U.S. patent application number 12/963896 was filed with the patent office on 2011-04-21 for dynamic tracking of moving targets.
This patent application is currently assigned to Accuray, Inc.. Invention is credited to John Robinson Dooley, Eric Earnst, Gopinath Kuduvalli, Chris Raanes, Euan Thomson, James Wang.
Application Number | 20110092793 12/963896 |
Document ID | / |
Family ID | 36126456 |
Filed Date | 2011-04-21 |
United States Patent
Application |
20110092793 |
Kind Code |
A1 |
Thomson; Euan ; et
al. |
April 21, 2011 |
DYNAMIC TRACKING OF MOVING TARGETS
Abstract
Treatment targets such as tumors or lesions, located within an
anatomical region that undergoes motion (which may be periodic with
cycle P), are dynamically tracked. A 4D mathematical model is
established for the non-rigid motion and deformation of the
anatomical region, from a set of CT or other 3D images. The 4D
mathematical model relates the 3D locations of part(s) of the
anatomical region with the targets being tracked, as a function of
the position in time within P. Using fiducial-less non-rigid image
registration between pre-operative DRRs and intra-operative x-ray
images, the absolute position of the target and/or other part(s) of
the anatomical region is determined. The cycle P is determined
using motion sensors such as surface markers. The radiation beams
are delivered using: 1) the results of non-rigid image
registration; 2) the 4D model; and 3) the position in time within
P.
Inventors: |
Thomson; Euan; (Los Gatos,
CA) ; Dooley; John Robinson; (Castro Valley, CA)
; Kuduvalli; Gopinath; (San Jose, CA) ; Wang;
James; (Palo Alto, CA) ; Earnst; Eric;
(Saratoga, CA) ; Raanes; Chris; (Portola Valley,
CA) |
Assignee: |
Accuray, Inc.
Sunnyvale
CA
|
Family ID: |
36126456 |
Appl. No.: |
12/963896 |
Filed: |
December 9, 2010 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
10957467 |
Sep 30, 2004 |
|
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|
12963896 |
|
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Current U.S.
Class: |
600/407 |
Current CPC
Class: |
A61N 5/1049 20130101;
A61B 5/113 20130101; A61N 5/1037 20130101; A61B 6/037 20130101;
A61B 34/20 20160201; A61N 5/1064 20130101; A61B 2034/105 20160201;
A61B 6/032 20130101; A61N 5/1067 20130101; A61N 2005/1061 20130101;
A61B 90/36 20160201; A61B 5/055 20130101; A61B 6/12 20130101; A61B
2034/2065 20160201; A61B 2090/367 20160201; A61N 2005/1055
20130101; A61B 2090/3762 20160201; A61B 6/5217 20130101; A61N
2005/1052 20130101; A61B 2017/00694 20130101; A61N 5/1039 20130101;
A61B 2090/101 20160201; A61N 2005/1062 20130101 |
Class at
Publication: |
600/407 |
International
Class: |
A61B 6/00 20060101
A61B006/00 |
Claims
1. A method, comprising: developing a four-dimensional model to
describe a movement of a target region over time; and developing a
radiation treatment plan based on the four-dimensional model,
wherein developing the radiation treatment plan comprises
determining a relative movement between a radiation beam path and
the target region.
2. The method of claim 1, wherein developing the radiation
treatment plan further comprises determining a gating of a
radiation beam, based on the relative movement, to intersect the
target region with the radiation beam path.
3. The method of claim 2, wherein determining the gating of the
radiation beam comprises determining a duration to turn on the
radiation beam when intersecting with the target region to give a
desired dose distribution of the radiation treatment plan.
Description
REFERENCE TO RELATED APPLICATION
[0001] This application is a continuation of application Ser. No.
10/957,467, filed Sep. 30, 2004, which is hereby incorporated by
reference.
BACKGROUND
[0002] In some medical applications, it may be necessary to
dynamically track targets that move with time. For example, in
radiosurgery it may be desirable to dynamically track tumors and/or
lesions in the human body that move with respiration and/or
heartbeat. In radiosurgery, accurate trajectories of the radiation
beams through the patient anatomy to the lesion or tumor being
treated can be critical, in order to achieve the radiation dose
distribution that was computed during treatment planning time. For
regions of the human anatomy that move, for example due to
breathing or heartbeat, it is important to take such motions into
consideration, when computing the effect of the motion on the
treatment plan being generated. Dynamic tracking may also be useful
in applications other than radio surgery in which parts of the
anatomy move, due to breathing, heartbeat, or any other type of
motion.
[0003] Fiducial markers have been used in the past, in order to
track moving regions of the anatomy. Fiducials-based tracking can
be difficult for a patient, for a number of reasons. For example,
high accuracy tends to be achieved by using bone-implanted fiducial
markers, but less invasive techniques such as skin-attached markers
or anatomical positions tend to be less accurate. Implantation of
fiducials into a patient is generally painful and difficult,
especially for the C-spine, the implantation process for which may
frequently lead to clinical complications.
[0004] In some methods that use gating to handle anatomical motion,
dynamic tracking may be achieved by establishing a relationship
between internally implanted fiducials, and externally placed
markers that are tracked in real time. These methods do not take
into account the non-rigid motions and deformations of the
surrounding anatomy, as a function of the motion cycle.
[0005] A method and system that address these deficiencies are thus
desirable. In particular, it is desirable to provide a reliable and
efficient method and system for dynamically tracking moving
targets.
SUMMARY
[0006] A method and system are presented for dynamically tracking
one or more targets within an anatomical region so that desired
doses of therapeutic radiation can be delivered to the targets,
while the anatomical region is undergoing motion. The targets may
be tumors or lesions, for example. The anatomical region may
include one or more reference structures, in addition to the
targets. The locations of the targets within the moving anatomical
region are determined relative to the reference structures, while
taking into account the deformation that the anatomical region
undergoes during its motion. The deformation of the anatomical
region includes non-rigid deformations, as well as rigid
deformations.
[0007] In one embodiment, the motion of the anatomical region may
be periodic. Examples of such periodic motion may include, for
example, respiration or heartbeat. The periodic motion may be
characterized by a periodic cycle P. In other embodiments, the
motion of the anatomical region may be aperiodic motion, in which
the cycle P may change over time, or for which no cycle P is
defined. Throughout this patent application, the terms "periodic
motion" and "periodic motion with cycle P" include periodic motions
having a time-varying period P(t). In other words, the terms
"periodic motion" and "periodic motion with cycle P" should be
understood as being descriptive of, and referring to, the changing
nature of the motions in the human anatomy.
[0008] In one embodiment, dynamic tracking of a periodic motion
with cycle P is accomplished by constructing a 4D (four
dimensional) mathematical model that describes the non-rigid
deformation and non-rigid motion of the anatomical region, as a
function of the relative position in time within the cycle P. The
4D mathematical model may be constructed by generating a plurality
of images (including but not limited to CT images) I.sub.j (j=1, .
. . , p) of the anatomical region, where each image is taken at one
of a succession of time points t.sub.j (j=1, . . . p) within the
cycle P. The continuous non-rigid deformation of the anatomical
region, as a function of the cycle P, is mathematically modeled by
morphing a CT image, acquired at one instant in time, into another
CT image acquired at a subsequent instant in time. The 4D
mathematical model relates the 3D (three dimensional) locations of
the skeletal structures with the 3D locations of the targets, as a
function of the instant in the cycle P (which may be, for example,
a breathing cycle or a heartbeat cycle).
[0009] In one embodiment, the 4D mathematical model of the
non-rigid deformation is used by a treatment plan generator to
generate a radiation dose distribution that prescribes a desired
amount of the therapeutic radiation to be delivered, in real time,
to the targets within the moving anatomical region. The radiation
dose distribution accounts for the non-rigid deformation of the
moving anatomical region during its periodic motion.
[0010] The cycle P for the periodic motion may be determined for
example by dynamically tracking motion sensors (such as surface
markers or LEDs (light emitting diodes), by way of example)
attached to the skin or exterior surface of the anatomical region.
The relative position within the motion cycle P of any desired time
point, including the time point t.sub.j at which the j-th image
I.sub.j was taken, can be determined in this way.
[0011] In this patent, the term "real time" refers to a time scale
that is substantially simultaneous to the actual radiation
treatment and delivery. For example, tracking that occurs at a
speed of at least several Hz or higher falls within the time scale
described as "real time" in this patent. In this patent, the term
"near real time" refers to a time scale that is slower than the
time scale referred to as "real time," for example by about one or
more orders of magnitude. For example, events occurring at a speed
of less than about once a second falls within the time scale
described as "near real time" in this patent.
[0012] In one embodiment, the near real time locations of the
reference structures are determined by performing fiducial-less
tracking of the reference structures. A set of DRRs are generated
from the CT image data, using the same beam projection geometry as
is used to acquire the live x-ray images. The live x-ray images,
acquired intra-operatively in near real time, are registered with
the DRRs (digitally reconstructed radiographs). In one embodiment,
a non-rigid image registration algorithm may be used, in which the
non-rigid transformation parameters are derived from a 3D full
motion field. The full motion field may be constructed by
estimating many local motion vectors. In one embodiment,
multi-level block matching may be used in the non-rigid image
registration algorithm, in conjunction with a similarity measure
based on pattern intensity. In one embodiment, hierarchical mesh
motion estimation may be performed.
[0013] In one embodiment, the locations of the targets can be
determined in real time, by correlating the known near real time
locations of the reference structures, which were determined using
non-rigid image registration, with the target locations that are
being sought, using the 4D mathematical model that describes how
the locations of the targets are related to the locations of the
reference structures, as a function of the relative position in
time within the cycle P. The information from the motion sensors
can be used to determine the relative position within the cycle P
of any desired time point.
[0014] In one embodiment, radiation treatment is delivered to the
real time locations of the targets, in accordance with the
radiation dose distribution generated by the treatment planning
generator. The radiation beam delivery can be synchronized to the
instant in breathing cycle determined in the treatment
planning.
BRIEF DESCRIPTION OF THE DRAWINGS
[0015] FIG. 1 provides a schematic flow chart of a method for
dynamic fiducial-less tracking of moving targets.
[0016] FIG. 2 schematically illustrates the acquisition of
pre-operative images (e.g. 3D CT scans) of a moving target within a
patient's anatomy, taken at different times points within a
breathing cycle of the patient.
[0017] FIG. 3 schematically illustrates the tracking of the 3D
motion of the target as a function of the breathing cycle of a
patient, using one of: a) a rigid reference structure that does not
move with breathing; and b) multiple reference structures that
themselves move with breathing.
[0018] FIG. 4 schematically illustrates fiducial-less tracking of a
moving target during delivery of treatment radiation, by
registering 2D near real time x-ray images of the target with DRRs
generated from a pre-operative 3D scan taken at a specific time
point within the breathing cycle of the patient.
[0019] FIG. 5 illustrates a flowchart of a non-rigid image
registration algorithm that may be used for a 2D/3D registration
between pre-operative 2D DRRs, and intra-operative x-ray projection
images.
[0020] FIG. 6 schematically illustrates the use of block matching
when estimating local motion for a point of interest within a
target in a patient.
[0021] FIG. 7 schematically illustrates a multi-resolution image
representation, for implementing multi-level block matching in one
embodiment, using multiple candidates.
[0022] FIG. 8 schematically illustrates a neighborhood R for
calculating a similarity measure based on pattern intensity.
[0023] FIG. 9 illustrates the estimation of global translation,
between the image center of a DRR and the image center of a
corresponding x-ray image.
[0024] FIG. 10 schematically illustrates a mesh grid established
for a DRR of a target, and a corresponding mesh grid established
for an x-ray image of the target, in an embodiment in which the
target is located within the cervical region of the spine.
[0025] FIG. 11 illustrates a mesh hierarchy, during mesh motion
estimation, the mesh hierarchy starting from a relatively coarse
mesh, and progressing onto finer meshes.
[0026] FIG. 12 schematically illustrates the passing on of node
estimation, from a coarse mesh resolution level onto a finer mesh
resolution level.
[0027] FIG. 13 schematically illustrates the determination of a
motion vector for a point of interest, by interpolation from
surrounding nodes.
[0028] FIG. 14 schematically illustrates, in vectorial form, a full
motion field reconstructed from many estimated local motion
vectors.
[0029] FIG. 15 illustrates the geometric relations between a
three-dimensional treatment target, and two orthogonal 2D x-ray
projections
[0030] FIG. 16 schematically illustrates dynamic tracking of a
moving target within a patient during real time delivery of
treatment radiation, using surface markers (e.g. LEDs) to monitor
the breathing motion of the patient.
[0031] FIG. 17 provides a schematic block diagram of a system for
dynamically tracking targets within an anatomical region that is
undergoing periodic motion (e.g. respiration or heartbeat) and
delivering therapeutic radiation to the moving targets.
DETAILED DESCRIPTION
[0032] A number of techniques are described for dynamically
tracking tumors/lesions in the human body that is undergoing
motion. The methods are of principal use in radiosurgery, but may
also be useful in other applications where it may be necessary to
dynamically track parts of the anatomy that move, for example
because of respiration or heartbeat.
[0033] Some portions of the detailed description that follows are
presented in terms of algorithms and symbolic representations of
operations on data bits within a computer memory. These algorithmic
descriptions and representations are the means used by those
skilled in the data processing arts to most effectively convey the
substance of their work to others skilled in the art. An algorithm
is conceived to be a self-consistent sequence of acts leading to a
desired result. These acts require physical manipulations of
physical quantities. Usually, though not necessarily, these
quantities may take the form of electrical or magnetic signals
capable of being stored, transferred, combined, compared, and
otherwise manipulated. It has proven convenient at times,
principally for reasons of common usage, to refer to these signals
as bits, values, elements, symbols, characters, terms, numbers, or
the like.
[0034] It should be borne in mind, however, that all of these and
similar terms are to be associated with the appropriate physical
quantities and are merely convenient labels applied to these
quantities. Unless specifically stated otherwise, it is appreciated
that throughout the descriptions below, discussions that utilize
terms such as "processing" or "computing" or "calculating" or
"determining" or "displaying" or the like, refer to the actions and
processes of a computer system, or a similar electronic computing
device. The computer system manipulates and transforms data,
represented as physical or electronic quantities within the
computer system's registers and memories, into other data similarly
represented as physical quantities within the computer system's
memories or registers, or within other such information storage,
transmission or display devices.
[0035] The methods and techniques described below can be
implemented by an apparatus for performing the operations discussed
below. Such an apparatus may be specially constructed for the
required purposes, or it may comprise a general purpose computer,
selectively activated or reconfigured by a computer program stored
in the computer. Such a computer program may be stored in a
computer readable storage medium, such as, but not limited to, any
type of disk including floppy disks, optical disks, CD-ROMs, and
magnetic-optical disks, read-only memories (ROMs), random access
memories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, or any
type of media suitable for storing electronic instructions. Each
such computer readable storage medium may be coupled to a computer
system bus.
[0036] The algorithms and displays presented herein are not
inherently related to any particular computer or other apparatus.
Various general purpose systems may be used with programs that are
designed in accordance with the teachings below, or it may prove
convenient to construct a more specialized apparatus to perform the
requisite methods and techniques. For example, any of the methods
described below can be implemented in hard-wired circuitry, or by
programming a general purpose processor, or by any combination of
hardware and software. One of skill in the art will appreciate that
the methods and techniques described below can be practiced with a
wide variety of computer system configurations, including hand-held
devices, multiprocessor systems, microprocessor-based or
programmable consumer electronics, network PCs, minicomputers,
mainframe computers, and the like. The methods and techniques
described below can also be practiced in distributed computing
environments where tasks are performed by remote processing devices
that are linked through a communications network.
[0037] The methods and systems described below may be implemented
using computer software. Computer software may be referred to using
different terms, for example a program, a procedure, or an
application. If written in a programming language conforming to a
recognized standard, sequences of instructions designed to
implement these methods and systems can be compiled for execution
on a variety of hardware platforms and for interface to a variety
of operating systems. Also, these methods and systems are not
described with reference to any particular programming language. It
will be appreciated that a variety of programming languages may be
used to implement the teachings of the invention as described
herein. Furthermore, it is common in the art to speak of software,
in one form or another, as taking an action or causing a result.
Such expressions are merely a shorthand way of saying that the
execution of the software by a computer causes one or more
processors in the computer to perform an action or produce a
result.
[0038] FIG. 1 provides a schematic flow chart 100 describing the
dynamic fiducial-less tracking of moving targets. The targets are
located within an anatomical region. The targets may be tumors or
lesions, for example, or organs of interest. The anatomical region
typically includes one or more reference structures, in addition to
the targets. In one embodiment of the dynamic tracking method and
system, the reference structures may be skeletal (i.e. bony)
structures. In another embodiment, the reference structures may be
other natural anatomical structures, including but not limited to
cartilages or other (typically rather dense) organs. In yet another
embodiment, the reference structures may be artificial structures,
for example fiducials or surgical hardware.
[0039] As mentioned earlier, throughout this description the term
"periodic motion with cycle P" should be understood as including
periodic motions in which the cycle P of the periodic motion, as
well as the amplitude and waveform of the motion, change with time,
In other words, the term "periodic motion" or "periodic motion with
cycle P" should be understood as also referring to the changing
nature of the motions in the human anatomy.
[0040] As mentioned earlier, the anatomical region may undergo a
deformation (which may be a non-rigid deformation) during its
motion. While in the embodiment illustrated in FIG. 1, the
anatomical region is described as moving periodically while
undergoing a non-rigid deformation, any other type of motion (e.g.
aperiodic motion) and any type of deformation of the anatomical
region may be tracked, using the method and system described in
this patent.
[0041] In overview, in the embodiment illustrated in FIG. 1 the
locations of the targets, within the periodically moving and
non-rigidly deforming anatomical region are determined, in steps
105-160. A radiation dose distribution is computed that results
from a substantially continuous delivery of radiation through the
non-rigidly moving and deforming anatomical region. Finally, in
step 170, radiation is delivered in real time to the targets, in
accordance with the computed radiation dose distribution.
[0042] As a first step, a set of CT images I.sub.j (j=1, . . . p)
are acquired in step 105, each CT image taken at one of a
succession of time points t.sub.j (j=1, . . . p) within the cycle
P. In step 110, the cycle of the periodic motion (e.g., the
respiratory cycle or the heartbeat cycle) is established, for
example by dynamic tracking of markers or other sensors attached to
the skin of the anatomical region.
[0043] In step 120, a 4D (3D+time) mathematical model is
constructed from these CT scans and from the motion cycle
information obtained from the sensors. The mathematical model
describes a non-rigid motion and deformation of the anatomical
region as it undergoes its periodic motion (e.g. respiration), as a
function of the instant in the motion cycle P. The 4D mathematical
model relates the locations of the targets to the locations of the
reference structures, as a function of the relative position in
time within the cycle P, within the periodically moving anatomical
region. More generally, this 4D model may describe the 3D+time
relationship between a part or parts of the anatomy, and a target
to be tracked for radiosurgery, as a function of the instant in
time in the breathing cycle.
[0044] Next, in steps 130, 135, and 140 the absolute position of a
part or parts of the anatomy is determined, using x-ray imaging and
2D-to-3D image registration. In these steps, the locations of the
reference structures and/or the targets are determined in near real
time, by fiducial-less tracking of the reference structures and/or
targets using a non-rigid image registration algorithm.
[0045] As explained earlier, in this patent the term "real time"
refers to a time scale that is substantially simultaneous to the
actual radiation treatment and delivery. In just one exemplary
embodiment, intra-operative real-time tracking of a target may be
implemented using optical markers which track at about a 30 Hz
rate, and using a model which updates its predictions at about a 80
Hz rate. This is intended to be an illustrative example only, and
real time tracking may occur at a wide range of different tracking
speeds, generally higher than about 1 Hz. The term "near real time"
refers to a time scale that is slower, for example by about one or
more orders of magnitude, than the time scale described by the term
"real time." As an example, the time scale for acquiring x-ray
images, which may range from about a fraction of a second to about
several seconds, will be described as "near real time."
[0046] A set of DRRs of the anatomical region are generated, in
step 130, from the CT images acquired in step 105. In step 135,
live x-ray projection images of the anatomical region are acquired.
In step 140, the DRRs of the reference structures are registered
with the near real time x-ray images of the reference
structures.
[0047] Step 150 describes 4D treatment planning, in which a
radiation dose distribution is computed that results from
continuous beam delivery through the non-rigidly moving and
deforming anatomical region. In this step, the radiation beam
trajectories are determined, using the knowledge (obtained in step
140) of the absolute locations of the reference structures, and
using the 4D model that relates the reference structures to the
instant in breathing cycle (as determined using information from
the sensors), and to the targets whose locations are being
tracked.
[0048] In step 160, the target locations are determined. In one
embodiment, the 4D mathematical model may be used, together with
the absolute positions of the skeletal structures as determined by
fiducial-less tracking, and the information obtained from the
sensors regarding the motion cycle, to determine the target
locations. In an alternative embodiment, the target locations may
be determined by 2D/3D non-rigid image registration, during which
the DRRs (generated during the treatment planning stage) are
registered onto near real time x-ray images. Finally, in step 170,
radiation is delivered to the targets in accordance with the
radiation dose distribution generated through 4D treatment
planning.
[0049] FIG. 2 schematically illustrates the acquisition of
pre-operative images (e.g. 3D CT scans) of a moving target within a
patient's anatomy. In the illustrated embodiment, the target moves
due to breathing motion of the patient. While the illustrated
embodiment shows 3D CT scans, any other type of 3D scans may be
performed, including but not limited to 3D MRI (magnetic resonance
imaging) scans, 3D PET (positron emission tomography) scans, and 3D
ultrasound scans. In the illustrated embodiment, a set of CT scans
are taken at different times points t.sub.j (j=1, . . . , k, . . .
, l, . . . , m, . . . , p) within a breathing cycle P of the
patient. In the illustrated embodiment,
t.sub.1<t.sub.k<t.sub.l<t.sub.m<t.sub.p. The time
points t.sub.j correspond to different epochs in the patient
breathing cycle. The cycle P is monitored by an external sensor,
e.g. a breathing sensor. For example, a surface marker (such as an
LED) or a similar device may be attached to the skin. In
embodiments in which the target undergoes motions other than
respiration, different types of sensors (e.g. a cardiac monitor
when heartbeat is being monitored) may be used.
[0050] In the illustrated embodiment, the CT images are taken at
time points t.sub.k, t.sub.l, and t.sub.p, respectively. The epochs
or time points within the breathing cycle P are preferably chosen
to substantially encompass the overall dynamic range of the
periodic motion. For example, in one embodiment, the time points
may include: a time point t.sub.1 corresponding to a trough of the
cycle P; a time point t.sub.p corresponding to a peak of the cycle
P; and a third time point t.sub.k disposed at an intermediate
location between the peak and the trough of the cycle P. In other
embodiments, the time points selected for taking the CT images may
include more than the three time points t.sub.1, t.sub.p, and
t.sub.k described above.
[0051] From this set of CT studies, a 4D mathematical model is
constructed that morphs the CT image acquired at one instant or
time point in the motion cycle into another CT image acquired at a
subsequent instant or time point in the motion cycle, providing a
model for the continuous non-rigid deformation of the anatomy as a
function of the motion cycle. In image processing, it is well known
in the art to morph one image into another image, and to describe
this in terms of a mathematical model. Any standard software and/or
algorithms that are known and may be commercially available can be
used.
[0052] In one embodiment, the 4D mathematical model constructed
from the set of CT images shown in FIG. 2 is used for 4D treatment
planning, i.e. to compute the dose distributions resulting from a
continuous radiation beam delivery through the non-rigidly moving
anatomy, taking into account the non-rigid motion and deformation
of the treatment targets as a function of the position in time
within the motion cycle. In this embodiment, 4D treatment planning
consists of two parts: a) creating a mathematical model for the
non-rigid deformations of the anatomy as a function of the instant
in the motion cycle, as described above; and b) using the
mathematical model to compute the dose distributions resulting from
continuous radiation beam delivery through the non-rigidly moving
anatomy.
[0053] In order to compute the desired radiation dose
distributions, the beam trajectories are initially defined with
respect to a nominal patient coordinate system. In one embodiment,
the nominal patient coordinate system may be chosen to orient with
respect to one of the several CT images illustrated in FIG. 2 and
acquired to cover the motion cycle. Different orientations may be
chosen in other embodiments. In one embodiment, each radiation beam
is turned on from the time corresponding to the point in time in
which each CT image was taken, and remains on for a duration
selected to give the desired dose distribution. The dose absorption
is calculated as a function of time from the initial time point,
taking into account the non-rigid deformations of the patient
anatomy.
[0054] In one embodiment, the 4D mathematical model relates the 3D
locations of one or more reference structures with the 3D locations
of the target, as a function of the instant in the motion cycle. In
the 4D model, one or more of the selected reference structures may
be stationary with respect to the motion cycle, while others of the
selected reference structures may undergo non-rigid motion with
respect to the motion cycle.
[0055] FIG. 3 schematically illustrates the tracking of the 3D
motion of the target as a function of the motion cycle of a
patient, using one of: a) a substantially rigid reference structure
that is substantially stationary throughout the motion of the
patient; and b) multiple reference structures that themselves move
with the motion of the patient. Two kinds of models are possible:
(a) 3D motion of the target (tumor/lesion) as a function of the
motion cycle, with reference to a substantially rigid reference
structure that is substantially stationary (including but not
limited to vertebral structures) or (b) 3D motion of the target
(e.g. tumor/lesion) as a function of the motion cycle, with
reference to multiple reference structures that themselves move,
along with the periodic motion of the anatomical region.
[0056] In the embodiment illustrated in FIG. 3, the target 210
moves within an anatomical region 200 because of the periodic
motion of the anatomical region 200. The reference structures are
illustrated using reference numeral 230 and 232. The reference
structures 230 themselves move with breathing. On the other hand,
reference structure 232 is a substantially rigid reference
structure that does not move with the breathing or other periodic
motion of the anatomical region 210.
[0057] The 3D location and orientation of the multiple skeletal
structures enable vectors to be drawn that point from each skeletal
structure to the tumor or lesion. A model that describes the 3D
motion of the tumor/lesion as a function of the breathing cycle,
with reference to a rigid skeletal structure (such as the skeletal
structure indicated in FIG. 3 using reference numeral 210) that
itself does not move with respect to breathing, may be used for
example in conjunction with the vertebral structures for a patient
lying supine for treatment, which is a practical example of such a
non-moving skeletal structure. In another model, in which reference
is made to rigid skeletal structures that themselves move with
breathing, the 3D motion of the tumor/lesion can be described as a
compound function of 1) the breathing cycle and the 2) location of
multiple skeletal structures which themselves move with
breathing.
[0058] Once the relationship between the locations of the reference
structures and the locations of the target is determined by the 4D
model, as a function of the point in time within the periodic
motion cycle, the absolute position of the reference structures is
determined in near real time. In one embodiment, this is
accomplished by 1) generating DRRs of the reference structures from
the 3D CT images, which were shown in FIG. 2 and which were used to
generate the 4D mathematical model; 2) taking "live" or near
real-time x-ray images of the reference structures; and then 3)
performing non-rigid image registration between the live x-ray
images and the DRRs. Once the locations of the reference structures
are determined using non-rigid image registration, the locations of
the targets can be easily determined using the 4D mathematical
model described above.
[0059] FIG. 4 schematically illustrates fiducial-less tracking of
reference structures. In one embodiment, this fiducial-less
tracking is performed by registering 2D near real time x-ray images
of the target with DRRs generated from a pre-operative 3D scan
taken at a specific time point within the motion cycle of the
patient. The position and orientation of the reference structures
in the patient anatomy (at the time of treatment delivery) can be
tracked with respect to the corresponding structures in one of the
CT studies, using a 2D to 3D registration methods. In one
embodiment, a non-rigid 2D/3D image registration is performed,
described in detail below.
[0060] As a first step in the 2D/3D non-rigid image registration
process, a library of DRRs is generated for the projection geometry
that will be used in the acquisition of live images at the time of
treatment delivery. A pair of live (or "near real time") x-ray
images of the patient is acquired during treatment delivery, using
the same projection geometry as used for generating the DRRs. The
CT images used to generate the DRRs will correspond to one of the
epochs in the motion cycle, typically the same one with respect to
which beams are assigned to during treatment planning. The DRRs may
be generated for several translations and orientations of the CT
image, in order to cover the typical range of patient movements
during treatment.
[0061] The acquired images are registered with the DRRs using a
feature recognition algorithm that tracks the reference structures.
The image registration algorithm (described in detail below) may be
repeated for multiple reference structures, to give the position
and orientation of each structure with respect to the corresponding
structures in the CT study (from which DRRs are made). In one
embodiment, the difference in imaging characteristics of the tumor
or lesion or a nearby anatomical region may be enhanced further by
using high-sensitivity x-ray detectors.
[0062] In one embodiment, the location of the tumor/lesion can be
derived from the locations of the reference structures, determined
by the non-rigid registration process. The location and orientation
of multiple reference structures tracked using the fiducial-less
algorithm are used to interpolate the location of the tumor/lesion,
using their corresponding geometric relationships, as learned from
the CT studies and the 4D model. In another embodiment,
fiducial-less tracking is performed for the targets (e.g.
tumors/lesions) themselves. If the target being tracked is
sufficiently different in x-ray imaging characteristics relative to
the surrounding tissue, the target itself can be directly tracked
using the 2D-to-3D registration technique described below.
[0063] A non-rigid 2D-to-3D image registration technique that
accounts for non-rigid deformations of the anatomy, and which uses
anatomical reference structures instead of fiducials, is described
below. While the non-rigid image registration algorithm described
below is described in the context of skeletal structures, and in
particular skeletal structures in the spinal region of the human
anatomy, it should be understood that reference structures other
than spinal skeletal structures may also be used with this
non-rigid image registration algorithm.
[0064] The non-rigid image registration technique is also described
in the following five co-owned patent applications: 1) U.S. patent
application Ser. No. 10/880,486, characterized by attorney docket
number ACCL-136, entitled "Fiducial-less Tracking With Non-Rigid
Image Registration"; 2) U.S. patent application Ser. No.
10/881,612, characterized by attorney docket number ACCL-137,
entitled "Motion Field Generation For Non-rigid Image
Registration"; 3) U.S. patent application Ser. No. 10/881,209,
characterized by attorney docket number ACCL-146, entitled "ROI
Selection In Image Registration"; 4) U.S. patent application Ser.
No. 10/881,208, characterized by attorney docket number ACCL-147,
entitled "Image Enhancement Method and System For Fiducial-less
Tracking of Treatment Targets"; 5) U.S. patent application Ser. No.
10/881,206, characterized by attorney docket number ACCL-150,
entitled "DRR Generation Using A Non-Linear Attenuation Model." All
five patent applications, owned are incorporated by reference
herein in their entireties.
[0065] FIG. 5 illustrates a flowchart 400 of a non-rigid image
registration algorithm that may be used for a 2D/3D registration
between 2D DRRs of an anatomical region, reconstructed from
pre-operative CT scan data, and intra-operative, near real-time
x-ray projection images of the target within the anatomical region.
In particular, the DRR is reconstructed from CT scan data
representative of a CT image I.sub.j taken at a specific time point
t.sub.j within the periodic cycle P.
[0066] As a first step, 2D DRRs may be generated from pre-operative
3D scan data representative of a CT image I.sub.j, in step 402. In
one embodiment, the images for which non-rigid image registration
is performed (i.e., DRRs and x-ray images) are discretized images
each characterized by an array of pixels, each pixel having an
associated pixel value representative of the intensity of the image
at a surface unit area corresponding to the pixel.
[0067] In one embodiment, an improved DRR generation process can be
implemented in step 402 to bring out the skeletal reference
structures, which are usually not easily visible in the images, or
even may be hidden. In step 402, the CT scan data are modified
based on a non-linear attenuation model that emphasizes the
skeletal structures and thus improves the quality of the DRRs. In
step 403 in flowchart 400, an image enhancement technique may also
be implemented for the DRRs. In this step, a top-hat filter is used
to bring out the skeletal structures in the DRRs generated in step
402.
[0068] In the illustrated embodiment, image registration is
performed in a selected region of interest (ROI) within the
enhanced DRR, in order to improve efficiency. Accordingly, an ROI
is defined in the DRR, in step 404, after enhancement of the DRRs.
The ROI selection process performed in step 404 is based on image
entropy, and is fully automatic so as not to require user
interaction. Intra-operative 2D x-ray projection images are then
generated, in near real time, in step 410. Image enhancement is
performed on the x-ray images, in step 415, using a top-hat filter
by analogy to step 403.
[0069] Non-rigid image registration is then performed between the
enhanced x-ray images and the enhanced DRRs, within the ROI. In
particular, a similarity measure is used to compare the pixel
intensities in the x-ray images and the DRR images, in order to
determine any change in the position and/or orientation and/or
physiological deformation of the patient. In steps 420-450, a
non-rigid deformation that describes real patient movement and body
deformation is defined. To define the non-rigid deformation, a full
motion field is constructed that is composed of many local motion
fields, i.e. a plurality of locally estimated motion vectors. To
estimate local motion at a given point of interest within the ROI,
a similarity measure based on pattern intensity is used to compare
pixel intensities.
[0070] A full motion field that is composed of many local motions
may describe any desired non-rigid deformation. Further, a full
motion field derived in this manner can account for non-rigid
motions (translations and/or rotations) of the object, in addition
to non-rigid deformations, between different image acquisitions of
the object. In order to efficiently compute the local motion
vectors at any point of interest within the ROI, hierarchical mesh
motion estimation and multi-level block matching (performed in
conjunction with an intensity-based similarity measure) are
performed. These methods allow for a fast computation of the image
registration algorithm 400. A smoothness constraint is imposed to
reconstruct the motion field at mesh nodes in which mismatching
occurred. The non-rigid transformation parameters for the non-rigid
image registration are then computed from the full motion
field.
[0071] In the embodiment illustrated in FIG. 5, the non-rigid
deformations described by the full motion field occur in between
the acquisition of the 3D CT scan data of a treatment target region
in a patient, and the acquisition of the x-ray projection images of
the target region. In step 420, a global translation of the entire
image is first estimated. The estimated global translation is used
as the initial estimate for all further local motion estimation. In
the next step 430, mesh nodal motion estimation is performed, using
a hierarchical mesh structure designed to estimate the local motion
in multiple levels. In the next step 440, motion field
reconstruction is performed for those mesh nodes in which a
mismatch occurs. The reconstruction of the motion field is
performed by imposing a smoothness constraint, which is based on
the assumption that local motions are continuous, because of matter
coherence. In step 450, the local motion vector at any desired
point of interest is derived by interpolating from the nodal
motions estimated for the mesh nodes that surround the point of
interest. The full motion field is then constructed, using the
local motion vectors derived for a plurality of desired points of
interest.
[0072] In the final steps, shown as step 455 and step 460 in FIG.
5, the non-rigid transformation parameters are derived from the
full motion field. In step 455, the target displacements are
derived from the full motion field. In step 460, the average rigid
transformation is derived from the full motion field.
[0073] The quality of DRR images relies on proper attenuation
modeling, as well as a proper interpolation scheme for
interpolation the CT numbers. In one embodiment, in step 402 (in
the flowchart shown in FIG. 2), an improved x-ray attenuation model
is formulated for fiducial-less tracking, so that the DRRs become
more like the real x-ray projection images. A linear attenuation
model is no longer assumed, and the CT numbers are modified in
order to compensate for the above-mentioned a difference in the
bone-to-tissue attenuation ratio. On the basis of many experiments
conducted with patient clinical data, the following empirical
equation was formulated to modify the original CT numbers:
C(x,y,z)=a C.sub.0(x,y,z)e.sup.bC.sup.0.sup.(x,y,z) (1)
where C(x,y,z) represents the modified CT number of a 3D CT voxel
located at a point (x,y,z); a and b represent weighting
coefficients; and C.sub.0(x,y,z) represents the un-modified CT
number, based on a linear attenuation model, of a 3D CT voxel
having a location (x,y,z).
[0074] The interpolation scheme used in one embodiment to improve
the quality of DRRs is bi-linear interpolation. In this embodiment,
bi-linear interpolation is performed in step 402, to integrate the
CT numbers along the CT voxels that are encountered by each cast
ray. In one embodiment, the bi-linear interpolation is followed by
a 1-D polynomial interpolation over three voxel slices, for each
voxel of interest. The three voxel slices include the voxel slice
containing the voxel of interest, plus each adjacent voxel
slice.
[0075] In some embodiments, fiducial-less tracking relies on
skeletal reference structures (e.g. vertebral structures) that are
usually not easily visible, or may even be hidden in the DRRs and
in the x-ray projection images. Because such skeletal structures
have to be registered, both the DRR and the x-ray images have to be
enhanced to bring out the details of the vertebral structures and
improve their visibility. In one embodiment, therefore, image
enhancement is undertaken for both the DRRs and the x-ray
projection images. In most thoracic and lumbar cases, the skeletal
structures are not easily visible or even hidden in DRR and X-ray
images. For these cases therefore, enhancement of the DRR and the
x-ray images is necessary, in order to make registration at all
possible. In cervical cases, the skeletal structures of spine are
well visible in both the DRR and the x-ray images, but the details
of the structures are still not clear. Accordingly, in cervical
cases, the DRR and the x-ray images should be enhanced to improve
the registration.
[0076] In the embodiment illustrated in FIG. 5, a top-hat filter is
designed and used to enhance the x-ray images (step 415 in FIG. 5)
and to enhance the DRR images (step 403 in FIG. 5). In particular,
the skeletal structures in the images are enhanced, i.e., brought
out, by applying a top hat filter operator to the pixels of the
x-ray projection images and the DRR images. As known, a top hat
filter is a nonlinear operator that finds the brightest pixels in
two different size neighborhoods, then keeps the extreme values. In
one embodiment, the top hat filter operates as follows: if the
brightest value in the smaller neighborhood region is greater that
the value in the larger neighborhood, by an amount determined by a
user-entered threshold, then the pixel remains, otherwise it is
eliminated. As a result of applying a top hat filter to the images,
it is possible to locate features of interest.
[0077] In one embodiment, the top-hat filter is designed by using a
weighted combination of image opening and closing with a certain
structural element. The top hat filter operator is defined
mathematically as follows:
f e = f + w .times. [ f - .gamma. B ( f ) ] - b .times. [ .PHI. B (
f ) - f ] = f + w .times. W T H ( f ) - b .times. BTH ( f ) ( 2 )
##EQU00001##
where f.sub.e represents the enhanced image, resulting from the
application of the top hat filter operator to each pixel in the
original image; f represents the original image; w and b represent
weighting coefficients, .gamma..sub.B(f) represents a structural
element for the opening of the original image f, and .phi..sub.B(f)
represents a structural element for the closing of the original
image f.
[0078] In expression (2) above, WTH(f)=f-.gamma..sub.B(f) is called
a white top-hat filter, whereas BTH(f)=.phi..sub.B(f)-f is called a
black top-hat filter. The structural elements .gamma..sub.B(f) and
.phi..sub.B(f) are masks that are used to perform the basic
morphological operation. The sizes of the structural elements vary
slightly for cervical, thoracic, and lumbar applications. The
empirical values are determined experimentally. The weighting
coefficients w and b are determined adaptively by the amplitudes of
WTH(f) and BTH(f), respectively. Empirically, the values of the
weighting coefficients w and b have been found to be about 1 each
(w=1, b=1), for a cervical case in which less tissue is present. In
the lumbar case, in which more tissue is present, the values of w
and b have been found to be greater than about 2 each (w>2,
b>2). In the lumbar case, the weighting process brings out the
skeletal structures to a greater degree, compared with the cervical
case.
[0079] In one embodiment, image registration is conducted only in a
certain region of interest (ROI) defined in the DRR. The ROI
contains the treatment target (e.g. a tumor or lesion). In one
embodiment, image entropy is specifically defined, in step 404 in
FIG. 5. In this way, the ROI can be automatically selected, for
optimum registration, minimizing or even eliminating user
interaction. Because image registration relies on the image content
or image information, in this embodiment the ROI is optimized to
contain as much information as possible.
[0080] The Shannon entropy, known from conventional communication
theory, is commonly used as a measure of information in signal and
image processing. It is defined as H=-.SIGMA..sub.i.sup.np.sub.i
log p.sub.i where H represents the average information supplied by
a set of n symbols whose probabilities are given by p.sub.1,
p.sub.2, . . . , p.sub.n. When applied to the pixels of each image
(as enhanced in steps 403 or 415 in FIG. 5), the Shannon entropy
for each image is defined by: H=-.SIGMA..sub.Ip(I) log p(I), where
I is the image intensity level, and p(I) is the probability of an
image intensity value I occurring within the ROI. In the original
formulation by Shannon, any change in the data that tends to
equalize the probabilities p.sub.1, p.sub.2, . . . , p.sub.n,
increases the entropy, as observed by Shannon. For a given image,
the Shannon entropy is conventionally calculated from a image
intensity histogram, in which the probabilities p.sub.1 p.sub.2, .
. . , p.sub.n, are histogram entries.
[0081] In one embodiment, the Shannon entropy H is modified, based
on the fact that the skeletal structures occur in bright areas. In
this embodiment, a modified Shannon entropy is used for each image,
which is defined as follows:
H=-.SIGMA..sub.IIp(I)log p(I), (3)
where again I is the image intensity level, and p(I) is the
probability of the image intensity value I occurring within the
ROI. In step 404 (shown in FIG. 5), the modified Shannon entropy is
first determined for the enhanced DRR image. Once the modified
Shannon entropy H is calculated, an ROI is then automatically
selected by determining the region within the DRR for which the
entropy H is maximized. Subsequent steps in the image registration
process (steps 420-450 in FIG. 5) take place only within the
ROI.
[0082] Restricting the image registration process to within a ROI
has several advantages. One advantage is that such a restriction
can speed up the registration process, since the registration needs
to be performed only for the ROI. For example, the similarity
measure needs only be computed for the ROI, and block matching need
only be performed within the ROI. Further, the registration process
is more accurate when limited to an area within the ROI. The more
limited the region in which registration is conducted, the less
likely it is that structures within the ROI would have moved
relative to each other between the time of the pre-operative CT
scans and the time of the medical treatment.
[0083] Based on the improved and enhanced DRRs (generated in steps
402 and 403 in FIG. 5), and the enhanced x-ray projection images
(step 415 in FIG. 5), in which the skeletal reference structures
have been brought out to make fiducial-less tracking possible, a
non-rigid deformation of the anatomical region is determined in
steps 420-450. In this patent, a `rigid body` assumption, i.e.
which is often made in image registration applications, and which
assumes that between image acquisitions, the anatomical and
pathological structures of interest do not deform or distort, does
not have to be made. If a rigid body assumption is not needed,
there is no longer a need to preserve the `rigid body` constraints,
i.e. to require that the body be rigid and not undergo any local
variations during the transformation. Based on an abundance of
observations and analyses on clinical patient data, in the present
patent a non-rigid deformation is assumed, in lieu of a rigid
transformation, to obtain an improved description of the real
patient movement and body deformation. By computing a non-rigid
deformation field, patient position/orientation can be more
reliably monitored and corrected during the initial alignment, as
well as throughout the entire treatment.
[0084] A non-rigid image registration allows the inherent local
anatomical variations that exist between different image
acquisitions to be accounted for, in contrast to a rigid image
registration which does not allow the overcoming of such
variations. Non-rigid registration defines a deformation field that
provides a translation or mapping for every pixel in the image. In
one embodiment, a full motion field, composed of many local motion
vectors or fields, is computed in order to derive the non-rigid
deformation field.
[0085] In order to estimate local motion fields, in one embodiment,
a multi-level block matching method is used in conjunction with a
similarity measure based on pattern intensity. This approach allows
the local motion to be rapidly and accurately estimated in most
parts of the ROI. Multi-level block matching, which allows for
computational efficiency, is described in conjunction with a rigid
registration algorithm, in a commonly owned application, U.S. Ser.
No. 10/652,786 (the "'786 application"), incorporated by reference
in its entirety. A similarity measure based on pattern intensity,
used in conjunction with a registration algorithm based on rigid
transformations, i.e. the "FAST 6D algorithm" developed by Accuray,
Inc. for use with the Cyberknife radiosurgery system, is described
in full in commonly owned applications, U.S. Ser. Nos. 10/652,786
(the '786 application"), 10/652,717 (the "'717 application"), and
10/652,785 (the "'785 application"), which are all incorporated by
reference in their entireties. In the present patent, the pattern
intensity based similarity measure and the multi-level block
matching method are used in conjunction with a registration
algorithm based on a non-rigid (rather than a rigid)
transformation. The pattern intensity-based similarity measure,
originally developed for a rigid image registration algorithm,
provides a powerful and efficient technique for solving the 2D/3D
image registration problem, also in a non-rigid framework.
[0086] In one embodiment, block matching is performed, i.e. a small
block centered around a point of interest is used in order to
locally estimate the displacements at each desired point within the
ROI. As known, when using block matching to register a first image
onto a second image, the first image is divided into different
blocks, typically rectangular boxes of equal size. Each point of
interest, which may be a mesh node, or may be a non-node pixel that
is surrounded by mesh nodes, is taken as the center of one of the
blocks. These blocks are then translated so as to maximize a local
similarity criterion, which in one embodiment is the pattern
intensity based similarity measure, described above.
[0087] In block matching methods, it is generally assumed that each
pixel in a block has the same motion, and a block matching
algorithm is typically used to estimate the motion vectors for each
block. In a block matching algorithm used in one embodiment, a
search is conducted for a matching block in the second image, in a
manner so as to maximize a measure of similarity, based on pattern
intensity, between the respective blocks. The search is for a
location of the maximum in the similarity measure function, the
maximum representing the existence of a matching block in the
second image. The search may be conducted within a search window
that is defined around the point of interest and that contains the
block.
[0088] In any block matching algorithm, it is important to optimize
the search strategy, and to select an appropriate block size. For
small blocks, the translational rigid model is typically assumed.
Even though rigid rotations or some other complicated deformations
exist, the rigid body translation model is valid for estimating the
translations for the block center point. When rotations or other
deformations exist in addition to the translations, the accuracy
increases with decreasing block size, and decreases with increasing
block size. With the use of smaller block sizes, however, the
possibility of mismatching increases. In one embodiment, a block
size selection strategy is adopted in which it is assumed that
larger blocks are needed for larger displacements, and that smaller
blocks are need for smaller displacements.
[0089] FIG. 6 schematically illustrates local motion estimation for
a point of interest within a target in a patient, using block
matching. In the embodiment illustrated in FIG. 6, the target is
located in the cervical region of the spine, although it is again
emphasized that the non-rigid 2D/3D image registration technique
can be used in applications other than structural spine tracking.
The left and the right pictures are the DRR and the x-ray images,
respectively. A small block 203A is defined around a point of
interest 205 in the DRR. Also, a search window 207 that encompasses
the block 203 is defined in the DRR. The matching block in the
x-ray image is indicated in FIG. 6 with reference numeral 203B. In
the embodiment illustrated in FIG. 6, the size of the search window
207 is 48 mm.times.48 mm, and the block size is 15.times.15 mm. It
can be seen, simply by visual inspection, that the point of
interest 205 is well located in the X-ray image.
[0090] FIG. 7 schematically illustrates a multi-resolution image
representation, when implementing multi-level block matching, using
multiple candidates. Multi-level block matching is a fast search
method that uses the displacement estimates made at a lower level
as the initial results for subsequent search phases. The basic idea
in multi-level block matching is to match the images at each of a
plurality of resolution levels, successively, starting from the
lowest resolution level and moving up to the highest resolution
level. The full-size image, having the highest resolution level, is
shown at the bottom in FIG. 7, as level 1. The upper images (level
2 and level 3) have successively lower spatial resolutions, the
image having the lowest resolution being shown as level 3. The
lower resolution images are obtained by lower pass filtering, and
sub-sampling the full-size images.
[0091] In FIG. 7, assuming that the full image block size is
W.times.H in Level 1, the block sizes are
W 2 .times. H 2 and W 4 .times. H 4 ##EQU00002##
in Level 2 and Level 3, respectively, as indicated in the figure.
In the lowest resolution level (Level 3), a large search range is
used to enable estimation of large displacements. A very small
search range (-2, +2) is used in the rest of the resolution
levels.
[0092] The results at the lower resolution level serve to determine
rough estimates of the displacements. The output at the lower level
is then passed onto the subsequent higher resolution level. The
estimated motion vector (in most cases, a translation vector) for
the block is successively refined, using the higher resolution
images. In the final matching results, the accuracy of the
estimated translations depends on the spatial resolution of the
highest resolution images (shown as level 1 in FIG. 7).
[0093] There is some risk in multi-level matching. It is possible
that the estimate at lower levels may fall in a local maximum, and
far away from the global maximum that is being sought. In this
case, further matchings at subsequent higher resolution levels may
not converge to its global maximum. To overcome this risk, multiple
candidates are used for the estimates, in one embodiment. Many
candidates that have shown optimal matching results are passed on
from the lower levels to the higher resolution levels. The more
candidates that are used, the more reliable are the estimates. In
one embodiment, the best candidates are ranked by the similarity
measure function values.
[0094] In one embodiment, a similarity measure based on pattern
intensity is used, in conjunction with multi-level block matching.
As mentioned earlier, this similarity measure is a key element
contributing to the success of the "FAST 6D algorithm," described
in the commonly owned '786 application, '717 application, and '785
application. In one embodiment, the similarity measure is
determined by forming a difference image between the "live" (or
near real time) x-ray projection images and the DRR images, and
applying upon each pixel of the difference image a pattern
intensity function. Specifically, the difference image
I.sub.diff(i,j) is formed by subtracting a corresponding pixel
value of the pre-operative DRR image from each pixel value of the
intra-operative x-ray projection image, within the ROI:
I.sub.diff(i,j)=I.sub.Live(i,j)-I.sub.DRR(i,j) (4)
[0095] In equation (4), I(i,j) represents the image intensity value
of a pixel located at the i-th row and j-th column of each pixel
array for the respective image. Specifically, I.sub.diff(i,j)
represents an array of pixel values for a difference image formed
by subtracting the corresponding pixel values of the second image
from each pixel value of the first image. I.sub.live(i,j)
represents the (i,j)-th pixel value of the first image of the
object. I.sub.DRR(i,j) represents the (i,j)-th pixel value of the
second image of the object. The similarity measure operates on this
difference image, and is expressed as the summation of asymptotic
functions of the gradients of the difference image over the pixels
within a neighborhood R:
i , j k , l R .sigma. 2 .sigma. 2 + ( I diff ( i , j ) - I diff ( i
+ k , j + l ) ) 2 ( 5 ) ##EQU00003##
[0096] In equation (5) above, the constant .sigma. is a weighting
coefficient for the pattern intensity function. The sensitivity of
the solution to the variation of x-ray image can be minimized by
careful selection of this constant. The larger the weighting
coefficient, the more stable the results. However, the choice of
.sigma. entails a tradeoff between stability and accuracy. When the
value of .sigma. is too large, some small details in the images
cannot be reflected in the similarity measure. Based on the
experiments, the empirical value for .sigma. is in the range from
about 4 to about 16, in one embodiment.
[0097] FIG. 8 schematically illustrates a neighborhood R for
calculating a similarity measure based on pattern intensity. As
seen from FIG. 8, the neighborhood R in the illustrated embodiment
is defined so that the gradients of the difference image can be
considered in at least four directions (horizontal, vertical,
45.degree. diagonal and -45.degree. diagonal). When the
neighborhood R is defined in this manner, equation (5) for the
similarity measure becomes:
i , j .sigma. 2 .sigma. 2 + ( ( I diff ( i , j ) - I diff ( i , j -
1 ) ) 2 + i , j .sigma. 2 .sigma. 2 + ( ( I diff ( i , j ) - I diff
( i - 1 , j ) ) 2 i , j .sigma. 2 .sigma. 2 + ( ( I diff ( i , j )
- I diff ( i - 1 , j - 1 ) ) 2 + i , j .sigma. 2 .sigma. 2 + ( ( I
diff ( i , j ) - I diff ( i - 1 , j + 1 ) ) 2 . ( 6 )
##EQU00004##
[0098] Equations (5) and (6) for pattern intensity have several
advantages. First, the difference image filters out the low
frequency part that predominantly consists of the soft tissues, and
keeps the high frequency part that predominantly consists of the
skeletal structures. This feature makes the algorithm robust to
some brightness intensity difference between live and DRR images.
Second, because of the asymptotic function, the measure is less
affected by the pixels whose intensity value slightly deviates from
its neighboring pixels. These types of pixels are thought to
contain random noise. Third, because the asymptotic function
quickly approaches to zero when the variable increases, large
intensity differences such as image artifacts have the same effects
on the similarity measure regardless of their magnitude. Due to
this feature, the pattern intensity is less sensitive to image
artifacts.
[0099] The estimation of local motion fields using block matching
together with hierarchical mesh motion estimation, as well as the
reconstruction of the full motion field from the plurality of
locally estimated motion fields, are performed in steps 420-450 of
the flowchart shown in FIG. 5. Fast generation of the full motion
field is achieved by using hierarchical mesh tracking, and using
SIMD (single instruction multiple data) technology to perform image
computation in parallel.
[0100] In one embodiment, a global translation of the entire image
(measured as a translation of the image center of the image) is
first estimated, then used as the initial estimates for all further
local motion estimation. In other words, a rough estimate is made
of the center displacement for the entire image, and is used as the
starting estimate for all local displacements. Referring back to
FIG. 5, the first step (indicated with reference numeral 420 in
FIG. 5) in generating a full motion field for a target, between the
pre-operative scan and the intra-operative treatment, is the step
of estimating a global translation for the entire image, or
equivalently, estimating the center displacement of the image.
[0101] FIG. 9 illustrates the estimation of global motion (in this
case, translation only), between the image center of a DRR and the
image center of a corresponding x-ray image. In the illustrated
embodiment, the image center is used as the block center. The step
of global translation estimation is very important, because any
failure during this step will affect the rest of the local motion
estimation process. To prevent any possibility of mismatching, a
very large image block is used in the illustrated embodiment. The
maximum tracking range can be calculated as the difference between
the block size and the entire image size. For example, if the
matching size is 80.times.80 mm, the maximum tracked translation is
60 mm. In the embodiment illustrated in FIG. 9, a block having a
size of 160.times.160 pixels (64 mm.times.64 mm) is used. The
search window in the illustrated embodiment is the entire image.
The maximum track range for the illustrated embodiment is (-50 mm,
+50 mm).
[0102] After global motion estimation, the next step 430 (see FIG.
5) is mesh motion estimation. In this step, a hierarchical 2D mesh
structure is designed in order to estimate local motion in multiple
levels. As known, a 2D mesh (or a 2D mesh grid) refers to a
tesselation of a 2D region into polygonal patches or elements,
whose vertices are called mesh nodes. Unlike block matching
algorithms, which generally assume only translational motion, 2D
mesh models allow for spatial transformations to model rotations,
scalings, and deformations of the object that was imaged, in
addition to translations of the object. Compared to block matching
algorithms, therefore, mesh-based methods may produce a more
accurate representation of the motion field, for example may
generate continuously varying motion fields.
[0103] FIG. 10 schematically illustrates a mesh grid 300
established for a DRR of a target region, and a corresponding mesh
grid 302 established for an x-ray image of the target region, in an
embodiment in which the target is located within the cervical
region of the spine. With a 2D mesh, motion compensation within
each mesh element or patch may be accomplished by deriving a
spatial transformation between the images, where the transformation
parameters are computed from the nodal motion vectors, i.e. from
the motion vectors that are estimated for the mesh nodes that are
located at the vertices of the mesh. In other words, mesh-based
motion estimation consists of finding a spatial transformation that
best maps one set of mesh elements in a first image acquisition
onto another set of mesh elements in a second image
acquisition.
[0104] In particular, mesh motion estimation consists of finding
the vertices of corresponding mesh elements in the other image,
i.e. finding the corresponding mesh nodes in the other image, such
that errors are minimized in the overall motion field. Typically, a
number of mesh nodes are selected in one image, and the
corresponding mesh nodes in the other image are estimated. For any
pixel located within a mesh element (as opposed to being located on
the vertices of the mesh elements), the mapping between different
image acquisitions is performed through interpolation. The local
motion vectors for such pixels are estimated by interpolating from
the nodal motion vectors that were estimated for the mesh nodes
that surround the pixel.
[0105] In one embodiment, hierarchical mesh motion estimation may
be performed. By hierarchical mesh motion estimation, it is meant
that nodal motion is estimated for the mesh nodes that define the
mesh structure, for each of a plurality of mesh resolution levels.
Motion estimation performed with a coarse mesh provides the
initialization for the subsequent (finer) resolution levels of the
mesh. To estimate the motion of each mesh node, multi-level block
matching may be performed.
[0106] FIG. 11 illustrates a mesh hierarchy, during mesh motion
estimation. As seen from FIG. 11, the mesh hierarchy starts from a
relatively coarse mesh, 320, and progresses onto finer meshes,
illustrated as 322 and 324. Using the global translations
(estimated in step 420 of FIG. 5 as the initial estimates), nodal
motion for the mesh nodes located at the vertices of the most
coarse mesh is first calculated. These estimates are then passed
onto the subsequent, finer mesh. At each level, nodal motion is
updated, using a smaller search range. Finally, the motion vectors
for the mesh nodes at the final one of the mesh resolution levels
(characterized by the finest mesh resolution level) are refined.
For all the nodes, multi-level block matching with multiple
candidates is used, together with the pattern-intensity based
similarity measure, given in equations (5) and (6).
[0107] FIG. 12 schematically illustrates the passing on of node
estimation, from a coarse mesh resolution level onto a finer mesh
resolution level. At each mesh resolution level after the first
level, the mesh nodes include both 1) mesh nodes generated at a
previous mesh resolution level; and 2) mesh nodes that are newly
added at the current mesh resolution level. In the illustrated
embodiment, the initial estimates for nodal motion vectors, for the
newly added nodes at the current mesh, are obtained by linear
interpolation of the existing nodal motion vectors, at the previous
mesh resolution level. During this process, any unreliable mesh
node needs to be detected, so that only reliable nodes are passed
onto the subsequent mesh level.
[0108] FIG. 12 illustrates how such a detection can be performed,
using a mesh node referred to in FIG. 12 as `node 5.` In the
illustrated embodiment, the difference between the motion vector
(in this case, translation vector) of node 5, and the median
motions (translations) computed from its 9 surrounding nodes (nodes
1-4, 6-9 in FIG. 12) is taken. As seen from FIG. 12, the
translation of node 2 is the average of the translations of node 1
and node 3; the translation of node 4 is the average of the
translations of node 1 and node 7; the translation of node 6 is the
average of the translations of node 3 and node 9; and the
translation of node 8 is the average of the translations of node 7
and node 9. The translation of node 5 is the average of the
translations of nodes 1, 3, 7, and 9. If the difference between the
translation of node 5 and the median translations computed from its
9 neighboring nodes is less than a predefined threshold, the node 5
is considered as a reliable node. Otherwise, it is considered as an
unreliable node, and its translations are replaced with the median
values and passed to the subsequent mesh.
[0109] For most mesh nodes, the estimates of motion are reliable
and accurate. For a few nodes where mismatching may occur and the
estimation may not be reliable, the displacements need to be
reconstructed by the surrounding node displacements. Accordingly,
the next step in the registration algorithm flow chart in FIG. 5 is
step 440 of motion field reconstruction, during which the motion
field is reconstructed from surrounding nodes, for those nodes in
which mismatching occurs. The inaccurate nodal motion vectors can
be detected by using 3.times.3 median filtering.
[0110] Local motion estimation relies on the local image content.
In some smooth local regions, mismatching may occur. During mesh
motion estimation, the estimation in most nodes is pretty accurate.
For a few nodes where mismatching occurs, the motions should be
reconstructed from their surrounding nodes. What is known a priori
is matter coherence of bone and tissue, and accordingly, the
smoothness of local motion. In other words, the estimated local
motion vectors are thought to be smooth and continuous, because of
matter coherence. By imposing this physically-based smoothness
constraint, a cost function is formulated to reconstruct the motion
field.
[0111] In one embodiment, the cost function is expressed
mathematically as follows:
E(d)=.intg..intg..beta.(d-u).sup.2dxdy+.lamda..intg..intg.(d.sub.,x.sup.-
2+d.sub.,y.sup.2)dxdy (7)
In equation (7) above, E(d) represents the cost function, d
represents a desired local estimate for a nodal motion vector at
coordinates (x,y), u represents a locally estimated nodal motion
vector at coordinates (x,y), and .beta. represents a reliability
constant that ranges from 0 to 1, where .beta.=0 indicates an
unreliable estimation, and 13=1 indicates a reliable
estimation.
[0112] By performing a finite difference of the derivatives over
the mesh grids, a discretized form for the cost function in
equation (7) is expressed as:
E(d.sub.i,j)=.SIGMA..SIGMA..beta..sub.i,j(d.sub.i,j-u.sub.i,j).sup.2+.la-
mda..SIGMA..SIGMA.[(d.sub.i,j-d.sub.i-1,j).sup.2+(d.sub.i,j-d.sub.i,j-1).s-
up.2] (8)
where u.sub.i,j represents the locally estimated translations,
d.sub.i,j is the local motion desired, .beta..sub.i,j=1 if the
estimation is reliable and .beta..sub.i,j=0 if the estimation is
unreliable. The first term on the right side of equation (8)
reflects the fidelity to the observed data in the reconstruction.
The second term imposes the smoothness constraints on the motion
field in two spatial directions.
[0113] The minimization of the cost function given by equation (8)
results in a system of simultaneous linear equations
.delta. E ( d i , j ) .differential. d i , j = ( .beta. i , j + 4
.lamda. ) d i , j - .lamda. ( d i - 1 , j + d i + 1 , j + d i , j -
1 + d i , j + 1 ) - .beta. i , j u i , j = 0 ( 9 ) ##EQU00005##
In one embodiment, the iterative algorithm of successive-over
relaxation (SOR), which is fast and convergent, is used to solve
the equations:
d.sub.i,j.sup.(n+1)=d.sub.i,j.sup.(n)-.omega.[(.beta..sub.i,j+4.lamda.)d-
.sub.i-1,j.sup.(n)-.lamda.(d.sub.i-1,j.sup.(n)+d.sub.i-1,j.sup.(n)+.sub.i,-
j-1.sup.(n)+d.sub.i,j+1.sup.(n))-.beta..sub.i,ju.sub.i,j]/(.beta..sub.i,j+-
4.lamda.) (10)
[0114] Once all the nodal motion vectors have been estimated at all
the mesh nodes, the translations for any point (or pixel) inside
the ROI can be computed by interpolation. FIG. 13 schematically
illustrates the determination of a motion vector for a point of
interest, by interpolation from surrounding nodes. In the
illustrated embodiment, quadratic interpolation is performed, using
the 9 nearest nodes, and 9 shape functions are used.
[0115] Assuming the motion vector (dx(i),dy(i)) for nine nodes, the
motion vector (dx,dy) at the point of interest is computed using
the following expressions:
dx = i = 1 9 N ( i ) dx ( i ) , dy = i = 1 9 N ( i ) dy ( i ) , (
11 ) ##EQU00006##
where N(i) is the shape function for the node (i), and where N(i)
for I=1, 2, . . . 9 are given as follows:
N(1)=(1-.xi.)(1-.eta.)/4-(N.sub.8+N.sub.5)/2,
N(2)=(1-.xi.)(1-.eta.)/4-(N.sub.5+N.sub.6)/2,
N(3)=(1+.xi.)(1+.eta.)/4-(N.sub.6+N.sub.7)/2,
N(4)=(1-.xi.)(1+.eta.)/4-(N.sub.7+N.sub.8)/2,
N(5)=(1-.xi..sup.2)(1-.eta.)/2,
N(6)=(1-.xi.)(1-.eta..sup.2)/2,
N(7)=(1-.xi..sup.2)(1+.eta.)/2,
N(8)=(1-.xi.)(1-.eta..sup.2)/2,
N(9)=(1-.xi..sup.2)(1-.eta..sup.2)/2 (12)
[0116] Using steps 420, 430, and 440, described above, the local
motion vectors can be estimated for a plurality of points of
interest within the ROI. The full motion field is obtained as a
composite or superposition of all of the local motion vectors that
are estimated for the many points of interest that have been
selected for motion estimation. FIG. 14 schematically illustrates,
in vectorial form, a full motion field (reconstructed from many
estimated local motion vectors), in an embodiment in which the
target is located within the cervical region of the spine.
[0117] The final step in the image registration process is target
localization, namely deriving the target translations and rotations
from the full motion field that has been determined. In one
embodiment, non-rigid image registration seeks to determine a
projection mapping or transformation between different coordinate
systems in respective image acquisitions such that points in each
space which correspond to the same anatomical point are mapped to
each other. In one embodiment, the transformation is represented by
a set of non-rigid transformation parameters (dx.sub.T, dy.sub.T,
dz.sub.T, r, p, w), where (dx.sub.T, dy.sub.T, dz.sub.T) represent
the translations of the target, and (r, p, w) represent rotations
of the target.
[0118] In one embodiment, two orthogonal x-ray projections are used
to solve for these six parameters. In this embodiment, the
registration in each projection is performed individually, and the
results of the registration for each projection are subsequently
combined, to obtain the six 3D transformation parameters. In other
embodiments, however, different projections or combinations thereof
may be used to solve for the transformation parameters.
[0119] FIG. 15 illustrates the geometric relations between a
three-dimensional treatment target, and two orthogonal 2D x-ray
projections (labeled A and B in FIG. 15), in a non-rigid image
registration method in accordance with one embodiment. A pair of
cameras (or image receivers) A and B receive their x-ray
projections from respective x-ray sources (not shown). In the
coordinate system of the 3D scan, the x-axis is directed inward
into the paper, and is not indicated in FIG. 15. As explained
above, the change in position of the treatment target is
represented by three translations and three global rigid rotations
(dx, dy, dz, r, p, w).
[0120] In FIG. 15, the orthogonal 2D projections A and B are viewed
from the directions o.sub.As.sub.A and o.sub.Bs.sub.B,
respectively. For each of the projections A and B, FIG. 15
illustrates respective 2D planar coordinate systems that are fixed
with respect to the image plane that characterizes each projection.
The image planes A and B for the projections A and B are thus
defined by mutually orthogonal axes within the respective
coordinate systems. These axes are shown in FIG. 15 as
(x.sub.A,y.sub.A) for projection A, and (x.sub.B, y.sub.B) for
projection B. The direction of the axis x.sub.A in the 2D
coordinate system for projection A, and the direction of the x-axis
in the 3D scan coordinate system, are opposite with respect to each
other. The direction of axis x.sub.B in the coordinate system for
projection B, and the direction of the axis x in the 3D scan
coordinate system, are the same.
[0121] For projection A, the 2D motion field (dx.sub.A, dy.sub.A)
is estimated by registering the x-ray image that is projected onto
the image plane A, with the corresponding reference DRR image. For
projection B, the 2D motion field (dx.sub.B, dy.sub.B) is estimated
by registering the x-ray image that is projected onto the image
plane B, with the corresponding reference DRR image. Given the 2D
motion fields (dx.sub.A, dy.sub.A) for projection A, and (dx.sub.B,
dy.sub.B) for projection B, the 3-D target translation
(dx.sub.T,dy.sub.T,dz.sub.T), as well as the global rigid rotations
(r, p, w), can be obtained for both projections A and B, by a
straightforward mathematical operation.
[0122] Referring back to FIG. 5, the 3-D target translation
(dx.sub.T,dy.sub.T,dz.sub.T) can easily be obtained in step 455
(shown in FIG. 5), given the 2D local motion fields (dx.sub.A,
dy.sub.A) for projection A, and (dx.sub.B, dy.sub.B) for projection
B, using the following expressions:
dx.sub.T=(dx.sub.TA+dx.sub.TB)/2,
dy.sub.T=(dy.sub.TA-dy.sub.TB)/ {square root over (2)},
dz.sub.T=(dy.sub.TA+dy.sub.TB)/ {square root over (2)} (13)
[0123] The global rigid rotations (r, p, w) can be calculated from
the motion fields (dx.sub.A, dy.sub.A) in projection A and
(dx.sub.B, dy.sub.B) in projection B. Using the target as the
rotation center, global rigid rotations are useful for position and
rotation correction and compensation during initial patient
alignment and treatment. Because the target translation is already
calculated, the calculation of the global translation is not
needed. To get the three rotations in 3D patient coordinates, three
2D in-plane rotations are first computed, including the in-plane
rotations .theta..sub.A and .theta..sub.B in projections A and B,
respectively, and the in-plane rotation .theta..sub.x in a plane
perpendicular to the inferior-superior axis. Approximately, the
global rotations can be expressed as:
r=.theta..sub.x,
p=(.theta..sub.B-.theta..sub.A)/ {square root over (2)},
w=(.theta..sub.B+.theta..sub.A)/ {square root over (2)}, (14)
[0124] Estimation of .theta..sub.A and .theta..sub.B is directly
based the 2D motion fields in projections A and B, respectively. To
estimate .theta..sub.x, a plane is first defined, which passes the
target point and is perpendicular to the axis x in the 3D patient
coordinate system. Then the motion field is calculated from the two
motion fields (x.sub.A, y.sub.A) and (x.sub.B, y.sub.B) in
projections A and B, respectively.
[0125] Assuming (dx, dy) is the motion field in the corresponding
coordinate (x, y) and .theta. is the global rotation, when the
rotation is small (<10.degree.), the following transformation
equation is valid:
{ dx dy } = [ 0 - .theta. .theta. 0 ] { x y } ( 15 )
##EQU00007##
[0126] Given (dx,dy) and (x,y) in many points, .theta. can be
easily calculated using least square minimization method
.theta. = i ( x ( i ) dy ( i ) - y ( i ) dx ( i ) ) i ( x ( i ) x (
i ) + y ( i ) y ( i ) ) ( 16 ) ##EQU00008##
Using equations (14) and (16) above, the average rigid
transformation parameters can be obtained, in step 160 illustrated
in FIG. 5.
[0127] Using the results of non-rigid registration, obtained as
described above, dynamic tracking of the targets can be performed
during treatment delivery, by using the 4D mathematical model, and
by monitoring the breathing (or other motion) cycle during delivery
of radiation. FIG. 16 schematically illustrates dynamic tracking of
a moving target within a breathing patient during delivery of
treatment radiation, using surface markers 340 (e.g. infrared LEDs)
to monitor the breathing motion of the patient as a function of
time. Although surface markers are described with respect to the
embodiment illustrated in FIG. 16, in other embodiments other
techniques may be used to track the surface of the patient. These
techniques include, but are not limited to: video systems; laser
scanners; ultrasound (or other acoustic) scanners; and when
tracking heartbeat motion, electrocardiograms. Any method and
device known in the art to track patient surfaces may be used.
[0128] Dynamic tracking of the target 210 (i.e. the tumor/lesion
210) during treatment delivery can achieved by combining the 4D
mathematical model obtained during 4D treatment planning, with the
registration information provided by fiducial-less tracking, and
monitoring of the patient breathing motion using surface markers.
As explained above, the 4D mathematical model obtained during
treatment planning relate the locations of the skeletal structures
230 and 232 to the locations of the target 210.
[0129] A number of approaches to dynamic tracking are possible,
when using fiducial-less tracking in association with 4D planning
and dynamic tracking of surface markers, as schematically shown in
FIG. 16. In a first approach, the surface markers 340 are first
used to determine the instant in the breathing cycle. The rigid
reference structures 232 can then be located, using non-rigid
fiducial-less tracking. The location of the tumor/lesion 210 is
then tracked, by drawing vectors from the reference locations
determined using non-rigid image registration. The knowledge of the
instant in the breathing cycle, obtained using the surface markers
340, and the models obtained from 4D treatment planning that relate
the locations of the target 210 to the locations of the rigid
skeletal structures 232, are used to draw vectors from the rigid
reference structures 232 whose locations have been obtained during
non-rigid image registration.
[0130] In a second approach, the tumor/lesion 210 is located, using
the location of the reference structures 232 and 230 obtained from
fiducial-less tracking, and using a model that relates the location
of the reference structures 232 and 230 to the lesion 210, obtained
from 4D treatment planning. Next, a mathematical model is built
that relates the motion of the surface markers 340 to the motion of
the lesion 210. In image processing, it is known in the art to
relate the motion of a first type of object with the motion of a
second type of object, and to describe such a relation in terms of
a mathematical model. Algorithms or software that are known and
that may be commercially available can be used to built the
mathematical model that relates the motion of the surface markers
340 to the motion of the lesion 210.
[0131] In a third approach, the locations of the reference
structures that are determined from the 4D model obtained during 4D
treatment planning are used to locate the lesion by interpolation.
A mathematical model is then built that relates the motion of the
surface markers 340 to the motion of the lesion 210. This third
approach involves the least dependence on the 4-D planning model
obtained from treatment planning.
[0132] In a final approach, the tumor or lesion 210 is directly
tracked, using the 2D/3D registration techniques described in the
above paragraphs. In this approach, the model relating the motion
of surface markers to the location of the target 210 can be built
directly, using just the results of the 2D/3D registration.
[0133] Once the targets have been located, using one of the
approaches described above, radiation beam delivery can be
implemented. The real time locations of the targets within the
moving anatomical region, which are determined as described in the
previous paragraphs, provide guidance for beam delivery. During
treatment planning, the beam trajectories are initially defined
with respect to a nominal patient co-ordinate system, perhaps
chosen to orient with respect to one of the several CT studies
acquired to cover the motion cycle. This epoch in the motion cycle
is determined by analyzing the motion of the surface markers, and
each radiation beam is to be turned on from this epoch.
[0134] FIG. 17 provides a schematic block diagram of an apparatus
500 for dynamically tracking targets 210 within an anatomical
region that is undergoing periodic motion having a cycle P, and for
delivering therapeutic radiation to the moving targets. The targets
may include tumors or lesions. The anatomical region includes
reference structures in addition to the targets 210. The reference
structures may include rigid reference structures 232 which do not
move during the periodic motion, and reference structures 230 which
themselves move during the periodic motion of the anatomical
region.
[0135] In overview, the apparatus 500 includes: a target locater
510 that determines in real time the locations of the target(s) 210
relative to the reference structures 230 or 232 within the
periodically moving anatomical region; a 4D treatment planning
generator 520 that generates a 4D treatment plan as a function of
the relative position in time within P for the moving anatomical
region; and a treatment beam generator 524 that delivers
therapeutic radiation to the targets in real time in accordance
with the treatment plan. The treatment plan prescribes a desired
radiation dose distribution to be delivered in real time to the
targets, while accounting for a deformation of the moving
anatomical region during the periodic motion. The treatment
planning generator 520 may be connected to a treatment delivery
controller (not shown) which controls the delivery of radiation, in
accordance with the treatment plan generated by the treatment
planning generator 520.
[0136] Also included may be a robot system (not shown), which
typically has a fixed base and an articulated arm assembly at the
distal end of which the treatment beam generator (e.g. x-ray source
such as a linac) may be mounted. The robot system may move (and
orient), in response to the directions of the delivery controller,
the treatment beam generator (i.e. x-ray linac). The robot system
and treatment beam generator are described in detail, for example
in commonly owned U.S. Pat. No. 5,207,223, and U.S. patent
application Ser. No. 10/814,451 characterized by attorney docket
No. ACCL-120, both incorporated by reference herein in their
entireties.
[0137] In one embodiment, the target locater 510 includes a 3D
scanner 520; a DRR generator 530; an x-ray imaging system 540; an
image registration subsystem 550; one or more motion sensors 560; a
4D model generator 570; and a target location computer 580. The 3D
scanner 520 generates a plurality of 3D images I.sub.j (j=1, . . .
, p) of the anatomical region, at each of a succession of time
points t.sub.j (j=1, . . . , p) within the cycle P. These 3D images
may include, but are not limited to: a 3D CT scan; a 3D MRI scan; a
3D PET (positron emission tomography) scan; and a 3D ultrasound
scan. The 3D scanner 520 can therefore be one of: a 3D CT scanner;
a 3D PET scanner; a 3D MRI scanner; and a 3D ultrasound
scanner.
[0138] The time points t.sub.j (j=1, . . . , p) are preferably
chosen to substantially encompass a full range of the periodic
motion of the anatomical region. For example, the time points may
include: a first time point corresponding to a peak of the cycle P,
a second time point corresponding to a trough of the cycle P, and a
third time point disposed at an intermediate location between the
peak and the trough of the cycle P. The motion sensors 560, which
may be surface markers, for example, monitor the periodic motion of
the anatomical region, and measure the cycle P. In this way, the
motion sensors 560 generate time position data representative of
the relative position within P of one or more desired time
points.
[0139] The DRR generator 530 reconstructs DRRs from the 3D scan
data, at each time point t.sub.j, by casting hypothetical rays
through the volumetric 3D scan data from a known beam projection
geometry, and integrating the 3D scan data along each ray. The
x-ray imaging system 540 generates near real time 2D x-ray
projection images of the targets 210 and the reference structures
230 and 232 within the moving anatomical region, by detecting x-ray
imaging beams after the beams have traversed at least a portion of
the anatomical region. These x-ray imaging beams are generated from
the same beam projection geometry as used to generate the DRRs.
[0140] The image registration subsystem 550 registers the near real
time x-ray projection images of the reference structures and/or the
targets, with the DRRs of the reference structures and/or the
targets, thereby determining the locations of the reference
structures and/or the targets. In one embodiment, the image
registration subsystem 550 includes: 1) an RO1 selector 620
configured to select an RO1 (region of interest) within the DRR,
the ROI containing the treatment target and preferably at least one
reference structure; 2) an image enhancer 630 configured to enhance
the DRRs and the x-ray images by applying a filter operator to the
DRR and to the x-ray image;
3) a similarity measure calculator 640 configured to determine a
measure of similarity between the DRR and the x-ray image; 4) a
motion field generator 650 configured to generate a 3D full motion
field by estimating, for each of a plurality of resolution levels,
one or more 2D local motion fields within the RO1, using the
similarity measure; and 5) a parameter determiner 660 configured to
determine a set of non-rigid transformation parameters that
represent the difference in the position and orientation of the
treatment target as shown in the x-ray image, as compared to the
position and orientation of the treatment target as shown in the
DRR, from the 3D full motion field.
[0141] The 4D model generator 570 generates a 4D model that
describes a motion of the targets 210 relative to the reference
structures 232 within the moving anatomical region, as a function
of the relative position in time within the cycle P. The target
location computer 580 computes the locations of the targets at the
one or more desired time points. The target location computer 580
uses the 4D model constructed by the 4D model generator 570, to
correlate the locations of the targets with the known locations of
the reference structures, as determined by the image registration
subsystem 550, and uses the time position data obtained by the
motion sensors 560 to determine the relative position within P of
each desired time point.
[0142] In one embodiment, the 4D model generator 570 includes a
deformation model constructor 575 configured to construct a
mathematical model that describes the deformation and motion of the
anatomical region, as a function of the relative position in time
within the cycle P. In this embodiment, the 4D model generator 570
derives the 4D model from the mathematical model constructed by the
deformation model constructor 575. The deformation model
constructor 575 extracts, from the plurality of images I.sub.j
generated by the 3D scanner 520, deformation data that contain
information relating to the deformation and motion of the
anatomical region.
[0143] In one embodiment, the deformation model constructor 575
extracts the deformation data from the plurality of images by
registering each image that is taken at a time point t.sub.j within
P, onto a consecutive image that is taken at a consecutive time
point t.sub.j+1 within P. The information contained in the
deformation data comprises information relating to the change in
the position and orientation of the targets relative to the
reference structures. The deformation model constructor 575 uses
the deformation data, together with the time position data from the
motion sensors 560 to mathematically correlate, for each time point
t.sub.j, the relative position within P of the time point t.sub.j
with the deformation and motion of the targets at the time point
t.sub.j.
[0144] Using the 4D mathematical model generated by the 4D model
generator 570, the results of non-rigid image registration as
performed by the image registration subsystem 550, and the relative
position in time within the breathing cycle as determined by the
motion sensors 560, the target location computer 580 computes the
locations of the tumors/lesions.
[0145] Using the 4D mathematical model generated by the 4D model
generator 570, the 4D treatment plan generator 520 generates a
desired radiation dose distribution that results from continuous
radiation beam delivery through the non-rigidly moving anatomical
region. Finally, the treatment beam generator 524 generates and
treatment beams in accordance with the desired radiation dose
distribution, and delivers them in real time to the targets.
[0146] In sum, a number of techniques have been described for
dynamically tracking tumors/lesions in the anatomy that move, for
example due to periodic motion such as respiration. These
techniques combine 4D treatment planning, fiducial-less tracking of
skeletal structures or targets, and dynamic tracking of surface
markers with pertinent mathematical models, to achieve dynamic
tracking of the tumors/lesions of interest.
[0147] While the invention has been particularly shown and
described with reference to specific embodiments, it should be
understood by those skilled in the art that many modifications and
variations in form and detail may be made in the techniques and
structures described and illustrated herein, without departing from
the spirit and scope of the invention. Accordingly, the techniques
and structures described and illustrated herein should be
understood to be illustrative only and not limiting upon the scope
of the present invention. The scope of the present invention is
defined by the claims, which includes known equivalents and
unforeseeable equivalents at the time of filing of this
application.
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