of the aforementioned
factors for border optimization, and that the use of additional
factors to adjust (or optimize) a border is within the spirit and
scope of the present invention.
[0054] In one embodiment of the present invention, the adjusted
borders are configured to be manually manipulated. In other words,
at least one point on the border can be selected and manually moved
to a new location. The active-contour application 160 is then used
(as previously discussed) to reconstruct the border accordingly. In
another embodiment of the present invention, the active-contour
application is further adapted to adjust related borders in
adjacent images. This is done by fitting a geometrical model (e.g.,
a tensor product B-spline, etc.) over the surface of a plurality of
related borders (e.g., as identified on multiple IVUS images). A
plurality of points on the geometrical model are then parameterized
and formulated into a constrained least-squares system of
equations. If a point on the border is manually moved, the
active-contour application can utilize these equations to calculate
a resulting surface (or mesh of control points). The affected
borders (e.g., adjacent borders) can then be adjusted
accordingly.
[0055] Once the border has been sufficiently adjusted, the
aforementioned process can be repeated to identify additional
borders. In an alternate embodiment of the present invention,
multiple borders (e.g., luminal and medial-adventitial borders) are
identified concurrently. In other words, the active-contour
application 160, for example, can either be used (e.g., initiated)
to identify a single border on a vascular image, thus requiring a
subsequent use to identify another border on the vascular image, or
used (e.g., initiated) to identify multiple borders on a vascular
image. In the latter, the multiple borders are identified at
substantially the same time. The multiple borders can then be
imaged (in either 2D or 3D) and analyzed by either a skilled
practitioner or a computer algorithm. For example, as illustrated
in FIG. 7, the luminal border 74 and the medial-adventitial border
76 can be used (by either a clinician or an algorithm) to identify
the plaque-media complex 78 of a vascular object.
[0056] One method of identifying a border on a vascular image is
illustrated in FIG. 8. Specifically, at step 800, RF data
backscattered from vascular tissue is acquired. The RF data is then
(i) transformed from the time domain into the frequency domain and
(ii) processed using traditional IVUS imaging techniques, at steps
810 and 820, respectively. With respect to the transformed RF data,
a plurality of parameters are identified at step 812, and compared
to stored parameters, at step 814, to identify a plurality of
tissue types. At step 816; RF data (or a transformation thereof)
that corresponds to at least relevant (e.g., border-related) tissue
types is identified. The corresponding RF data is then used to
determine spectral information (e.g., spectral-force data,
spectral-border data, etc.) at step 818.
[0057] With respect to the IVUS data, gradients are used to
determine gradient information (e.g., gradient-force data,
gradient-border data, etc.) at step 822. At step 824, other-border
data (e.g., data related to at least one border on at least one
other image) is used to approximate at least one border on the
current vascular image. At step 826, the gradient information and
spectral information are used to modify the at least one border on
the current vascular image, ending the method at step 828. It
should be appreciated that the present invention is not limited to
the method illustrated in FIG. 8, and further includes all methods
of operation generally described or suggested herein or discernable
by one skilled in art. For example, a method that uses only
spectral information to determine at least one border on a vascular
image is within the spirit and scope of the present invention.
[0058] Having thus described preferred embodiments of a system and
method of identifying a border on a vascular image, it should be
apparent to those skilled in the art that certain advantages of the
system have been achieved. It should also be appreciated that
various modifications, adaptations, and alternative embodiments
thereof may be made within the scope and spirit of the present
invention. The invention is further defined by the following
claims.
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