U.S. patent application number 11/731146 was filed with the patent office on 2008-05-29 for automatic selection of multiple collimators.
Invention is credited to John W. Allison, Calvin R. Maurer, Jay B. West.
Application Number | 20080123813 11/731146 |
Document ID | / |
Family ID | 39149312 |
Filed Date | 2008-05-29 |
United States Patent
Application |
20080123813 |
Kind Code |
A1 |
Maurer; Calvin R. ; et
al. |
May 29, 2008 |
Automatic selection of multiple collimators
Abstract
Systems and methods for automatically determining a beam
parameter at each of a plurality of treatment nodes are disclosed.
The beam parameter may include a beam shape, beam size and/or beam
orientation. Systems and methods for automatically selecting
multiple collimators in a radiation treatment system are also
disclosed.
Inventors: |
Maurer; Calvin R.; (Mountain
View, CA) ; West; Jay B.; (Mountain View, CA)
; Allison; John W.; (Los Altos, CA) |
Correspondence
Address: |
Jennifer Hayes;BLAKELY, SOKOLOFF, TAYLOR & ZAFMAN LLP
Seventh Floor, 12400 Wilshire Boulevard
Los Angeles
CA
90025
US
|
Family ID: |
39149312 |
Appl. No.: |
11/731146 |
Filed: |
March 30, 2007 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
60790503 |
Apr 7, 2006 |
|
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|
Current U.S.
Class: |
378/96 |
Current CPC
Class: |
G21K 1/046 20130101;
A61N 5/1036 20130101; A61N 5/103 20130101; A61N 5/1042
20130101 |
Class at
Publication: |
378/96 |
International
Class: |
H05G 1/28 20060101
H05G001/28 |
Claims
1. A method comprising: automatically selecting multiple
collimators to deliver radiation at a plurality of treatment nodes;
and automatically calculating a beam duration corresponding to a
radiation dose to be delivered at each of the plurality of
treatment nodes by the multiple collimators.
2. The method of claim 1, wherein automatically selecting multiple
collimators comprises automatically determining a collimator
size.
3. The method of claim 1, wherein automatically selecting multiple
collimators comprises automatically determining a collimator
shape.
4. The method of claim 1, further comprising automatically
determining an orientation of each of the multiple collimators.
5. The method of claim 1, wherein one or more of the multiple
collimators is a fixed aperture collimator.
6. The method of claim 1, wherein one or more of the multiple
collimators is an iris collimator.
7. A system comprising: means for automatically selecting multiple
collimators to deliver radiation at a plurality of treatment nodes;
and means for automatically calculating a beam duration
corresponding to a radiation dose to be delivered at each of the
plurality of treatment nodes by the multiple collimators.
8. The system of claim 7, wherein one or more of the multiple
collimators is a fixed aperture collimator.
9. The system of claim 7, wherein one or more of the multiple
collimators is an iris collimator.
10. An apparatus comprising: a radiation beam treatment system
having a plurality of collimators to deliver a radiation beam to a
treatment site; and a radiation treatment planning system
operatively coupled to the radiation beam treatment system, the
radiation treatment planning system to automatically select
multiple collimators of the plurality of collimators to deliver
radiation at a plurality of treatment nodes and automatically
calculate a beam duration corresponding to a radiation dose to be
delivered at each of the plurality of treatment nodes by the
multiple collimators.
11. The apparatus of claim 10, wherein one or more of the plurality
of collimators is a fixed aperture collimator.
12. The apparatus of claim 10, wherein one or more of the plurality
of collimators is an iris collimator.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application claims priority to U.S. Provisional Patent
Application No. 60/790,503, filed on Apr. 7, 2006, the entirety of
which is hereby incorporated by reference.
FIELD
[0002] Embodiments of the present invention relate generally to
radiation treatment and, more particularly, to treatment planning
in radiation treatment.
BACKGROUND
[0003] Tumors and lesions are types of pathological anatomies
characterized by abnormal growth of tissue resulting from the
uncontrolled, progressive multiplication of cells, while serving no
physiological function. Pathological anatomies can be treated with
an invasive procedure, such as surgery, but this can be harmful and
full of risks for the patient. A non-invasive method to treat a
pathological anatomy (e.g., tumor, legion, vascular malformation,
nerve disorder, etc.) is external beam radiation therapy. In one
type of external beam radiation therapy, an external radiation
source is used to direct a sequence of x-ray beams at a tumor site
from multiple angles, with the patient positioned so the tumor lies
in the path of the beam. As the angle of the radiation source
changes, every beam passes through the tumor site, but passes
through a different area of healthy tissue on its way to the tumor.
As a result, the cumulative radiation dose at the tumor is high and
the average radiation dose to healthy tissue is low.
[0004] The term "radiotherapy" refers to a radiation treatment
procedure in which radiation is applied to a target region for
therapeutic, rather than necrotic, purposes. The amount of
radiation utilized in radiotherapy treatment sessions is typically
about an order of magnitude smaller, as compared to the amount used
in a radiosurgery session. Radiotherapy is typically characterized
by a low dose per treatment (e.g., 100-200 centiGray (cGy)), short
treatment times (e.g., 10 to 30 minutes per treatment) and
conventional or hyperfractionation (e.g., 30 to 45 days of
treatment). For convenience, the term "radiation treatment" is used
herein to mean radiosurgery and/or radiotherapy unless otherwise
noted by the magnitude of the radiation.
[0005] In order to deliver a requisite dose to a targeted region,
whilst minimizing exposure to healthy tissue and avoiding sensitive
critical structures, a suitable treatment planning system is
required. Treatment plans specify quantities such as the directions
and intensities of the applied radiation beams, and the durations
of the beam exposure. It is desirable that treatment plans be
designed in such a way that a specified dose (required for the
clinical purpose at hand) be delivered to a tumor, while avoiding
an excessive dose to the surrounding healthy tissue and, in
particular, to any important nearby organs. Developing an
appropriate treatment planning system is especially challenging for
tumors that are larger, have irregular shapes, or are close to a
sensitive or critical structure.
[0006] A treatment plan may typically be generated from input
parameters such as beam positions, beam orientations, beam shapes,
beam intensities, and desired radiation dose constraints (that are
deemed necessary by the radiologist in order to achieve a
particular clinical goal). Sophisticated treatment plans may be
developed using advanced modeling techniques, and state-of-the-art
optimization algorithms.
[0007] Two kinds of treatment planning procedures are known:
forward planning and inverse planning. In the early days of
radiation treatment, treatment planning systems tended to focus on
forward planning techniques. In forward treatment planning, a
medical physicist determines the radiation dose duration, or
beam-on time, and trajectory of a chosen beam and then calculates
how much radiation will be absorbed by the tumor, critical
structures (i.e., vital organs) and other healthy tissue. There is
no independent control of the dose levels to the tumor and other
structures for a given number of beams, because the radiation
absorption in a volume of tissue is determined by the properties of
the tissue and the distance of each point in the volume to the
origin of the beam and the beam axis. More specifically, the
medical physicist may "guess" or assign, based on his experience,
values to various treatment parameters such as beam positions and
beam intensities. The treatment planning system then calculates the
resulting dose distribution. After reviewing the resulting dose
distribution, the medical physicist may adjust the values of the
treatment parameters. The system re-calculates a new resulting dose
distribution. This process may be repeated, until the medical
physicist is satisfied by the resulting dose distribution, as
compared to his desired distribution. Forward planning tends to
rely on the user's ability to iterate through various selections of
beam directions and dose weights, and to properly evaluate the
resulting dose distributions. The more experienced the user, the
more likely that a satisfactory dose distribution will be
produced.
[0008] Forward planning often utilizes an isocentric treatment
process in which an external radiation source is used to direct a
sequence of x-ray beams at a tumor target from multiple angles,
with the patient being positioned so the tumor is at the center of
rotation (isocenter) of the beams. In isocentric planning, each
available beam is targeted at the same point to form the
"isocenter," which generally may be a roughly spherical isodose
region as represented by a sphere. Accordingly, isocentric planning
may be often applied when treating a tumor that has a substantially
regular (e.g., spherical) shape. The radiation beams are shaped by
a device called a collimator. The collimator consists of dense
material that is opaque to radiation, with the exception that there
is a hollow portion adjustable leaves which are able to block
and/or filter radiation to vary the beam intensity and control
distribution of the radiation. The leaves are typically made of a
dense material (e.g., tungsten) that is essentially opaque to
radiation, and are mechanically driven, individually, in and out of
the radiation field of the beam to create a radiation field shape.
FIG. 1 shows the leaves of an MLC adjusted to create a radiation
field shape corresponding to a target silhouette. There are two
conventional ways in which radiation treatment plans are generated
for MLCs.
[0009] Most radiation delivery systems make use of a circular
gantry surrounding the patient with a linear accelerator free to
rotate within the circle. Multiple beams may be produced moving the
accelerator around the circle; the trajectory of the beam can be
characterized by a single angle describing the angle of rotation,
called the "gantry angle". With conventional IMRT (Intensity
Modulated Radiation Therapy) systems having an MLC, treatment
planning is performed by, first, determining an optimal dose
distribution at each node of the treatment system, i.e. each
desired angle. After the dose distribution has been determined,
field shapes are generated using a leaf sequencing algorithm,
taking into account constraints of the MLC. That is, a set of
instructions is generated to move the leaves in a given pattern, in
order to achieve as closely as possible the optimum dose
distribution. After the predicted dose distribution is calculated
from the generated leaf sequencing algorithm, the radiation
treatment of the target volume of interest ("VOI") occurs.
[0010] With conventional 3D conformal systems having an MLC,
treatment planning is performed by first matching the leaves of the
MLC to the target silhouette. In this case, there is no leaf
sequencing algorithm, so the planning component seeks only to match
the shape of each beam to the silhouette of the target from that
gantry angle. Once the MLC positions have been determined, a
predicted dose distribution may be generated, and the radiation
treatment of the target VOI occurs.
[0011] Another mode of delivering radiation treatment is that
provided by the CyberKnife.RTM. system. Instead of moving the
radiation delivery device in a circle around the patient, it is
mounted on a multi-jointed robotic manipulator that has freedom to
make both translational and rotational movement. Hence, radiation
may be delivered from a wide range of positions and orientations
relative to the patient, instead of being restricted to angles
chosen within the circular arc on which the gantry-mounted linac
can travel.
BRIEF DESCRIPTION OF THE DRAWINGS
[0012] The present invention is illustrated by way of example and
not limitation in the figures of the accompanying drawings, in
which:
[0013] FIG. 1 is a plan view of a multileaf collimator adjusted to
conform to a pathological anatomy;
[0014] FIG. 2 is a schematic of a graphical output of a treatment
planning software displaying a slice of a CT image;
[0015] FIG. 3 is a graph showing an ideal DVH for a pathological
anatomy;
[0016] FIG. 4 is a graph showing a desirable DVH for a critical
region;
[0017] FIG. 5 is a perspective view of a radiation treatment system
having spatial nodes in accordance with one embodiment of the
invention;
[0018] FIG. 6 is a perspective view of a collimator at different
orientations in accordance with one embodiment of the
invention;
[0019] FIG. 7A is a flow chart of one implementation of a treatment
planning algorithm;
[0020] FIG. 7B is a flow chart of one implementation of a treatment
planning algorithm in accordance with one embodiment of the
invention;
[0021] FIG. 7C is a flow chart showing pre-optimization at spatial
nodes in accordance with one embodiment of the invention;
[0022] FIGS. 8A-8K are schematic views illustrating
pre-optimization algorithms in accordance with embodiments of the
invention;
[0023] FIGS. 9A-9B are schematic views illustrating
pre-optimization algorithms in accordance with embodiments of the
invention;
[0024] FIGS. 10A-10B are schematic views illustrating
pre-optimization algorithms in accordance with embodiments of the
invention;
[0025] FIGS. 11A-E are screen shots of a user interface
corresponding to a treatment planning algorithm in accordance with
one embodiment of the invention;
[0026] FIG. 12 is a perspective view of a non-isocentric radiation
beam delivery at a pathological anatomy in accordance with one
embodiment of the invention;
[0027] FIG. 13 is a block diagram of a system for diagnostic
imaging and/or treatment delivery in accordance with one embodiment
of the invention; and
[0028] FIG. 14 is a perspective view of a system for diagnostic
imaging and/or treatment delivery in accordance with one embodiment
of the invention.
DETAILED DESCRIPTION
[0029] In the following description, for purposes of explanation,
numerous specific details are set forth in order to provide a
thorough understanding of the present invention. It will be
evident, however, to one skilled in the art that the present
invention may be practiced without these specific details. In other
instances, well-known circuits, structures, and techniques are not
shown in detail or are shown in block diagram form in order to
avoid unnecessarily obscuring an understanding of this
description.
[0030] Reference in the description to "one embodiment" or "an
embodiment" means that a particular feature, structure, or
characteristic described in connection with the embodiment is
included in at least one embodiment of the invention. The
appearances of the phrase "in one embodiment" in various places in
the specification do not necessarily all refer to the same
embodiment.
[0031] An apparatus and method for automating the selection of one
or more radiation beam parameters for a radiation treatment system
are described. In one particular embodiment, the apparatus and
method automatically selects a beam size. In another embodiment,
the apparatus and method automatically determines the beam shape.
In still another embodiment, the apparatus and method automatically
determines the beam orientation. It will be appreciated that the
apparatus and method may automatically determine combinations of
the beam size, beam shape and beam orientation. Embodiments of the
apparatus and method may also automatically select multiple
collimators. Embodiments of the apparatus and method may also
automatically select one or more collimators based on the
automatically determined beam parameter(s).
[0032] In inverse planning, in contrast to forward planning, the
medical physicist specifies a desired dose distribution, for
example, the minimum dose to the tumor and the maximum dose to
other healthy tissues, independently, and the treatment planning
module then selects the direction, distance, and total number and
intensity of the beams in order to achieve the specified dose
conditions. Given a desired dose distribution specified and input
by the user (e.g., the minimum and maximum doses), the inverse
planning module selects and optimizes dose weights and/or beam
directions, i.e. selects an optimum set of beams that results in
such a distribution.
[0033] During inverse planning, volumes of interest (VOIs) are used
to represent user-defined structures to be targeted or avoided with
respect to the administered radiation dose. That is, the radiation
source is positioned in a sequence calculated to localize the
radiation dose into a VOI that represents the tumor requiring
treatment, while as much as possible avoiding radiation dose to
VOIs representing critical structures. Once the target (e.g.,
tumor) VOI has been defined, and the critical VOIs and soft tissue
(all tissue within the treatment region that is represented by
neither a target nor critical VOI) volumes have been specified, the
responsible radiation oncologist or medical physicist specifies,
for example, the minimum radiation dose to the target VOI and the
maximum dose to normal and critical healthy tissue. The software
then produces the inverse treatment plan, relying on the positional
capabilities of the radiation treatment system, to meet the dose
constraints of the treatment plan.
[0034] FIG. 2 is a conceptual illustration of a graphical output of
a treatment planning system displaying a slice of a CT image. The
illustration of the CT image includes a pathological anatomy that
is targeted for treatment, as well as a critical region that is
positioned near the pathological anatomy. The treatment planning
software enables the generation of a critical region contour around
the critical region and a target region contour around the
pathological anatomy. Conventionally, a user manually delineates
points (e.g., some of the dots on the contour lines of FIG. 2) on
the display that are used by the treatment planning software to
generate the corresponding contours. While this may seem an easy
task, such matching is difficult due to the three-dimensional
nature and irregularities of pathological and normal anatomies.
Based on specified minimum dose to the target region and the
maximum dose to the critical region, the treatment planning
software generates the dose isocontour for the target region. The
dose isocontour is a line of constant dose, and represents either a
given dose percentage (e.g., 60%, 70%, 80%, etc.) of a specified
prescription dose for the target region, or an absolute dose value
(e.g. 2000 centigray). Ideally, the dose isocontour representing
the minimum amount of dose deemed to be clinically effective should
perfectly match the contour of the target region. In some cases,
the dose isocontour generated by the treatment planning software is
not optimal, and can include portions of the critical region, as
illustrated in FIG. 2.
[0035] Two of the principal requirements for an effective radiation
treatment system are homogeneity and conformality. Homogeneity is
the uniformity of the radiation dose over the volume of the target
(e.g., pathological anatomy such as a tumor, lesion, vascular
malformation, etc.) and can be characterized by a dose volume
histogram (DVH). The DVH represents, on the y axis, a volume,
either as an absolute measurement or a percentage of the VOI
volume. On the x axis are dose values, either as absolute dose or
as percentage of a given dose (e.g. maximum dose or prescription
dose). The DVH graph shows how much volume of the VOI is covered by
a dose greater than or equal to the corresponding dose value on the
x axis. An ideal DVH for the pathological anatomy would be a
rectangular function as illustrated in FIG. 3, where the dose is
100 percent of the prescribed dose over the volume of the
pathological anatomy. A desirable DVH for a critical region would
have the profile illustrated in FIG. 4, where the volume of the
critical anatomical structures receives as little of the prescribed
dose as possible.
[0036] Conformality is the degree to which the radiation dose
matches (conforms to) the shape and extent of the target (e.g.,
tumor) in order to avoid damage to critical adjacent structures.
More specifically, conformality is a measure of the amount of
prescription (Rx) dose (amount of dose applied) within a target
VOI. Conformality may be measured using a conformality index
(CI)=total volume at .gtoreq.Rx dose/target volume at .gtoreq.Rx
dose. Perfect conformality results in a CI=1. With conventional
radiotherapy treatment, using treatment planning software, a
clinician identifies a dose isocontour for a corresponding VOI for
application of a treatment dose (e.g., 3000 cGy).
[0037] A goal of radiation treatment planning is to find a set of
radiation beams including the position, shape, and "weight" (amount
of radiation delivered by the beam) of each beam that produces a
dose distribution that matches clinical objectives (such as minimum
and maximum dose to target and critical structures, conformality,
and homogeneity). In a robotic-based radiation treatment such as
the CyberKnife.RTM. system, the radiation beam can be moved to a
variety of positions and orientations relative to the patient.
[0038] FIG. 5 is a perspective view of a workspace of a radiation
treatment delivery system 100 including a set of spatial nodes at
which to position the radiation source, in accordance with an
embodiment of the invention. The illustrated embodiment of
radiation treatment delivery system 100 includes a radiation source
105, a treatment couch 110, detectors 115A and 115B (collectively
115, also referred to as imagers), imaging sources 120A and 120B
(collectively 120), and a robotic arm 125.
[0039] Radiation treatment delivery system 100 may be used to
perform radiation treatment (e.g., radiosurgery and/or
radiotherapy) to treat or destroy a lesion (e.g., tumor tissue)
within a patient. During radiation treatment, the patient rests on
treatment couch 110, which is maneuvered to position a volume of
interest ("VOI") describing a target to a preset position or within
an operating range accessible to radiation source 105 (e.g., field
of view). In one embodiment, radiation treatment delivery system
100 is an image guided radiation treatment delivery system.
Together, imaging sources 120 and detectors 115 are an imaging
guidance system that provides visual control over the position of
treatment couch 110 and the patient thereon and the alignment of
radiation source 105 with respect to the VOI within the patient. In
one embodiment, treatment couch 110 may be coupled to a positioning
system (not illustrated), such as a robotic arm, that receives
feedback from the imaging guidance system to provide accurate
control over both the displacement and orientation of the VOI
within the patient relative to radiation source 105.
[0040] In one embodiment, robotic arm 125 has multiple (e.g., six)
degrees of freedom capable of positioning radiation source 105 with
almost an infinite number of possibilities within its operating
envelope. Allowing this type of movement would result in several
challenges. Firstly, a large number of positional possibilities
creates a difficult problem to solve for a treatment planning
system when determining beam positions and trajectories for
treating a particular VOI. Secondly, allowing unconstrained
movement within the operating envelope of robotic arm 125 may
result in possible collisions between radiation source 105 and the
patient or other stationary objects. These problems may be solved
by limiting radiation source 105 to a finite number of spatial
nodes from which radiation source 105 may emit a radiation beam and
further creating specific paths (known safe paths) that robot arm
125 must follow between the spatial nodes.
[0041] A collection of spatial nodes and associated safe paths
interconnecting these spatial nodes is called a "workspace" or
"node set". FIG. 5 illustrates a workspace 130, including a number
of spatial nodes 135 each represented by a "+" symbol (only a
couple are labeled). Multiple different workspaces may be created
and defined for different patient work areas. For example,
workspace 130 may be spherical (as illustrated) and defined for
treating VOIs residing within the head of a patient. Alternatively,
workspace 130 may have other geometries (e.g., elliptical) and
defined for treating VOIs residing within other areas of a patient.
Additionally, multiple workspaces 130 may be defined for different
portions of a patient, each having different radius or source to
axis distances ("SAD"), such as 650 mm and 800 mm. The SAD is the
distance between the electron target used for photon generation in
radiation source 105 and the target described by the VOI. The SAD
defines the surface area of the workspace. In one embodiment of an
elliptical workspace, the SAD may range from 900 mm to 1000 mm.
Other SADs may be used.
[0042] Spatial nodes 135 reside on the surface of workspace 130.
Spatial nodes 135 represent positions where radiation source 105 is
allowed to stop and deliver a dose of radiation to the VOI within
the patient. During delivery of a treatment plan, robotic arm 125
moves radiation source 105 to each and every spatial node 135
following a predefined path. In one embodiment, even if a
particular treatment plan does not call for delivery of a dose of
radiation from a particular spatial node 135, radiation source 105
will still visit that particular spatial node 135, since it falls
along a predetermined safe path. In other embodiments the robot may
skip unused nodes using more detailed knowledge of allowable
transitions between nodes.
[0043] FIG. 5 illustrates a complete node set including an
exemplary number of spatial nodes 135. The complete node set may
include spatial nodes 135 substantially uniformly distributed over
the geometric surface of workspace 130. The complete node set
includes all programmed spatial nodes 135 and provides a workable
number of spatial nodes 135 for effectively computing treatment
plan solutions for most ailments and associated VOIs. The complete
node set provides a reasonably large number of spatial nodes 135
such that homogeneity and conformality thresholds can be achieved
for a large variety of different VOIs, while providing enough
vantage points to avoid critical structures within patients. It
will be appreciated that the complete node set may include more or
less spatial nodes 135 than is illustrated or discussed. For
example, as processing power increases and experience gained
creating treatment plans, the average number of spatial nodes 135
may increase with time to provide greater flexibility and higher
quality treatment plans. In some embodiments, targets may have
pre-defined spatial node sets based on their location. The sets are
typically discovered through experience with similar targets in the
same or similar locations.
[0044] FIG. 6 illustrates re-orientation of the radiation source
105 at a node. As explained above, the radiation source 105 can be
positioned at any of the spatial nodes 135. In addition, at each
node, the radiation source can be reoriented. For example, the
radiation source 105 may be positioned at a first orientation
(orientation 1) at an angle .alpha..sub.1 at the node 135. The
radiation source 105 may also be reoriented to any number of
orientations at angle .alpha..sub.N at the same node 135. In one
embodiment, the radiation source 105 can be reoriented to twelve
different orientations at each node 135 (at twelve different angles
.alpha..sub.1 . . . .alpha..sub.12). It will be appreciated that
the radiation source 105 can be reoriented to fewer orientations or
more orientations. As shown in FIG. 6, one orientation (orientation
1) may deliver a radiation beam at an angle that passes through the
center of the VOI. Other orientations may deliver radiation beams
within the VOI, but not through the center of the VOI, and still
other orientations may deliver radiation beams outside of the VOI.
It will be appreciated that the treatment planning system may
automatically eliminate the orientations that deliver radiation
beams outside of the VOI.
[0045] FIGS. 7A-7C illustrate exemplary algorithms for generating a
treatment plan for use in a treatment planning system. In one
embodiment, the algorithm is an iterative algorithm that optimizes
deviations above the maximum dose constraint and below the minimum
dose constraint. The iterative planning algorithm first generates a
set of candidate beams and performs an initial dose distribution
calculation, and subsequently attempts to improve the initial dose
distribution calculation by altering the weight of one or more
beams. In another embodiment, the algorithm performs convex
optimization, such as, for example, the Simplex algorithm. One
example of a cost function that may be optimized by convex
optimization is the number of monitor units (linearly related to
the total amount of time for which the treatment beam enabled)
subject to the minimum/maximum dose constraints. The Simplex
algorithm is well-known in the art. Alternatively, other iterative
and non-iterative optimization algorithms may be used. In one
embodiment, a combination of both algorithms may be used. In any
event, the target delineation by the user is converted into a VOI
bit mask (i.e., an overlay on the 3D image volume used for
delineation, such that each position with the 3D image has a bit
representing each VOI, set to `1` if the given VOI overlaps that
image position, and `0` if it does not) for use with the treatment
planning algorithm.
[0046] Typically, the treatment planning algorithms require target
identification by the user. The treatment planning algorithm
typically presents the user with a stack of 2D images which combine
to represent the patient's 3D treatment area, and requires the user
to identify contours on the 2D images which are then combined to
define the 3D target volume (target VOI). In one embodiment, target
identification includes a combination of edge detection and
conversion of the edge to a series of points in image space. This
series of points may then be combined to generate a 3D structure
which is rendered on top of a 3D image. Edge detection is described
in further detail in Delp et al., "Edge Detection Using Contour
Tracing," Center for Robotics and Integrated Manufacturing, Robot
through which radiation may pass. The shape and size of the
radiation beam is then determined by the shape and size of this
hollow portion (aperture). When we refer to "collimator size", we
mean the size of radiation beam created by a given collimator
configuration, as measured at a given distance from the radiation
source. Hence the size of the sphere of radiation dose in
isocentric planning may depend on the collimator size which may be,
for example, about 30 millimeters as measured at about 800
millimeters from the radiation source. As the angle of the
radiation source is changed, every beam passes through the tumor,
but may pass through a different area of healthy tissue on its way
to the tumor. To treat a target pathological anatomy, multiple dose
spheres are superimposed or "stacked" on each other in an attempt
to obtain a contour that closely matches the silhouette of the
pathological anatomy. By stacking isocenters within a target
volume, a plan may be developed that ensures that nearly all the
target receives a sufficient dose. As a result, the cumulative
radiation dose at the tumor may be high and the average radiation
dose to healthy tissue may be low.
[0047] In gantry-based radiation treatment systems, the radiation
beam may be shaped by a multileaf collimator (MLC), to conform to
the silhouette of the target as seen from the orientation of the
radiation beam source. The MLC is mounted on a gantry and coupled
to a linear accelerator. The MLC includes several System Division,
College of Engineering, University of Michigan RSD-TR-12-83 (1983)
43. Contouring of points is described in further detail in Mat,
Ruzinoor Che, "Evaluation of Silhouette Rendering Algorithms in
Terrain Visualisation," MSC Computer Graphics and Virtual
Environment Dissertation, Computer Science Department, The
University of Hull
(http:staf.uum.edu.my/ruzinoor/dissertation.htm). Other well-known
methods for target identification may be used in the treatment
planning algorithms.
[0048] FIG. 7A shows a process 200 for generating a treatment plan.
In the implementation illustrated in FIG. 7A, the process 200
begins by delineating a target VOI (block 205). In the
implementation of FIG. 7A, the user identifies the target, and the
system creates the target VOI (block 210). For brevity, we
hereafter refer to this process as "the user identifying the target
VOI", and similarly for the user identifying the critical structure
VOIs.
[0049] The process 200 continues at block 215 by identifying dose
constraints. The dose constraints include, for example but not
limited to: minimum target VOI dose, maximum allowable dose to
healthy tissue, degree of homogeneity, degree of conformality,
total beam on time, a total number of monitor units and a number of
beams. In the implementation of FIG. 7A, the user also identifies
the dose constraints (block 220). Alternatively, a user may first
identify dose constraints and then identify the target VOI, or the
user may identify some dose constraints, identify the target VOI,
and then identify other dose constraints.
[0050] The process 200 continues at block 225 where the user
manually selects the beam shape and beam size. It will be
appreciated that by manually selecting the beam shape and beam
size, the user is manually selecting the collimator(s) to be used
in the treatment delivery. The beam orientation is randomly
determined by the treatment planning algorithm. The treatment
planning algorithm may use a random number generator in combination
with the VOI bit mask to identify orientations which result in a
beam will intersect an internal or surface point in the VOI.
[0051] The process continues at block 230 where a dose mask is
generated for candidate beams. A dose mask is a representation of
the amount of radiation dose delivered by the beam to a set of
locations in space, normalized to the duration of the beam. One
example element in a dose mask would be a voxel location, say (128,
203, 245) in a CT image of the patient, and a dose value of 1 cGy
per second of beam on time. Any well-known process for generating a
dose mask may be used. In the implementation of FIG. 7A, the
candidate beams are randomly generated (block 235). The treatment
planning algorithm may use a random number generator in combination
with the number of available beams, sizes, positions, orientations,
or combinations thereof to generate the candidate beam set. At
block 240, beam weights are optimized for candidate beams. Any
well-known process for optimizing beam weights may be used. As
discussed above, the dose calculation and/or beam optimization may
be an iterative, convex or combination algorithm.
[0052] The process 200 ends at block 245 where the treatment plan
is generated. The treatment plan may be subsequently delivered to
the patient using a radiation treatment system. In one embodiment,
the radiation treatment system is the radiation treatment system
100 described above with reference to FIG. 5.
[0053] FIG. 7B shows another process 300 for generating a treatment
plan in accordance with one embodiment of the invention. In the
implementation illustrated in FIG. 7B, the process begins by
identifying a target VOI (block 305). In the implementation of FIG.
7B, the user identifies the target VOI (block 310), as described
above. The process continues at block 315 by identifying dose
constraints. The dose constraints include, for example but not
limited to: minimum VOI dose, maximum allowable dose to healthy
tissue, degree of homogeneity, degree of conformality, total beam
on time, a total number of monitor units and a number of beams. In
the implementation of FIG. 7B, the user also identifies the dose
constraints (block 320). Alternatively, a user may first identify
dose constraints and then identify the target VOI, or the user may
identify some dose constraints, identify the target VOI, and then
identify other dose constraints.
[0054] The process continues at block 325 where one or more beam
parameters are automatically determined. In one embodiment, the
beam parameter(s) include, for example, one or more of the beam
orientation, beam shape and beam size. Exemplary algorithms for
automatically determining the one or more beam parameters are
disclosed hereinafter. It will be appreciated that because the
treatment planning algorithm automatically determines the beam
parameter(s), the treatment planning algorithm can also
automatically select one or more collimator sizes in order to best
satisfy the dose constraints that have been applied. In one
embodiment, the collimator(s) are fixed aperture collimator(s). In
another embodiment, the collimator(s) are iris collimator(s). With
an iris collimator, the shape of the collimator aperture is fixed,
but the size of the aperture may be varied during the treatment
session, either continuously or in fixed increments of size. In one
embodiment, the IRIS collimator may be an IRIS collimator being
developed by Deutsches Krebsforschungszentrum (DKFZ, German Cancer
Research Center in the Helmholtz Association) of Heidelberg,
Germany.
[0055] The process continues at block 330 where a dose mask is
generated for candidate beams. Any well-known process for
generating a dose mask may be used. In the implementation of FIG.
7B, the candidate beams are determined using the beam parameter(s)
determined at block 325. The candidate beams may also be determined
using the dose constraints and VOI bit mask. At block 340, beam
weights are optimized for the candidate beams. Any well-known
process for optimizing beam weights may be used. As described
above, the dose calculation and/or beam optimization may be an
iterative, convex or combination algorithm.
[0056] The process 300 ends at block 345 where the treatment plan
is generated. The treatment plan may be subsequently delivered to
the patient using a radiation treatment system. In one embodiment,
the radiation treatment system is the radiation treatment system
100 described above with reference to FIG. 5.
[0057] FIG. 7C shows an iterative process 400 for automatically
determining one or more beam parameter(s) in accordance with one
embodiment of the invention. As shown in FIG. 7C, the process 400
determines at block 405 if a node needs to be analyzed. The nodes
referred to in the process of FIG. 7C may be the spatial nodes 135
from FIG. 5. If a node needs to be analyzed (block 405), the target
silhouette is identified at block 410, the dose constraints are
determined at block 420, the shape and/or size are automatically
determined using the geometry of the target at block 430, the
orientation and/or size are automatically determined using a
packing algorithm at block 440. The process returns to block 405
and repeats itself at each node until no nodes remain. When no
nodes remain, the process continues to block 450 where the dose
mask is generated.
[0058] Exemplary processes for determining shape and/or size using
the target geometry and exemplary processes for determining
orientation and/or size using a packing algorithm are disclosed
hereinafter. It will also be appreciated that the iterative process
of FIG. 7C may include fewer steps or more steps. For example, the
iterative process may only include automatically determining one or
more of the beam orientation, shape and size at each node. It will
also be appreciated that the order of steps in the iterative
process may vary. For example, the orientation and/or size may be
determined using the packing algorithm before the shape and/or size
are determined using the target geometry.
[0059] It will also be appreciated that the treatment planning
algorithm may include a combination of user selection (FIG. 7A) and
automatic determination (FIGS. 7B and 7C). For example, the user
may manually select the beam size and beam shape, but the treatment
planning algorithm automatically determines the beam orientation.
In another example, the user manually selects the beam shape, and
the treatment planning algorithm automatically determines the beam
size and beam orientation. In addition, due to system constraints,
the number of collimators may be fixed. Similarly, the collimator
sizes may be fixed (e.g., a single collimator size) or restricted
to a discrete set of sizes. Configurations having continuously
variable-sized beams may be rounded to a nearest allowed collimator
size(s).
[0060] As explained above with reference to FIGS. 7B and 7C, the
treatment plan may include automatically determining one or more
beam parameters. FIGS. 8A-10B illustrate an aspect of the exemplary
algorithms for automatically determining beam parameter(s).
[0061] FIGS. 8A-8K illustrate an aspect of exemplary processes for
automatically determining a beam parameter using a packing
algorithm. The object used to pack the VOI in the packing algorithm
of the radiation treatment planning system corresponds to a cross
section of a radiation beam. The radiation beam, in turn,
corresponds to the radiation profile produced by one or more
collimator(s). Thus, the packing object defines one or more beam
parameters. The beam parameter(s) can be used to automatically
select one or more collimators. For example, the size of the
packing object may define the size of the collimator, and the shape
of the packing object may define the shape of the collimator.
Similarly, the center of the packing shape may define the
orientation of the collimator, with the orientation being defined
by taking the line from the node to the center of the packing
shape.
[0062] Packing algorithms, such as penny packing (for circles of
equal size) or circle packing (for circles of varying size)
algorithms, produce a set of circles that best fill an object, such
as a target silhouette with non-overlapping circles. FIG. 8A shows
a target (VOI) 500 having multiple circles 505 arranged in the VOI
500 according to a penny packing algorithm with no overlap allowed.
Alternative packing algorithms find a set of overlapping circles
whose union in the object. FIG. 8B illustrates an overlapping penny
packing algorithm. In FIG. 8B, the circles 505 are arranged in the
target 500 such that at least a portion of each circles overlaps
another circle. It will be appreciated that the degree of overlap
may vary from that shown in FIG. 8B. Exemplary circle packing
algorithms are described at Collins et al., "A circle packing
algorithm," Computational Geometry 25 (2003) 233-356, and Chen et
al., "Algorithms for Congruent Sphere Packing and Applications,"
SCG '01 (2001) 212-221. Alternatively, other packing algorithms
known in the art may be used.
[0063] The circles (or other packing objects) may be a fixed size
or multiple sizes. FIG. 8C illustrates that packing objects of
different sizes may be used by the packing algorithm. FIG. 8C shows
the VOI 500 having a circle 510 having a first size, circles 515
having a second size and circles 520 having a third size. In FIG.
8C, circle 510 is larger than circles 515, which are larger than
circles 520. It will be appreciated that fewer than three or
greater than three sizes may be used by the packing algorithm and
that the size may vary from the sizes illustrated.
[0064] The size of the objects used in the packing algorithm may be
determined by examining the cross section of the predicted dose
distribution (e.g., as represented by a dose mask) for a given
collimator size. For example, taking the cross section of the dose
mask for a beam with 30 mm collimator diameter, and taking all
elements in the cross section having a value of more than 1
cGy/second may give an approximation to a circle with radius 15
mm.
[0065] As explained above, the packing algorithm may be an
overlapping algorithm. Medial axis transformation is an exemplary
overlapping packing algorithm. A medial axis transformation is a
locus of centers of maximal inscribed disks. A maximal inscribed
disk is a disk with a radius equal to the distance to the nearest
boundary point that is not fully contained in any other inscribed
disk centered at any other point in the object. The union of the
set of all maximal inscribed disks is the object itself (i.e., the
VOI). The skeleton plus the radii of the maximal disks at all
skeleton points is a symmetric axis transform. An exemplary medial
axis transformation algorithm is described at Ge et al., "On the
Generation of Skeletons from Discrete Euclidean Distance Maps."
IEEE Transactions on Pattern Analysis and Machine Intelligence,
Vol. 18, No. 11 (1996) 1055-1066. Alternatively, other medial axis
transformation algorithms or non-overlapping algorithms known in
the art may be used.
[0066] FIGS. 8D and 8E illustrate medial axis transformation with a
VOI. FIG. 8D shows a VOI 500 having an irregular geometry with a
skeleton 525 therein formed using a medial axis transformation
algorithm. FIG. 8E illustrates the medial axis transformation
algorithm with a simple target geometry. It will be appreciated
that medial axis transformation algorithms may be used with more
complex target geometry as well; a simple target geometry is merely
used for ease of description. In FIG. 8E, the VOI 500a includes a
skeleton 525a. Circles 505a are arranged along the skeleton 525a.
The skeleton 525a is used to determine the set of possible circles
505a. The algorithm, based on the dose constraints, then decides
which of those circles 505a can be used to satisfy the dose
constraints. For example, if the algorithm identifies 100 circles
505a, the algorithm may only pick five of the circles 505a, and
hence corresponding collimator sizes and orientations, for
treatment purposes. In addition, a maximum amount of overlap can be
identified, and/or a maximum amount of uncovered area can be
defined by the user or calculated based on the dose constraints,
such as homogeneity, maximum dose amount and conformality, to
eliminate some of the circles 505a.
[0067] FIG. 8F shows a VOI 500 having a first outline of the target
silhouette 530 and a second outline of the target silhouette 540.
The first outline of the target silhouette 530, as opposed to the
actual silhouette 500, can be used by the packing algorithm if the
user desires, for example, conformality. The second outline of the
target silhouette 540, as opposed to the actual silhouette 500, can
be used by the packing algorithm if the user desires, for example,
dose homogeneity.
[0068] FIG. 8G illustrates the application of erosion and dilation
of a beam to a packing algorithm. In FIG. 8G, the VOI 500b includes
circles 505b, each circle having a first outline of the circle 530b
and a second outline of the circle 540b. The first outline 530b
corresponds to erosion and the second outline 540b corresponds to
dilation of the radiation beam. Erosion and dilation allow
overlapping packing algorithms to become non-overlapping algorithms
and non-overlapping algorithms to become overlapping algorithms,
respectively.
[0069] FIG. 8H-K illustrate packing algorithms with packing objects
having different shapes and combinations of shapes. In one
embodiment, the shape of the packing object is a geometric
primitive (i.e., the shape of the collimator is a geometric
primitive). Exemplary geometric primitives include, for example,
circles, ellipses, hexagons, regular polygons and irregular
polygons (e.g., a trapezium).
[0070] FIG. 8H shows a VOI 500 packed with ellipses 500,
corresponding to an elliptically shaped radiation beam (i.e.,
elliptically shaped collimator). FIG. 8I shows a VOI 500 packed
with a circle 555 and ellipses 560. FIG. 8j shows the target 500
packed with hexagons 565. FIG. 8K shows the target 500 packed with
a hexagon 570, ellipses 575 and circles 580. It will be appreciated
that the types of shapes, combinations of shapes, etc., used in the
treatment planning algorithm may vary from those illustrated in
FIGS. 8H-8K.
[0071] As shown in FIGS. 8A-8H, the use of collimator(s) of
different sizes and/or shapes and/or at different orientations can
be particularly advantageous with irregularly shaped targets. For
example, a large collimator can deliver dose rapidly to the central
part of the target while smaller collimators can deliver dose to
conform to the irregular shape of the periphery. In addition, the
use of collimator(s) of different sizes and/or shapes and/or at
different orientations can result in more effective treatment
planning.
[0072] FIGS. 9A and 9B illustrate exemplary algorithms in which one
or more beam parameters are automatically determined using the
geometry of the target (VOI) 600. An exemplary algorithm is
disclosed in Alpert et al., "The Principal Axes Transformation--A
Method for Image Registration." J Nucl Med 1990; 31:1717-1722. As
discussed above, the beam parameters lead to the selection of one
or more collimators. The collimator may be selected as a function
of a characteristic geometric dimension and/or a characteristic
measure of shape. Various measures of shape can be used, including
the ratio of minimum and maximum principal axis, various measures
of eccentricity, and surface-to-volume ratio (with or without
normalization to the surface-to-volume ratio of a sphere of
identical volume).
[0073] FIG. 9A shows the VOI 600 having a center of mass 605. A
collimator is shown in the center of the target 600 at the center
of mass 605. A coordinate system 615 is shown, originating from the
center of mass 605. In one embodiment, the collimator is selected
as a specific percentage of a characteristic geometric dimension.
For example, the primary axes (principal axes) of the
user-delineated target are determined, and the collimator is
selected as a specific percentage of the smallest principal axis.
In the illustrated embodiment, the principal axes are represented
by the coordinate system 615 and the smallest principal axis is
represented by the axis 620. In one embodiment, the collimator size
may be 100%-200% of the smallest principal axis. It will be
appreciated that the collimator size may also be less than 100% of
the smallest principal axis.
[0074] FIG. 9B shows an axis 625 through the center of the target
600. A plurality of axes 630 are shown perpendicular to the axis
625. In one embodiment, the axes 630 are used in a root mean square
analysis of the target 625. The root mean square analysis may be
useful in identifying a beam size.
[0075] The treatment planning algorithm analyzes the VOI from each
node position to find the one or more collimator sizes such that
geometric primitives (i.e., packing object shape) of one or more
characteristic sizes (e.g., circles of one or more diameters),
corresponding to the available collimators, optimally fill or pack
the VOI subject to the dose constraints. FIGS. 10A and 10B show a
target (VOI) from two different node positions. FIG. 10A shows the
VOI 700a from a first position, and FIG. 10B shows the VOI 700b
from a second position. The same VOI has different shapes depending
on the position. Both VOIs 700a and 700b are shown packed with
circles 705, but the VOI 700b is more efficiently packed than the
VOI 700a. As described above, the shape of the packing object and
its size correspond to the collimator shape and size, and its
position in the VOI corresponds to the beam orientation used to
generate the candidate beams at each node position.
[0076] FIGS. 11A-E are exemplary screen shots of a user interface
800 for a treatment planning system. It will be appreciated that
the user interface and screen shots may vary from those illustrated
and described. As shown in FIG. 11A, images of the treatment region
are loaded into the treatment planning system. FIG. 11B shows
different 2D image slices containing cross sections of the target.
As shown in FIG. 11C, the user may enter various dose constraints,
as described above, into the user interface 800. FIG. 11D shows a
treatment plan for the target generated using an algorithm
described herein. FIG. 11E shows a treatment plan for the target,
in which the collimator sizes are automatically selected.
Alternatively, the user may be presented with suggested collimator
size(s), and can accept and/or modify the suggested collimator
size(s).
[0077] It should be noted that embodiments of the present invention
may be used with either, or both, forward and inverse planning
techniques (e.g., isocentric and non-isocentric, or conformal, beam
geometries) to develop a treatment plan. FIG. 12 illustrates a
two-dimensional perspective of non-isocentric radiation beam
delivery at a target region based on conformal planning. It should
be noted that four beams, beam_1 901, beam_2 902, beam_3 903, and
beam_4 904 are illustrated in FIG. 12 only for ease of discussion
and that an actual treatment plan may include more, or fewer, than
four beams. Moreover, the four beams are representative of
conformal planning, in which each beam passes through various
points within target region 900 (e.g., the pathological anatomy).
In conformal planning, some beams may or may not intersect or
converge at a common point, and although the four beams appear to
intersect in the perspective of FIG. 12, the beams may not
intersect in their actual three-dimensional space. The radiation
beams need only intersect with the target volume and do not
necessarily converge on a single point, or isocenter, within the
target 900. In one embodiment, conformal planning takes advantage
of an image-guided, robotic-based radiation treatment system (e.g.,
for performing radiosurgery) such as the CyberKnife.RTM. system,
because the LINAC positioning mechanism (e.g., robotic arm 3012 of
FIG. 14) can move around freely with multiple degrees of freedom,
allowing the radiation beams of the LINAC to point anywhere in
space.
[0078] FIG. 13 illustrates one embodiment of systems that may be
used to perform radiation treatment in which features of the
present invention may be implemented. As described below and
illustrated in FIG. 13, system 4000 may include a diagnostic
imaging system 1000, a treatment planning system 2000, and a
treatment delivery system 100. Diagnostic imaging system 1000 may
be any system capable of producing medical diagnostic images of a
treatment region in a patient that: may be used for subsequent
medical diagnosis, treatment planning and/or treatment delivery.
For example, diagnostic imaging system 1000 may be a computed
tomography (CT) system, a magnetic resonance imaging (MRI) system,
a positron emission tomography (PET) system, an ultrasound system
or the like. For ease of discussion, diagnostic imaging system 1000
may be discussed below at times in relation to a CT x-ray imaging
modality. However, other imaging modalities such as those above may
also be used.
[0079] Diagnostic imaging system 1000 includes an imaging source
1010 to generate an imaging beam (e.g., x-rays, ultrasonic waves,
radio frequency waves, etc.) and an imaging detector 1020 to detect
and receive the beam generated by imaging source 1010, or a
secondary beam or emission stimulated by the beam from the imaging
source (e.g., in an MRI or PET scan). In one embodiment, diagnostic
imaging system 1000 may include two or more diagnostic X-ray
sources and two or more corresponding imaging detectors. For
example, two x-ray sources may be disposed around a patient to be
imaged, fixed at an angular separation from each other (e.g., 90
degrees, 45 degrees, etc.) and aimed through the patient toward
(an) imaging detector(s) which may be diametrically opposed to the
x-ray sources. A single large imaging detector, or multiple imaging
detectors, may also be used that would be illuminated by each x-ray
imaging source. Alternatively, other numbers and configurations of
imaging sources and imaging detectors may be used.
[0080] The imaging source 1010 and the imaging detector 1020 are
coupled to a digital processing system 1030 to control the imaging
operation and process image data. Diagnostic imaging system 1000
includes a bus or other means 1035 for transferring data and
commands among digital processing system 1030, imaging source 1010
and imaging detector 1020. Digital processing system 1030 may
include one or more general-purpose processors (e.g., a
microprocessor), special purpose processor such as a digital signal
processor (DSP) or other type of device such as a controller or
field programmable gate array (FPGA). Digital processing system
1030 may also include other components (not shown) such as memory,
storage devices, network adapters and the like. Digital processing
system 1030 may be configured to generate digital diagnostic images
in a standard format, such as the DICOM (Digital Imaging and
Communications in Medicine) format, for example. In other
embodiments, digital processing system 1030 may generate other
standard or non-standard digital image formats. Digital processing
system 1030 may transmit diagnostic image files (e.g., the
aforementioned DICOM formatted files) to treatment planning system
2000 over a data link 1500, which may be, for example, a direct
link, a local area network (LAN) link or a wide area network (WAN)
link such as the Internet. In addition, the information transferred
between systems may either be pulled or pushed across the
communication medium connecting the systems, such as in a remote
diagnosis or treatment planning configuration. In remote diagnosis
or treatment planning, a user may utilize embodiments of the
present invention to diagnose or treatment plan despite the
existence of a physical separation between the system user and the
patient.
[0081] Treatment planning system 2000 includes a processing device
2010 to receive and process image data. Processing device 2010 may
represent one or more general-purpose processors (e.g., a
microprocessor), special purpose processor such as a digital signal
processor (DSP) or other type of device such as a controller or
field programmable gate array (FPGA). Processing device 2010 may be
configured to execute instructions for performing the operations of
the treatment planning system 2000 discussed herein that, for
example, may be loaded in processing device 2010 from storage 2030
and/or system memory 2020.
[0082] Treatment planning system 2000 may also include system
memory 2020 that may include a random access memory (RAM), or other
dynamic storage devices, coupled to processing device 2010 by bus
2055, for storing information and instructions to be executed by
processing device 2010. System memory 2020 also may be used for
storing temporary variables or other intermediate information
during execution of instructions by processing device 2010. System
memory 2020 may also include a read only memory (ROM) and/or other
static storage device coupled to bus 2055 for storing static
information and instructions for processing device 2010.
[0083] Treatment planning system 2000 may also include storage
device 2030, representing one or more storage devices (e.g., a
magnetic disk drive or optical disk drive) coupled to bus 2055 for
storing information and instructions. Storage device 2030 may be
used for storing instructions for performing the treatment planning
methods discussed herein.
[0084] Processing device 2010 may also be coupled to a display
device 2040, such as a cathode ray tube (CRT) or liquid crystal
display (LCD), for displaying information (e.g., a two-dimensional
or three-dimensional representation of the VOI) to the user. An
input device 2050, such as a keyboard, may be coupled to processing
device 2010 for communicating information and/or command selections
to processing device 2010. One or more other user input devices
(e.g., a mouse, a trackball or cursor direction keys) may also be
used to communicate directional information, to select commands for
processing device 2010 and to control cursor movements on display
2040.
[0085] It will be appreciated that treatment planning system 2000
represents only one example of a treatment planning system, which
may have many different configurations and architectures, which may
include more components or fewer components than treatment planning
system 2000 and which may be employed with the present invention.
For example, some systems often have multiple buses, such as a
peripheral bus, a dedicated cache bus, etc. The treatment planning
system 2000 may also include MIRIT (Medical Image Review and Import
Tool) to support DICOM import (so images can be fused and targets
delineated on different systems and then imported into the
treatment planning system for planning and dose calculations),
expanded image fusion capabilities that allow the user to treatment
plan and view dose distributions on any one of various imaging
modalities (e.g., MRI, CT, PET, etc.). Treatment planning systems
are known in the art; accordingly, a more detailed discussion is
not provided.
[0086] Treatment planning system 2000 may share its database (e.g.,
data stored in storage device 2030) with a treatment delivery
system, such as treatment delivery system 100, so that it may not
be necessary to export from the treatment planning system prior to
treatment delivery. Treatment planning system 2000 may be linked to
treatment delivery system 100 via a data link 2500, which may be a
direct link, a LAN link or a WAN link as discussed above with
respect to data link 1500. It should be noted that when data links
1500 and 2500 are implemented as LAN or WAN connections, any of
diagnostic imaging system 1000, treatment planning system 2000
and/or treatment delivery system 100 may be in decentralized
locations such that the systems may be physically remote from each
other. Alternatively, any of diagnostic imaging system 2000,
treatment planning system 2000 and/or treatment delivery system 100
may be integrated with each other in one or more systems.
[0087] Treatment delivery system 100 includes a therapeutic and/or
surgical radiation source 105 to administer a prescribed radiation
dose to a target volume in conformance with a treatment plan.
Treatment delivery system 100 may also include an imaging system
3020 to capture intra-treatment images of a patient volume
(including the target volume) for registration or correlation with
the diagnostic images described above in order to position the
patient with respect to the radiation source. Treatment delivery
system 100 may also include a digital processing system 3030 to
control radiation source 105, imaging system 3020, and a patient
support device such as a treatment couch 110. Digital processing
system 3030 may include one or more general-purpose processors
(e.g., a microprocessor), special purpose processor such as a
digital signal processor (DSP) or other type of device such as a
controller or field programmable gate array (FPGA). Digital
processing system 3030 may also include other components (not
shown) such as memory, storage devices, network adapters and the
like. Digital processing system 3030 may be coupled to radiation
source 105, imaging system 3020 and treatment couch 110 by a bus
3045 or other type of control and communication interface.
[0088] In one embodiment, as illustrated in FIG. 14, treatment
delivery system 100 may be an image-guided, robotic-based radiation
treatment system (e.g., for performing radiosurgery) such as the
CyberKnife.RTM. system developed by Accuray Incorporated of
California. In FIG. 14, radiation source 105 may be represented by
a linear accelerator (LINAC) mounted on the end of a robotic arm
3012 having multiple (e.g., 5 or more) degrees of freedom in order
to position the LINAC to irradiate a pathological anatomy (target
region or volume) with beams delivered from many angles in an
operating volume (e.g., a sphere) around the patient. Treatment may
involve beam paths with a single isocenter (point of convergence),
multiple isocenters, or with a non-isocentric approach (i.e., the
beams need only intersect with the pathological target volume and
do not necessarily converge on a single point, or isocenter, within
the target as illustrated in FIG. 12). Treatment can be delivered
in either a single session (mono-fraction) or in a small number of
sessions as determined during treatment planning. With treatment
delivery system 100, in one embodiment, radiation beams may be
delivered according to the treatment plan without fixing the
patient to a rigid, external frame to register the intra-operative
position of the target volume with the position of the target
volume during the pre-operative treatment planning phase.
[0089] In FIG. 14, imaging system 3020 may be represented by X-ray
sources 120A and 120B and X-ray image detectors (imagers) 115A and
115B. In one embodiment, for example, two x-ray sources 120A and
120B may be nominally aligned to project imaging x-ray beams
through a patient from two different angular positions (e.g.,
separated by 90 degrees, 45 degrees, etc.) and aimed through the
patient on treatment couch 110 toward respective detectors 115A and
115B. In another embodiment, a single large imager can be used that
would be illuminated by each x-ray imaging source. Alternatively,
other numbers and configurations of imaging sources and imagers may
be used.
[0090] Digital processing system 3030 may implement algorithms to
register (i.e., determine a common coordinate system for) images
obtained from imaging system 3020 with pre-operative treatment
planning images in order to align the patient on the treatment
couch 110 within the treatment delivery system 100, and to
precisely position the radiation source with respect to the target
volume.
[0091] The treatment couch 110 may be coupled to another robotic
arm (not illustrated) having multiple (e.g., 5 or more) degrees of
freedom. The couch arm may have five rotational degrees of freedom
and one substantially vertical, linear degree of freedom.
Alternatively, the couch arm may have six rotational degrees of
freedom and one substantially vertical, linear degree of freedom or
at least four rotational degrees of freedom. The couch arm may be
vertically mounted to a column or wall, or horizontally mounted to
pedestal, floor, or ceiling. Alternatively, the treatment couch 110
may be a component of another mechanical mechanism, such as the
Axum.RTM. treatment couch developed by Accuray Incorporated of
California, or be another type of conventional treatment table
known to those of ordinary skill in the art.
[0092] It should be noted that the methods and apparatus described
herein are not limited to use only with medical diagnostic imaging
and treatment. In alternative embodiments, the methods and
apparatus herein may be used in applications outside of the medical
technology field, such as industrial imaging and non-destructive
testing of materials (e.g., motor blocks in the automotive
industry, airframes in the aviation industry, welds in the
construction industry and drill cores in the petroleum industry)
and seismic surveying. In such applications, for example,
"treatment" may refer generally to the effectuation of an operation
controlled by the treatment planning system, such as the
application of a beam (e.g., radiation, acoustic, etc.).
[0093] In the foregoing specification, the invention has been
described with reference to specific exemplary embodiments thereof.
It will, however, be evident that various modifications and changes
may be made thereto without departing from the broader spirit and
scope of the invention as set forth in the appended claims. The
specification and drawings are, accordingly, to be regarded in an
illustrative sense rather than a restrictive sense.
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