Jiang, Steve Bin
has prevented this new treatment modality from
being widely implemented in clinical routine. These
problems mainly are: 1) external gating is noninvasive but inaccurate, therefore should not be used
alone; 2) abdominal tumors can be treated with
internal gating by fluoroscopically tracking the
implanted markers but imaging dose is a concern;
and 3) for thoracic tumors internal gating does not
work even if one ignores the imaging dose issue, due
to the difficulty in target localization without implanted
fiducial markers. These problems have to be solved
before gated radiotherapy can be safely implemented
in many clinics.
The project proposed here will try to solve the
above mentioned problems by developing necessary
tools and software infrastructure, using an in-house
on-board x-ray imaging system and a commercial
respiratory gating system as hardware platforms. We
plan to develop two sets of tools. One will allow direct
lung tumor mass localization without implanted
fiducial markers. The other will allow an optimal
combination of external gating with internal gating.
The combined gating scheme can be called hybrid
gating. Hybrid gating is the solution to both imaging
dose problem for internal gating and accuracy
problem for external gating. By combining external
gating surrogates with internal surrogates, the
imaging frequency thus the imaging dose can be
greatly reduced. By frequently re-calibrating the
external/internal correlation during the treatment,
target localization accuracy should be greatly
improved. A software infrastructure will also be
developed to facilitate the use of the developed tools
in a streamlined clinical gating procedure.
Correspondingly, there are three specific aims of this
proposal.
Principal Investigator/Program Director (Last, first, middle):
RESEARCH PLAN
A. Specific Aims
Respiratory gated radiotherapy holds promise
to reduce the incidence and severity of normal tissue
complications and perhaps provide a means for
increased local control by dose escalation for the
management of mobile tumors in thorax and
abdomen. Precise target localization in real time is
particularly important for gated radiotherapy due to
the reduced clinical tumor volume to planning target
volume (CTV-to-PTV) margin and/or escalated dose.
Our overall hypothesis is that, with precise target
localization using image guidance techniques, gated
radiotherapy
will
enable
improvements
in
radiotherapy outcome for mobile tumors in thorax
and abdomen.
Direct localization of the tumor mass in real
time is often difficult, if not impossible. Various
surrogates are then used to derive the tumor position
during the treatment. Currently there are two forms of
gated radiotherapy based on the surrogates used:
internal gating and external gating. Internal gating
utilizes internal tumor motion surrogates such as the
implanted fiducial markers while external gating uses
external respiratory surrogates such as markers
placed on the surface of the patient’s abdomen.
Each method has its own pros and cons. By using
external markers, external gating is easy,
noninvasive, and does not require radiation dose for
imaging. The weakness of external gating is related
to the uncertainty in correlation between external
surrogates and internal target position. One can then
say that external gating is “cheap” however often
inaccurate.
For
internal
gating,
with
the
fluoroscopically tracking of implanted markers, the
precision of target localization is often satisfactory,
since in most cases fiducial markers implanted inside
or near the tumor are good surrogates for tumor
position. However, fluoroscopically tracking requires
radiation dose for imaging and the marker
implantation procedure is invasive. With many
treatment fractions or long treatment time of a single
fraction, the imaging dose can be more than what is
clinically acceptable. The invasiveness of the marker
implantation procedure might be clinically acceptable
for abdominal tumors such as liver, but not for
thoracic tumors such as lung, due to the risk of
pneumothorax that could be caused by percutaneous
marker insertion. Therefore, internal gating can be
described as accurate but “expensive” or even
impractical for some tumor sites.
The existence of various problems with the
current state-of-art techniques for gated radiotherapy
PHS 398 (Rev. 05/01)
SA1. To develop tools for gated treatment of lung
cancer without implanted fiducial markers.
Patient’s 4D CT images will be acquired using
developed techniques. Target volume will be
segmented either manually or automatically (using
tools developed for other projects) at each breathing
phase of the 4D CT scan. Tools will be developed to
generate digitally reconstructed fluoroscopy (DRF)
images from the 4D CT scan. Before each fraction of
the treatment, two simultaneous anterior-posterior
(AP) and lateral fluoroscopic images for about 15
seconds long will be acquired using the on-board xray imaging system. Tools will be developed to
register
these
fluoroscopic
images
with
corresponding DRF images 1) to identify the target
contour in the fluoroscopic images and 2) to align
the patient. During the treatment delivery, two
simultaneous orthogonal sets of fluoroscopic images
Page 20__
Jiang, Steve Bin
We propose to develop a software infrastructure to
facilitate the incorporation of the proposed tools as
well as existing tools into a streamlined clinical
procedure for gated radiotherapy. The infrastructure
should include the following functions: 1) display 4D
CT data and generate DRFs, 2) display in real time,
and play back fluoroscopic images, 3) detect and
display marker/tumor position in fluoroscopic
images, 5) register fluoroscopic images with the
DRFs with/without implanted markers, 6) display
reference marker/tumor position and generate
warning sign when the detected marker/tumor
position is outside the tolerance zone around the
reference position, 7) input external surrogate signal
and generate corresponding external gating signal,
8) input internal surrogate signal (detected
marker/tumor position) and generate corresponding
internal gating signal, 9) estimate marker/tumor
position from the combined external/internal
surrogate signals, 10) estimate the optimal time to
image using the external surrogate signal, 11) gate
the on-board imaging system, and 12) gate the
linear
accelerator.
For
each
function,
a
corresponding software module will be developed.
Proposed tools and existing tools will be tested and
implemented into corresponding modules.
By developing the above mentioned tools, we
believe we can treat tumors in thorax and abdomen
with gated radiotherapy in a safe and clinically
practical way.
Principal Investigator/Program Director (Last, first, middle):
will be acquired. Tools will be developed to localize
the target in every frame of these images to
generate gating signals, by using the DRF images of
the corresponding imaging angle.
SA2. To develop tools for combining internal
surrogates with external surrogates.
The correlation between internal tumor position and
external marker position will be investigated using
some existing measured data. Emphasis will be
given to the intra- and inter-fraction variation of the
internal/external correlation. We will find out, if we
use external marker position to derive the internal
tumor position, how frequent we need to re-calibrate
the internal/external correlation by acquiring the xray images. Four schemes of combining external
signal with internal information will be investigated
and the corresponding tools will be developed. The
first approach is called external gating with internal
verification. The external marker position will be
used for gating, while x-ray images will be taken
during the external gating window to verify the
internal marker/tumor position. Tools will also be
developed for the therapists to visualize the detected
tumor/marker position and to monitor the treatment.
If the tumor/marker position differs from the
reference position by a pre-set tolerance value, the
therapists will interrupt the treatment and resume the
treatment after re-aligning the patient. The second
approach is called double gating. The gating signal
generated from the external surrogate will be used to
gate the on-board imaging system. Then based on
the x-ray images the target will be localized and the
linac will be gated. The third and four approaches
are both called hybrid gating, i.e., the target position
will be derived and the gating signal will be
generated by using the external and internal signals
together. For the third approach, called hybrid gating
with minimal imaging, the x-ray imaging takes place
at a uniform rate but the rate will be minimized with
the help of external signal. For the fourth approach,
called hybrid gating with adaptive imaging, the x-ray
images are only taken whenever necessary (so
images are taken at a non-uniform rate). These four
approaches are at an order of increasing technical
difficulty. We will study for various clinical scenarios
(tumor sites, individual patients, etc.), the optimal
way of combining external gating with internal gating
by looking at the clinical practicality, target
localization accuracy, and the imaging dose
reduction of each of the four schemes.
SA3. To develop a software infrastructure for gated
radiotherapy
PHS 398 (Rev. 05/01)
B. Background and Significance
B.1. Problems with Respiratory Tumor Motion in
Radiotherapy
Radiation therapy is a treatment modality
directed towards local control of cancer. The primary
goal is to precisely deliver a lethal dose to the tumor
while minimizing the dose to surrounding healthy
tissues and critical structures. Recent technological
advances in radiation therapy, such as intensitymodulated radiation therapy (IMRT), provide the
capability of delivering a highly conformal radiation
dose distribution to a complex static target volume.
However, treatment errors related to internal organ
motion may greatly degrade the effectiveness of
conformal radiotherapy for the management of
thoracic and abdominal lesions, especially when the
treatment is done in a hypo-fraction or single fraction
manner [1-8]. This has become a pressing issue in
the emerging era of image-guided radiation therapy
(IGRT).
Intra-fraction organ motion is mainly caused
by patient respiration, sometimes also by skeletal
Page 21__
Jiang, Steve Bin
target volume) margins are often applied based on
the expectation of reduced tumor motion [46]. In an
idealized gated treatment, tumor position should be
directly detected and the delivery of radiation is only
allowed when the tumor is at the right position.
However, direct detection of the tumor mass in realtime during the treatment is often difficult. Various
surrogates are then used to indicate the tumor
position. Based on surrogates used, we may
categorize respiratory gating into internal gating and
external gating. Internal gating utilizes internal tumor
motion surrogates such as implanted fiducial
markers while external gating relies on external
respiratory surrogates such as makers placed on the
patient’s abdomen.
A basic concept for the gated treatment is
called gate or gating window. A gating window is a
range of the surrogate signal (such as the 3D marker
position in case of internal gating and the marker
position in case of external gating). When the
surrogate signal falls in the range (gating window),
the gating signal is 1; otherwise it is 0. Therefore, the
gating window converts the surrogate signal into
gating signal and then the gating signal controls the
linac. For internal gating, the gating window is often
a small rectangular solid corresponding to the 3D
position of the implanted fiducial marker. For
external gating, the gating window can be either
defined by two anterial-posterial (AP) positions or
two phase values of the surface marker, which
correspond to two types of external gating:
displacement or amplitude gating, and phase gating.
Another basic concept for gated treatment is
called duty cycle. Duty cycle is a measure of the
treatment efficiency and defined as the ratio of
beam-on time to the total treatment time. Intuitively,
the larger the gating window, the higher the duty
cycle. However, for the same patient, the larger the
gating window, the larger the tumor residual motion.
Therefore, it is always a trade-off between duty cycle
and residual motion.
Principal Investigator/Program Director (Last, first, middle):
muscular, cardiac, or gastrointestinal systems.
Respiration induced organ motion has been studied
by directly tracking the movement of the tumor [2, 912], the host organ [13, 14], radio-opaque markers
implanted at the tumor site [4, 15-19], radioactive
tracer targeting the tumor [20, 21], and surrogate
structures such as diaphragm and chest wall [22-24].
Various imaging modalities have been used for
organ motion studies, including ultrasound [13, 14],
CT [9, 10, 22, 25], MR [26], and fluoroscopy [2, 4,
11, 15-19, 23, 24, 27-31]. It has been shown that the
motion magnitude can be clinically significant (e.g.,
of the order of 2 - 3 cm), depending on tumor sites
and individual patients.
One category of methods to account for
respiratory motion is to minimize the tumor motion,
using techniques such as breath holding and forced
shallow breathing (such as jet ventilation) [10, 3239]. These techniques require patient compliance,
active participation and, often, extra therapist
participation. They may not be well tolerated by
patients with compromised lung function which is the
case for most lung cancer patients [40]. Another
category of the methods accounting for respiratory
motion is to allow free tumor motion while adapting
the radiation beam to the tumor position by either
respiratory gating or beam tracking.
Respiratory gating limits radiation exposure
to the portion of the breathing cycle when the tumor
is in the path of the beam [15, 23, 29, 30, 40-51].
Beam tracking technique follows the target
dynamically with the radiation beam [52]. It was first
implemented in a robotic radiosurgery system
(CyberKnife) [53-57]. For linac-based radiotherapy,
tumor motion can be compensated for using a
dynamic multileaf collimator (MLC) [58-67]. Linac
based beam tracking is still under development. Due
to technical difficulties and quality assurance
considerations, a lot of work has to be done before it
can be applied to patient treatment. One the other
hand, respiratory gating is technically less
challenging and clinically more practical. It has been
introduced in clinic practice in a limited number of
cancer centers. It is believed that gated radiotherapy
will be widely implemented in clinical routine for
treating tumors in thorax and abdomen after some
needed tools are developed. This proposal will focus
on the tool development for gated radiotherapy.
B.2. Some Basic Concepts of Gated
Radiotherapy
For gated radiotherapy, precise and real time
tumor localization is extremely important because
tighter CTV-PTV (clinical tumor volume to planning
PHS 398 (Rev. 05/01)
B.3. History and Current Status of Gated
Radiotherapy
Respiratory gated radiation therapy was first
developed in Japan in the late 1980s and early
1990s for linac photon beams as well as for heavy
ion beams [23, 41, 68, 69]. Various external
surrogates were used to monitor respiratory motion,
including a combination airbag and strain gauge
taped on the patient’s abdomen or back (for prone
treatments) to gate a proton beam [41, 68], and
position sensors placed on the patient [23, 69, 70]. A
major advancement of the gated radiotherapy was
Page 22__
Jiang, Steve Bin
tracking external markers. The weakness is the
uncertainty in the correlation between external
marker position and internal target position. That is
to say, tracking external is not equivalent to tracking
tumor; naïvely trusting the external surrogate can
cause significant errors. A solution to this problem is
the frequent re-calibration of the internal/external
correlation during the treatment.
The Mitsubishi/Hokkaido RTRT system as
well as its application in radiotherapy has been
extensively published by the Hokkaido group [19, 29,
30, 47-51, 77-88]. The system consists of four sets
of diagnostic x-ray camera systems, each of the
camera system consisting of an x-ray tube mounted
under the floor, a 9-inch image intensifier mounted in
the ceiling, and a high-voltage x-ray generator. The
four x-ray tubes are placed at right caudal, right
cranial, left caudal, and left cranial position with
respect to the patient couch at a distance of 280 cm
from the isocenter. The image intensifiers are
mounted on the ceiling, opposite to the x-ray tubes,
at a distance of 180 cm from the isocenter, with
beam central axes intersecting at the isocenter. At a
given time during patient treatment, depending on
the linac gantry angle, two out of the four x-ray
systems are enabled to provide a pair of unblocked
orthogonal fluoroscopic images. To reduce the
scatter radiation from the therapeutic beam to the
imagers, the x-ray units and the linac are
synchronized, i.e., the MV beam is gated off the kV
x-ray units are pulsed.
Using this system, the fiducial markers
implanted at the tumor site can be directly tracked
fluoroscopically at a video frame rate [29]. The linear
accelerator is gated to irradiate the tumor only when
the marker is within the internal gating window. The
size of the gating window is set at +/-1 to +/-3 mm
according to the patient's characteristics and the
margin used in treatment planning[30]. Techniques
for the insertion of gold markers of 1.5-2.0 mm
diameter into or near the tumor were developed for
various tumor sites, including bronchoscopic
insertion for the peripheral lung, image-guided
transcutaneous insertion for the liver, cystoscopic
and image-guided percutaneous insertion for the
prostate, surgical implantation for spinal/paraspinal
lesions[49, 51].
Percutaneously implanting fiducial markers is
an invasive procedure with potential risks of
infection. Many clinicians are reluctant to use this
procedure for lung cancer patients because
puncturing of the chest wall may cause
pneumothorax. The insertion of gold markers using
bronchofiberscopy is feasible and safe only for
peripheral-type lung tumors, not for central lung
Principal Investigator/Program Director (Last, first, middle):
the real-time tumor tracking (RTRT) system
developed by Mitsubishi Electronics Co., Ltd., Tokyo,
in collaboration with the Hokkaido University [29, 30,
47-51]. The RTRT system uses real-time
fluoroscopic tracking of gold markers implanted in
tumor.
Around the mid 1990s, Kubo and his
colleagues at the University of California at Davis
introduced the gated radiotherapy technique into the
United States. They reported the first feasibility study
of gated radiotherapy with a Varian 2100C
accelerator, as well as an evaluation of different
external surrogate signals to monitor respiratory
motion [15]. They also reported a gated radiotherapy
system which tracks inferred reflective markers on
the patient abdomen using a video camera,
developed jointly with Varian Medical Systems, Inc.
(Palo Alto, CA) [40]. This system was later
commercialized by Varian and called real-time
position management (RPM) respiratory gating
system. The RPM system has been implemented
and investigated clinically at a number of centers
[24, 42, 43, 45, 71-76].
Currently, the Mitsubishi/Hokkaido RTRT
system is the only internal gating system used in
clinical routine, while the Varian RPM system can be
considered as the representative external gating
system. Each system has its own strengths and
weaknesses. The weaknesses of existing gating
techniques have been the barriers for the broad
implementation of gated radiotherapy. The goal of
this project is to develop tools that can
eliminate/mitigate the problems, and combine the
strengths, of the current internal gating and external
techniques.
For the RPM system, two passive inferred
reflective markers pasted on a lightweight plastic
block are placed on the patient's anterior abdominal
surface and monitored by a charge-coupled-device
(CCD) video camera which is mounted on the
treatment room wall. The surrogate signal is then the
abdominal surface motion. Both amplitude and
phase gating are allowed by the RPM system. A
periodicity filter checks the regularity of the breathing
waveform and immediately disables the beam when
the breathing waveform becomes irregular, such as
patient movement or coughing, and re-enables the
beam after establishing that breathing is again
regular. The RPM can also be used for treatment
simulation along with a radiotherapy simulator and/or
a CT scanner to acquire the patient treatment
geometry in the gating window and to setup the
gating window.
The major strength of the external gating
systems is the non-invasiveness and easiness of
PHS 398 (Rev. 05/01)
Page 23__
Jiang, Steve Bin
We suspect the current margin sizes may not be
sufficient considering large uncertainties recently
discovered in conventional helical free breathing CT
scans, daily setup based on lasers and/or portal
imaging, and in handling of intra-fraction motion.
Gated
radiotherapy with
precise target
localization can lead to reduced margin. The
quantitative clinical gain of margin reduction
depends on various factors, such as clinical site,
tumor stage and size, as well as the treatment
technique (e.g., fractionation scheme). For large
tumors, the normal tissue volume saved by margin
reduction is small relative to the target volume, but is
large in absolute volume, and is large relative to the
already small un-irradiated normal tissue volume.
For small tumors, either primary or metastases, the
margin size may not be dose limiting and may not
contribute to complications for treatment with
conventional fractionation scheme. However, it may
be crucial for increasingly popular hypo-fractionation
treatments [90].
Studies on the clinical/dosimetric gain of margin
reduction are rare and preliminary. Using respiratory
gating, Wagman et al were able to reduce GTV-toPTV margin from 2 cm to 1 cm for 8 liver patients
[74]. This margin reduction allowed for treatment in 2
patients who otherwise would not had been
candidates for radiation therapy due to the fact that
both patients had only one functioning kidney
located on the ipsilateral side as the tumor. For the
remaining 6 patients, the reduction in margin allowed
for dose increases of 7-27% (median: 21.3%), with
stable normal liver NTCPs, as calculated according
to the Michigan modification of the Lyman model
parameters [91]. Barnes et al found that, on
average, self-gated DIBH decreased the percent of
lung volume receiving >20 Gy (V20) from 12.8% to
11% without and to 8.8% with GTV-to-PTV margin
reduction, which means margin reduction along can
greatly reduce V20 [11]. By CT imaging of
dynamically moving spheres, Keall et al found that
gated radiotherapy may allow a 2-11 mm reduction
in the CTV-to-PTV margin [46]. Our preliminary work
using deformable registration, 4D CT data, and
Monte Carlo simulation has also indicated that there
is a significant dosimetric gain of using gated
therapy. More details will be given in the “Preliminary
Work” section.
Principal Investigator/Program Director (Last, first, middle):
lesions[49, 51]. The Hokkaido group found that the
markers fixed into the bronchial tree may
significantly change their relationship with tumor
after 2 weeks of insertion[87]. Therefore,
bronchoscopic insertion of markers seems not a
good solution for lung tumor treatment, especially for
a large number of fractions.
Imaging dose is a concern for the RTRT
treatment. The Hokkaido group has measured the
air kerma rate, surface dose with backscatter, and
dose distribution in depth in a solid phantom from the
RTRT system [85]. It was found that the air kerma
rate from one fluoroscope was about 240 mGy/h for
a nominal pulse width of 2.0 ms and nominal 100
kVp of X-ray energy at the isocenter of the linear
accelerator. The estimated skin surface dose from
one fluoroscope in RTRT can be up to about 1200
mGy/h[85]. Therefore, approaches to reduce the
amount of exposure are mandatory.
In summary, the major strength of the
internal gating systems represented by the RTRT
system is the precise and real-time localization of the
tumor position during the treatment. The implanted
internal markers are often good surrogates for tumor
position. Marker migration usually is not an issue if
the simulation images are acquired a few day after
marker implantation[51, 78]. It is even less a concern
if multiple markers are used. The two major
weaknesses of internal gating are the risk of
pneumothorax for implantation of markers in lung
and the high imaging dose required for fluoroscopic
tracking.
B.4. Clinical Gain of Gated Radiotherapy
Gated radiotherapy with precise targeting will
certainly improve local control and normal tissue
complication, although the magnitude of the clinical
gains is still unclear. There is a lot we don’t know
about the value of reacting to respiratory motion in
general, and of gated radiotherapy in particular. This
application is not designed to answer these
questions – a process which will take many years
and the engagement of many institutions. It is
designed to develop tools to facilitate the large scale
implementation of gated radiotherapy and thus to
lead to answering these questions. This is an
application without human studies designed to lead
to human experimentation.
Local failure rates remain substantial despite
the use of 3D conformal radiotherapy (CRT) for nonsmall cell lung cancer [89]. The biggest cause of
local failure may not be a volume effect, but simply
due to unappreciated degree of inter- and intrafraction target movement. We may be missing parts
of the target, some of the time, for many patients.
PHS 398 (Rev. 05/01)
B.5. Potential Impact of the Proposed Project
Both external and internal gating techniques
have only been clinically used mainly at major
academic centers with extreme caution. What
prevents the gated radiotherapy from wide clinical
application is mainly due to the concerns about the
Page 24__
Jiang, Steve Bin
integrated solution not only for gated radiotherapy
but also for IGRT in general.
We have investigated the reduction of
imaging rate by using predictive filters to predict
tumor motion[95]. Our work as well as others [96,
97] indicated that the imaging frequency may be
safely reduced to 10 Hz, and thus the imaging dose
can then reduced by a factor of 3 compared to the
video frequency (30 Hz) that the RTRT system uses.
The imaging frequency can be substantially reduced
further by utilizing the external surrogate signal
together with the internal marker position. This idea
was preliminary demonstrated on CyberKnife
systems[52, 54]. Inferred markers on the patient
abdomen are constantly tracked and used to drive
the tumor position. Once in a while an x-ray image is
taken to re-calibrate the correlation between internal
and external markers. Promising results were
obtained by using this simple way of combining
external and internal signals. In this project we plant
to explore more sophisticated ways along this
direction. This approach is actually the solution to
two problems; adding internal signal from time to
time improves the confidence of external gating,
while using external signal reduces the imaging dose
of internal gating. The SA2 of this proposal is mainly
related to the development of tools for optimally
combining external and internal surrogate signals.
In order to apply the tools proposed in SA1
and SA2 in the clinical routine in a streamlined
fashion, a software infrastructure will be developed,
based a software framework that is being developed
as part of the funded NIH R21 grant for real-time
tumor tracking[92]. This development work is the
SA3 of this proposal.
If we successfully reach all three SA’s, we will
have tools that can solves all major problems existed
in current gating techniques, and a software
infrastructure
required
for
smooth
clinical
implementation of these tools. That is to say, this
project will provide a clinically safe and practical way
to implement gated radiotherapy at a large scale.
Many patients with tumors in thorax, abdomen, and
pelvic will benefit from this work by gaining access to
gated radiotherapy of high precision that can lead to
reduced margin, escalated dose, and thus improved
local control.
Principal Investigator/Program Director (Last, first, middle):
problems existed in the current techniques. The
reluctance to implement external gating reflects the
lack of confidence in deriving tumor position through
external surrogates. If this confidence can be
established by frequently re-calibrating the
internal/external correlation, it is believed that
external gating will be accepted by majority of
clinicians.
One of the problems with internal gating is
related to the lung tumor localization. Due to the risk
of pneumothorax, percutaneous insertion of fiducial
markers inside the lung will never become a popular
procedure. Without fiducial markers, current
technology does not allow us to track lung tumor
precisely and in real time for internal gating. As a
small part of a funded NIH R21 grant[92], we have
been studying the feasibility of gating lung cancer
treatment directly based on the fluoroscopic
images[93]. The idea is to calculate the correlation
score between the motion-enhanced reference
template and each frame of fluoroscopic images and
to generate gating signal based on the correlation
score. Preliminary work shows this approach is
feasible. We plan to continue our research along this
direction and to develop tools to facilitate the clinical
application of this approach. This work will be far
beyond the R21 grant and substantial funding is
needed. We propose this work as the SA1 of the
current project.
The other major concern with internal gating
is the high imaging dose required for fluoroscopic
tracking. Imaging dose must be significantly reduced
before internal gating becomes a well-accepted
clinical procedure. Implanted electromagnetic
transponders can be used for real time tumor
localization without any ionizing radiation dose. Submillimeter accuracy was demonstrated in phantom
experiments for such a system[94]. The weaknesses
of this technology include: 1) it does not work for
lung cancer patients, 2) it does not give an
appreciation of patient anatomy near transponders,
and 3) it requires other image guidance systems,
such as a cone beam CT system, to generate 3D
patient data that may be needed for adaptive
therapy. Therefore, we plan to take another strategy
which uses very low frequency fluoroscopy with
imaging dose within the acceptable range. This can
be done with an on-board x-ray imaging system. The
system can also be used for lung tumor imaging
without implanted markers, for cone beam CT scan
during the course of the treatment to adjust the
treatment plan and/or to re-calibration marker/tumor
relationship, and for patient geometric information
other than marker positions. Apparently this is more
PHS 398 (Rev. 05/01)
C. Preliminary Studies
C.1. An On-board X-ray Imaging System
An on-board x-ray imaging system, as shown
in figure 1, called the Integrated Radiotherapy
Imaging System (IRIS), which consists of two pairs
Page 25__
Jiang, Steve Bin
Step 2: Breath training and motion
assessment. Patient’s breathing pattern can vary
inter- and intra-fractionally. To have a reasonable
duty cycle for the gated treatment, and more
importantly, to keep good reproducibility throughout
the whole treatment course, breath coaching is
required for our IGRG technique. A breath training
session of one hour is scheduled on the simulator,
where fluoroscopic images are taken and initial
gating window is determined.
Step 3: 4D CT simulation. After the breath
training session, a 4D CT simulation is scheduled
before the treatment. At first, a free breathing helical
CT scan is taken. Then, a 4DCT scan with breathing
coaching is acquired. Usually 10 sets of CT data are
reconstructed corresponding to 10 different
breathing phases. Special attention is paid on the CT
data corresponding to breathing phases within the
gating window. At this point, the gating window is
fine tuned and finalized, considering the balance
between residual motion and duty cycle.
Step 4: Treatment planning. The GTV and/or
CTV are contoured in each of the 4DCT data sets in
the gating window and then combined to define a
composite target volume that includes the residual
motion. The composite target volume is fused to the
4D CT data set at end-of-exhale (EOE) phase which
is used as the planning CT. The critical structures
are contoured on the EOE CT data set. A margin is
added to CTV to obtained PTV. A 3D CRT or IMRT
treatment plan is developed. A backup plan with
larger margin for non-gated treatment is also
developed using the free breath CT scan.
Step 5: Imaged guided patient setup. The
patient is initially setup using laser alignment to skin
tattoos as in a conventional treatment. The RPM
system is applied to the patient to monitor and coach
the patient’s breathing. After the patient’s breathing
is properly coached, a pair of gated AP and lateral
IRIS radiographs are taken at the EOE phase. Using
an in-house software called DIPS [99], the gated
radiographs are then matched with DRRs to detect
patient shifts.
Step 6: Gated treatment delivery. After the
patient alignment, the gated treatment starts with the
patient under breath coaching with the RPM system.
If it is a 3DCRT treatment, EPID images are taken in
cine mode for treatment verification during the
delivery of each field.
Step
7:
Treatment
verification
and
assessment. The recorded EPID images are
analyzed retrospectively to verify the gated
treatment. The residual marker motion in the gating
window is measured. If the residual motion is
significantly larger than what was estimated during
Principal Investigator/Program Director (Last, first, middle):
of gantry-mounted diagnostic x-ray tubes and
imagers, has been developed at MGH[98]. The
system was assembled in early 2004 and has been
used in clinical routine for radiograph-based patient
setup. The software required for real-time marker
tracking is currently being under the development as
part of the funded NIH R21 grant[92]. The IRIS
system, along with the RPM system, will serve as the
hardware platforms for the proposed tool
development work.
Figure 1. A dual-head
on-board x-ray imaging
system, called Integrated
Radiotherapy Imaging
System (IRIS),
developed at MGH. The
system consists of two xray tubes, two flat
panels, and two x-ray
generators.
C.2. A Clinical Procedure for Gated Radiotherapy
An image guided respiration gated (IGRG)
treatment procedure has been developed at MGH for
gated liver and lung radiotherapy[76]. This procedure
uses Varian RPM system as the linac gating and
patient respiration monitoring/coaching tool and the
IRIS system as image guidance tool. As en effort to
reduce the uncertainty in tumor localization using
external surrogates, 4D CT scanning, gated
radiographic setup, cine EPID (Electronic Portal
Imaging Device) treatment verification, as well as
patient breath coaching, have been used together
with the RPM system. For precise patient setup and
treatment verification, implanted fiducial markers are
required for liver patients and clear anatomic
features near the target are required lung cancer
patients. The procedure includes seven major steps:
Step 1: Patient selection and preparation. In
addition to medical considerations judged by the
attending clinician, we determine the suitability of the
patient for receiving IGRG treatment according to
three factors. First, since breath coaching technique
is used throughout the whole treatment course, the
patient should be able to follow the breath coaching
instruction. Second, the patient should have radioopaque makers implanted inside or near the liver
tumor or clear anatomic features near the lung
tumor. Third, the patient has a large intra-fraction
tumor motion in order to have a significant gain from
the gated treatment.
PHS 398 (Rev. 05/01)
Page 26__
Jiang, Steve Bin
in between two lines while breathing out. When
breathing in, there is no constraint on the EOI
position. An example of this coaching technique is
given in figure 2, along with the free breathing trace
for the same patient for comparison.
Principal Investigator/Program Director (Last, first, middle):
the simulation session, the treatment will be modified
by simply reducing the gating window. This
technique has been published[100] and will be
described in section C.5.
C. 3. A 4D CT Scanning Protocol
For
gated
radiotherapy,
patient/tumor
geometry within the gating window should be used
for treatment planning and patient setup. To acquire
such information, gated CT or 4D CT scan should be
an integral part of the gated radiotherapy treatment.
At MGH, in collaboration with GE Medical Systems
(Waukesha, Wisconsin), we have developed a
scanning protocol for generating 4D CT image data
sets for patients with respiratory tumor motion [101].
This protocol has been implemented in our clinic
since 2004 and by now a few hundred of lung and
liver patients have been scanned [102-104].
C.4. A Patient Breath Coaching Technique
The RPM gating system has audio instruction
and visual feedback functions for patient breath
coaching. The audio instruction function tells the
patient when to breath in and out, while the visual
feedback function guides the patient to have
constant end-of-exhale (EOE) position and constant
end-of-inhale (EOI) position by letting the patient to
look his/her own breathing waveform in real time.
Sometimes the audio instruction technique is used
along [72, 75]. A commonly used coaching protocol
is based both audio instruction and visual feedback
[72]. This audio/video protocol has been tested at
MGH, on five healthy volunteers, observed during 6
sessions, and 33 lung cancer patients, observed
during one session when undergoing 4D CT scans,
with free breathing as a control [105]. For all 5
volunteers, breath coaching was well tolerated and
the intra- and inter-session reproducibility of the
breathing pattern was greatly improved. However,
about half of the patients could not follow both audio
and video instructions simultaneously, suggesting
that the audio/video coaching protocol is too
complicated for patients and needs to be simplified.
For amplitude gating, the variation of
breathing period has no effect on the treatment.
Therefore, audio prompting may not be necessary
here. If the gating window is set at EOE, there is no
need to have a stable EOI position of breathing
waveform. Based on these considerations, we
developed a new breath coaching protocol. Two
straight lines that contain EOE positions are used to
define the amplitude gating window. By looking at
his/her own breathing waveform on a pair of video
goggles, the patient is asked to put the EOE position
PHS 398 (Rev. 05/01)
Free breathing
Coached breathing
Figure 2. The breath waveforms of a lung cancer
patient, treated with gated radiotherapy, with and
without breath coaching using a protocol developed
at MGH. The two dashed lines define a gating
window.
C.5. A Treatment Verification Technique for
Gated 3DCRT
Figure 3. Cine EPID image (right) is compared with the
DRR image (left) for the same beam to determine the
residual marker motion within the gating window.
For external gating, the verification of tumor
position when beam turns on is important. For gated
3DCRT, we have developed a new technique for
treatment verification using EPID in cine mode [100].
Implanted radiopaque fiducial markers inside or near
the target are required for this technique. During the
treatment, a sequence of EPID images can be
acquired without disrupting the treatment. Implanted
markers are visualized in the images and their
positions in the beam’s eye view are calculated offline and compared to the reference position by
Page 27__
Jiang, Steve Bin
poor image quality due to such as MV beam
interference. We have developed a parameterized
template matching method that addresses some of
the issues, which has been used for analysis of the
tumor motion due to respiration[107, 108]. As part of
the funded NIH R21 grant[92], we are also
developing a technique called multiple hypothesis
tracking (MHT) that is able to track multiple markers
simultaneously without mixing them up and is robust
enough to continue tracking even when the marker
is moving behind bony anatomy[109, 110]. The
developed marker tracking algorithm will be
integrated into SA3 to facilitate gated radiotherapy
based on internal marker position.
Principal Investigator/Program Director (Last, first, middle):
matching the field apertures in corresponding EPID
and DRR images, as shown in figure 3. The
precision of the patient setup, the placement of the
beam-gating window, as well as the residual tumor
motion can be assessed for each treatment fraction.
Since this technique uses the exit image of a
treatment beam, it might be difficult to be applied to
gated IMRT treatment. Also, the current technique is
off-line. In the proposed project, we plan to improve
this technique, by developing software infrastructure
to allow on-line treatment verification/monitoring
based on real-time IRIS images. This is part of SA3.
C. 6. Studies on the Dosimetric Gain of Gated
Radiotherapy
In an early treatment planning study, we
investigated the dosimetric gain of margin reduction
for beam gating and beam tracking for one lung
patient and one liver patient [106]. Tissue motion
and deformation were estimated across breathing
phases of 4D CT data using deformable registration.
The CTV (in the reference phase) volumes were 118
cc for the lung case and 162 cc for the liver case. It
was found that the integral dose to healthy tissue
outside the CTV was reduced by 20% (lung) and
30%(liver) by using respiratory gating.
To have a more realistic, quantitative, and
systematic estimation of the gains of margin
reduction in dose and then in TCP/NTCP, we have
been trying to compare the gated treatment with
non-gated treatment by calculating dose distributions
using Monte Carlo simulation on deformablyregistered multiple-phase 4D CT scans (cite AAPM).
For patients with large tumor motion, gated
radiotherapy can significant improve the target
coverage. For example, for a patient with 2 cm peakto-peak respiratory motion, the equivalent uniform
dose (EUD) of CTV based on the treatment plan
should be 60 Gy. However, the actual delivered EUD
is only 30 Gy without gating. With gating, 60 Gy can
be delivered.
C.7. Marker Tracking Techniques
The implanted markers can be in either
spherical (e.g., 1.5-2.0 mm diameter) or cylindrical in
shape (e.g., 0.8 mm diameter and 3 mm length).
Although tracking high-density markers may seem
easy, there are still some technical challenges for
precise and reliable real-time tracking. These
challenges include the change of the marker shape
from frame to frame (for cylindrical and wire
markers), the occlusion by and the confusion with
bony structure, air bubbles, etc, the correspondence
issue when multiple markers are present, and the
PHS 398 (Rev. 05/01)
C.8. Tracking Failure Detection Techniques
Tracking algorithms may fail when the signal
quality is poor, or when background clutter mimics
the target. Therefore, any tracking system should
have the ability to detect and correct the tracking
failures. When a tracking failure happens, the
treatment beam should be held off until the recovery
of the correct tracking, or the therapist should
interrupt the treatment and re-set the tracking
system. We have developed an algorithm that
utilizes four signals to detect tracking errors that
include distance between rays, pattern recognition
score, instantaneous velocity and acceleration[111].
Those signals will be displayed in real time for the
therapist to monitor the treatment. In case of lung
tumor tracking without implanted markers, the
correlation score will be displayed. This is part of
SA3.
C.9. Gating Based on Lung Tumor Mass
As part of the fund NIH R21 grant, we have
investigated the feasibility of gating the treatment
using
fluoroscopic
information
without
the
implantation of radiopaque fiducial markers. Some
promising preliminary results have been obtained
[112]. We found that the average image intensity in
a region of interest in lung fluoroscopy fluctuates
following the patient breathing pattern. This is due to
the fact that, as the lungs fill/empty, the radiological
pathlength through them shortens/lengthens, giving
brighter/darker fluoroscopic intensities. The motion,
for which we would like to compensate, is the key to
marker-less gating. Our results show that a moving
tumor can be isolated from the rest of the anatomy
by using a motion enhancement algorithm. The
correlation scores calculated between a motionenhanced reference template and motion-enhanced
fluoroscopic images are then used as surrogate
signal to generate gating signal. The resulting beam-
Page 28__
Jiang, Steve Bin
a finite state model that breaks a normal breathing
cycle into three states: inhale (IN), exhale (EX), and
end of exhale (EOE)[115]. A forth state (IRR) is also
introduced to handle irregular breathing. The
transition from one state to another is automatically
guided by the finite state automaton (FSA) as
illustrated in figure 4. The model has been
successfully applied to analyze the tumor motion
data of 23 lung cancer patients and is being tested
for tumor motion prediction (cite AAPM).
Principal Investigator/Program Director (Last, first, middle):
on pattern is similar to one produced by the RPM
external gating system. We plan to investigate along
this direction to develop accurate, clinically robust,
and computational efficient methods for gated
treatment of lung cancer without implanted fiducial
markers. This will be SA1 of this proposal.
C.10. Residual Tumor Motion in External Gating
External gating assumes that the correlation
between the external surface and the internal tumor
position remains constant during the treatment. This
assumption has yet to be validated. As the first step,
we have measured the residual tumor motion within
an external gating window[113]. If the correlation is
stable at least at EOE, the residual motion should be
similar to what was measured during treatment
simulation and used for treatment planning. We used
synchronized internal/external data from our
collaborators in Japan. Eight lung patients with
implanted fiducial markers were studied at the NTT
Hospital in Sapporo, Japan. Synchronized internal
marker positions and external abdominal surface
positions were measured during the entire course of
treatment. The RTRT system was used to find the
internal markers in four dimensions. We then used
the data retrospectively to assess the residual tumor
motion within the external gating window. We found
that the residual motion (95th percentile) was
between 0.7-5.8 mm, 0.8-6.0 mm, and 0.9-6.2 mm,
for 20%, 30%, and 40% duty cycle windows,
respectively. Large fluctuations (>300%) were seen
in the residual motion between some beams, which
indicates that for some patients the internal/external
correlation may vary significantly even during the
same treatment fraction, and frequent re-calibration
of the correlation and on-line monitoring are
necessary during the treatment.
C.11. Modeling Tumor Trajectory
Modeling tumor trajectory not only helps
understanding tumor motion characteristics, but also
facilitates the development of tumor motion
prediction filters. Tumor motion is often very
complicated. A simple cosine model certainly cannot
handle such complexity[19, 114]. We have tested
two methods for tumor trajectory modeling. One is
based on a new concept that we introduced, called
Average Tumor Trajectory (ATT), which scales and
averages several cycles of tumor trajectory
measured during treatment simulation session and
then is used to estimate tumor trajectory during the
treatment[59]. This works only when breathing coach
is applied to enforce a stable and regular breathing
pattern. The other method is more general. It utilizes
PHS 398 (Rev. 05/01)
REOE
EOE
EOE
REOE
RIRR
RIN
IN
EX
IRR
REX
(a)
RIRR
EX
RIRR
RIN
RIRR
IN
REX
REX
RIN
(b)
5
-5
-15
-25
0
2
3
5
7
8
10
12
13
15
17
18
20
22
23
25
27
28
Time (secs)
(c)
Figure 4. The finite state model for tumor respiratory
motion: (a) three states of a regular breathing cycle, (b)
finite state automation for respiratory motion, (c) raw
and modeled respiratory motion with irregular breathing.
C.12 Tumor Motion Prediction
Prediction filters are needed in a tumor
tracking system to handle the following issues: 1).
the system latency (the time delay between imaging
and treatment beam reaction), 2) the detection of
irregular tumor motion (such as unexpected patient
cough or movement), 3) the reduction of imaging
frequency for lower imaging dose. We have
evaluated various predictive models for reducing
tumor localization errors when a real-time tumortracking system targets a moving tumor at a slow
imaging rate and with large system latencies[95]. We
compared linear prediction, neural network
prediction and Kalman filtering, against a system
which uses no prediction. We also developed a
model-based probabilistic solution for mining and
prediction of tumor respiratory motion (cite AAPM).
By analyzing the historical motion data based on a
finite state model, two probability distributions are
proposed for knowledge discovery of tumor moving
status. These probabilities can be used to determine
the current motion state and capture transitions from
Page 29__
Jiang, Steve Bin
MGH as part of the NIH funded research
collaboration between MGH and Varian Medical
Systems[92]. This framework, called the IRIS
Software Environment (ISE), is responsible for realtime communication with the dual fluoroscopic
imagers hardware, real-time dual display, and realtime tracking of implanted markers in 2D and 3D.
Completion of ISE framework is scheduled for
October 2006, with development of advanced
marker tracking and prediction algorithms continuing
through October 2007. ISE will serve as the basis for
the proposed software infrastructure.
Principal Investigator/Program Director (Last, first, middle):
one state to another. They are dynamically built and
used in real-time motion prediction. Results from
both studies indicate that using prediction filters,
reasonable marker localization accuracy can be
achieved for gated treatment for systems that have
latencies as large as 200 ms and for systems that
have imaging rates as low as 10 Hz. To further
reduce the imaging rate, external signal, which can
always be at high frequency such as 30 Hz, should
be included into the prediction process. This is
actually part of SA2.
C.13. A Dynamic Phantom
As part of the PI’s Whitaker grant[116], a
computer-controlled motor-driven motion phantom
has been developed to simulate external marker
motion and tumor motion projected in beam’s eye
view (BEV), using measured external and internal
signals as input. The design of the phantom
composed of three main parts: three sets of step
motors and linear stages, an electrical interface and
a computer system. The RPM marker box, attached
to one stage, can move vertically to simulate
external gating surrogate. A mechanical platform,
fixed on top of two stages, can move in 2D to
simulate tumor motion in BEV. For dosimetric
measurement, the platform can hold solid water. All
three motors can replicate input external and internal
motion signals precisely spatially and temporally.
The accuracy of the phantom has been tested using
a calibrated camera system. The phantom’s ability to
recreate variable patterns of movement makes it
useful for testing tools developed in SA2.
C.14. A Radiograph-based Patient Setup
Software
A radiograph-based patient setup software,
called Digital Image Positioning System (DIPS), has
been developed and used at MGH proton therapy
center since 2001. To date, more than 50,000
images have been collected and used for patient setup. Recently, we have developed a semi-automatic
feature extracting technique for DIPS and integrated
it into the IRIS system for patient setup in our photon
therapy clinic [117]. The procedure uses the
manually selected features from the first treatment
fraction to automatically locate the same features on
the second and subsequent fractions. Some
software modules of DIPS can be used for the
development of the infrastructure for this proposal.
C.15. Software Infrastructure
A software framework for tracking implanted
markers is currently under active development at
PHS 398 (Rev. 05/01)
D. Research Design and Methods
D.1. A General Description of the Proposed
Clinical Procedure for Gated Radiotherapy
In this section, we propose a new clinical
procedure for gated radiotherapy using IRIS and
RPM as hardware platforms, and based on the tools
and software infrastructure to be developed. As
described in the section of “Preliminary Work”, the
clinical procedure developed at MGH for gated
radiotherapy treatment includes the following steps:
1) patient selection and preparation, 2) breath
training and motion assessment, 3) 4D CT
simulation, 4) treatment planning, 5) image-guided
patient setup, 6) gated treatment delivery, and 7)
treatment verification and assessment. Various
techniques have been developed and clinically
implemented; however, there still exist major
technical challenges in the last three steps that
prevent the clinical use of gated radiotherapy in an
effective and safe way. The proposed tools and
infrastructure in this project will handle those
challenges, covering steps 5 to 7, which are all
related to the treatment delivery part of gated
radiotherapy and happen on the linac.
The proposed new clinical procedure for
gated radiotherapy is the same as the existing one
for the first four steps. With the tools to be
developed, the last three steps will be different
(improved). For step 5, i.e., image-guided patient
setup, before each fraction of treatment, there will be
a fluoro setup session, in which about 15 seconds of
AP and lateral fluoroscopic images will be taken
using the on-board imaging system (IRIS) while the
patient’s breathing is coached and stabilized. The
reason to choose 15 seconds is that for this time
period we can have 3-5 breathing cycles and that is
considered necessary for fluoro-based patient setup.
These images will be processed off-line to calculate
the necessary patient shift in order to align the target
properly. For patients with implanted fiducial
Page 30__
Jiang, Steve Bin
treatment and re-align the patient. Research issues
here include: 1) to determine the required minimum
imaging rate (SA2), 2) to detect the lung tumor
position without implanted markers (SA1), and 3) to
develop software for displaying measured and
reference target positions as well as the tolerance
zone, and for generating warning signal (SA3).
Principal Investigator/Program Director (Last, first, middle):
markers, such as liver patients, markers will be
automatically tracked. The mean position of all
markers within the gating window will be calculated
and compared to the reference position derived from
the 4D CT scan. Then patient shift can be
calculated.
In general, tracking implanted fiducial
markers seems an easy problem however there are
still some technical challenges in clinical practice.
These challenges include: 1) for cylindrical or wire
markers, the marker shape in projection images can
change constantly with motion; 2) markers may be
occluded by and confused with bony structure, air
bubbles, or other features in the fluoroscopic
images; 3) from some imaging angles, multiple
markers may located closely in the images and the
correspondence issue is not trivial; and 4) during the
treatment the quality of the fluoroscopic images is
often degraded due to the MV beam interference.
We have developed a technique for tracking a single
marker of cylindrical shape (cite Greg’s abstract). In
a funded NIH grant, we are currently developing
tools for tracking multiple markers simultaneously
(cite). Those marker tracking tools will be used in the
proposed project.
For patients with tumors in thorax, we may
not have implanted fiducial markers. The detection
of tumor location for patient setup is a major
technical challenge which will be addressed in the
next section of this proposal (SA1). The basic idea is
to match the setup fluoroscopic images in the gating
window to the corresponding DRFs generated from
the patient’s 4D CT data. Therefore, we need to
develop tools to generate DRFs and to detect lung
tumor position by registering the fluoroscopic images
with DRFs.
For gated treatment delivery (step 6), we
propose to use both external and internal surrogate
signals for gating to combine the merits of external
gating and internal gating, i.e., reduced imaging
dose and still sufficient treatment accuracy. We plan
to test four schemes of combining external and
internal signals for gated treatment delivery:
G1. External gating with internal verification
Gating signal will be generated using the external
surrogate signal (Varian RPM system) to control the
linac. Within the external gating window, x-ray
fluoroscopic images will be taken at a pre-set rate.
Marker/tumor position will be detected and displayed
on a computer monitor along with the reference
position and tolerance zone. Warning signal will be
given when the detected marker/tumor position is
outside of the tolerance zone. When this happens
persistently, the therapist should interrupt the
PHS 398 (Rev. 05/01)
G2. Double gating
The basic idea is that, RPM gates IRIS, and then
IRIS gates linac. Gating signal generated from the
external surrogate signal will be used to gate the
IRIS system. Therefore, fluoroscopic images will only
be taken within the external gating signal.
Marker/tumor position will be detected in the gated
fluoroscopic images and then used to gate the linac.
Research issues here include: 1) to gate the IRIS
system using external gating signal (SA3) and 2) to
detect the lung tumor position without implanted
markers (SA1), and 3) to gate the linac using internal
gating signal (SA3).
G3. Hybrid gating with minimized imaging
External surrogate signal will be used to derive the
target position. X-ray images will be taken at a
constant rate to re-establish the internal/external
correlation. Research issues here include: 1) to
model the internal/external correlation (SA2), 2) to
determine the minimum imaging rate needed to
maintain a good internal/external correlation (SA2),
and 3) to detect the lung tumor position without
implanted markers (SA1).
G4. Hybrid gating with adaptive imaging
External surrogate signal is constantly acquired and
the target position as well as a confidence value will
be estimated using the external signal. The internal
surrogate signal is only acquired (i.e., an x-ray image
is taken) whenever the confidence value falls below
a pre-set threshold value. The estimated
marker/tumor position will be used to gate the linac.
Research issues here include: 1) to model the
internal/external correlation (SA2), 2) to develop
algorithms to derive the target position with a
confidence value from the external signal (SA2), and
3) to detect the lung tumor position without implanted
markers (SA1).
The technical difficulty increases from
scheme 1 to 4. Each scheme has its pros and cons.
All four schemes will be developed, tested, and
compared with each other as well as pure external
gating and internal gating, to evaluate their
practicality, imaging dose reduction, marker/tumor
localization precision, etc. Recommendations will be
then given for various clinical scenarios.
Page 31__
Jiang, Steve Bin
(RPI) [118], which is beyond the scope of this grant
proposal. For those lung patients under gated
treatment, we also take occasional (mostly weekly)
IRIS fluoroscopic images to assess, in an off-line
manner, the tumor motion and motion pattern
change during the treatment course. The 4D CT
data and fluoroscopic data acquired for routine
patient treatment will be used retrospectively for the
development and test tasks proposed fro SA1. An
IRB will be applied later.
Principal Investigator/Program Director (Last, first, middle):
D.2.2. Registration of the fluoroscopic images to
DRFs for patient setup
4D CT
PHS 398 (Rev. 05/01)
Fluoro
50%
30%
D.2. SA1: To develop tools for gated treatment of
lung cancer without implanted fiducial markers
D.2.1. Patient data
4D CT scan has become a routine clinical
procedure at MGH since 2004 [101]. More than 100
lung patients have been scanned with our 4D CT
protocol [102-104]. Currently, for our lung cancer
patients, 4D CT data is always acquired for purposes
of motion assessment and treatment planning. Most
of those patients are treated with non-gated 3DCRT
or IMRT if motion is less than 1.5 cm peak-to-peak,
using the concept of internal target volume (ITV) to
define PTV. For some lung cancer patients, if tumor
motion is greater than 1.5 cm, and tumor mass has
high contrast in fluoroscopy, or tumor is attached or
close to an easily detectable anatomic feature such
as diaphragm, we treat them with gated 3D CRT or
IMRT using the protocol described in section C.2..
For those patients, GTV will be contoured on each
phase of the 4D CT scan. CTV contours will be
generated for phases within the gating window and
will be combined to generate a PTV with proper
margin to take into account the setup error and
estimated organ motion that is additional to what is
shown during the 4D CT scan. Critical structures will
be contoured only on the phase at the center of the
gating window, such as the end of exhale (EOE)
phase. Currently, we perform manual contouring of
GTV for all the phases which is a time consuming
process. Automatic segmentation tools have been
under the development through a collaborative effort
between MGH and Rensselaer Polytechnic Institute
DRF
0%
In this new clinical procedure of gated
radiotherapy, treatment verification (step 7) will be
integrated into treatment delivery (step 6) and
becomes more like treatment monitoring. No matter
which treatment delivery scheme is used, we will
always display the detected marker/tumor positions
within the external gating window on a computer
monitor along with the reference position and
tolerance zone. Therapists will always be asked to
monitor the detected marker/tumor position and
interrupt the treatment when any abnormity happens.
Statistics of marker/tumor positions within the
external gating window will be computed and stored
for off-line assessment. Research issues here
include: 1) to develop software for displaying
measured and reference target positions and the
tolerance zone, and for generating warning signal
(SA3), and 2) to develop software for calculating and
displaying statistics of the target positions in the
external gating window (SA3).
Figure x. Coronal slices of 4D CT data, AP DRFs, and
AP fluoroscopic images at various breathing phases:
0% (inhale), 30%, and 50%(exhale). The GTV contour
of EOE is also shown in the CT slices.
For each patient, AP and lateral DRFs will be
generated from the 4D CT data. The frequency of
the DRFs is about 2-5 Hz depending on the period of
the patient breathing cycle. The outer contour of the
GTV will be projected in each frame of the DRFs.
The mean position of the lung tumor in the gating
window will be calculated and used later as the
reference position for patient setup. We will also
acquire about 15 seconds of simultaneous AP and
lateral fluoroscopic images during the patient setup
session prior to each fraction of treatment. The
fluoroscopic images will be processed off-line and
registered to corresponding DRFs to calculate the
necessary patient shift. An example of 4D CT data,
DRFs, and fluoroscopic images is shown in figure x.
We plan to explore two approaches for the
registration of fluoroscopic images to DRFs. The first
approach is automatic registration. Both fluoroscopic
Page 32__
Jiang, Steve Bin
position as defined by the template using the
correlation score.
We propose two strategies for gated
treatment for lung cancer patients without implanted
fiducial markers: score-based gating and locationbased gating. Score-based gating uses the gating
signal generated based on the correlation score
between a reference template and a region of
interest (ROI) in each fluoroscopic image acquired
during the treatment, while location-based gating
uses the gating signal generated directly from the
tracked tumor location. Note that in both strategies,
we pre-process all the image frames by motionenhancement [112].
Principal Investigator/Program Director (Last, first, middle):
images and DRFs will be motion-enhanced and
averaged over all the frames in the gating window.
The motion-enhanced method has been shown to
work well in our preliminary work, for removing
irrelevant static structures [112]. Various existing
image registration algorithms, such as crosscorrelation or mutual information, will be tested to
match the motion-enhanced average fluoroscopic
image to the motion-enhanced average DRF image.
The registration can be done for both AP and lateral
fluoroscopic images either simultaneously or
sequentially. When it is done sequentially, the shifts
along the common direction, i.e., the super-inferior
direction, will be averaged. Both simultaneous and
sequential registration methods will be tested and
compared.
The automatic registration procedure proposed here
has not been tested and it may not work for some
patients. As an alternative and backup, we will also
develop a manual registration procedure. Various
techniques will be explored to facilitate the manual
registration. One technique is to play back the setup
fluoroscopic images and DRFs with GTV contours
side by side, and the clinician will use computer
mouse to drag the GTV contour in fluoroscopic
images to the right position. Another technique is to
overlay DRFs on the fluoroscopic images with
different
colors
or
transparency.
Motionenhancement and image subtraction will also be
tested. Software tools and user interface to allow
manual registration will be developed as part of SA3.
D.2.3. Generation of gating signal from the
fluoroscopic images during treatment
During
the
treatment
delivery,
two
simultaneous orthogonal sets of fluoroscopic images
will be acquired. The central axis of each set of
images is 45 degrees from the therapy beam central
axis. Depending on the technique of combining
external and internal signals, the time and frequency
of taking fluoroscopic images might be different. For
both G1 and G2, fluoroscopic images will only be
taken within the external gating window at a pre-set
imaging rate. For G3, fluoroscopic images will be
taken at a constant imaging rate to re-calibrate the
internal/external correlation. For G4, fluoroscopic
images will only be taken when required by the
algorithm. The required minimum imaging rate might
be different for G1, G2, and G3, and will be
investigated as part of SA2. Also, how to determine
the time to take images for G4 will be studied in
SA2. No matter which technique is used for
combining external/internal signals, we need to
locate the tumor position in each fluoroscopic image,
or to identify if the tumor is at/near the reference
PHS 398 (Rev. 05/01)
D.2.3.1. Score-based gating
Our preliminary work belongs to this strategy
[112], which will be extended in this proposal. We
plan to examine three scoring schemes:
(1)
s  R  f (Ti ) ,
s  f ( R  Ti ) ,
(2)
and
(3)
s  f ( R, Ti ) , in increasing generality. Here, R is the
ROI in a measured fluoroscopic image, Ti is the i-th
reference template (such as one of EOE templates in
the EOE gating window), the sign  represents
correlation, s is the score used for generating the
gating signal, i.e., g  H ( s  s0 ) , where s0 is the
threshold score, H ( x ) is the Heaviside step function.
H ( x )  0 for x  0 and H ( x)  1 for x  0 . The
gating signal g  1 means beam on while
g  0 means beam off.
Scheme 1: s  R  f (Ti ) . Apparently, our
preliminary work is a special case of this scheme,
with f (Ti )  T  Ti . This work can be improved by

i
applying automatic thresholding to the score to
generate the gating signal. As shown in figure x, an
ROI containing the tumor for all breathing phases is
selected. This ROI is determined based on the
registration of DRF to the fluoroscopic image during
the patient setup session. Assume we plan to gate
the treatment at EOE with 35% duty cycle. Using the
fluoroscopic images acquired in the setup session,
we can determine the intensity threshold to have
35% duty cycle, as shown in figure x. All images with
average ROI intensity value less than this threshold
are then motion-enhanced and averaged to generate
the reference template. During the treatment, the
correlation score between the reference template
and each motion-enhanced frame of the fluoroscopic
images is calculated. High correlation score indicates
that the ROI contains an image that is similar to the
Page 33__
Principal Investigator/Program Director (Last, first, middle):
Jiang, Steve Bin
reference template, i.e., the tumor is in the gating
window. In the setup session, the intensity threshold
can be translated into the correlation score threshold.
This threshold for correlation score is then applied
through out the whole treatment fraction. This
procedure is illustrated in figure x.
Figure x. A frame of setup fluoroscopic images. Left:
original image. Right: motion-enhanced image. The
selected ROI is shown in both images as a rectangular
region containing tumor positions of all phases.
Figure x. Same as figure x, except for a different
patient.
Scheme 2: s  f ( R  Ti ) . In stead of
combining all EOE templates into one reference
template, we can compute the correlation score of
each template with the ROI of the fluoroscopic
images, i.e., si  R  Ti , then combine the scores.
There are various ways to combine the correlation
scores to generate a more robust score. We could
simply find the total correlation overall s 
si to

i
Figure x. The average intensity in the ROI shown in
figure x as a function of time (top row). The first 12
seconds (as indicated by the vertical line) is used to
simulated setup session while the rest images are used
to simulated treatment. The image intensity threshold is
determined to have 35% gating duty cycle in the setup
session. The threshold for correlation score (middle row)
is automatically translated from the intensity threshold
and then used for generating gating signal (bottom row)
for the treatment session.
This method works well for some patients.
However, we observed that with this simple
implementation of score-based method, gating could
be erratic, as shown in figure x. We plan to
investigate other forms of combining templates into
one reference template to improve the accuracy and
robustness of the score-based gating.
PHS 398 (Rev. 05/01)
serve as our scoring function. In a more elegant way,
we plan to apply a voting scheme to generate the
final score. Voting several scores in a sense can
provide smoother scores, and hence, can give us
smoother gating boundaries. The idea of combining
the scores through voting is inspired by bagging
classifiers [119]. Bagging is an ensemble technique
for combining multiple classifiers. The idea is to
generate different expert classifiers by diversification
through random sampling with replacement of the
training data and then take the majority vote as the
final classification. Breiman has proved that bagging
improves the performance of the base classifier
[119]. In our case, we diversify by random sampling
different EOE templates. We will also investigate the
performance, if we pick representative templates at
fixed phase points within the EOE gating window. If
we have images more than one cycle in setup
session, we can average all templates at the same
breathing phase point to generate Ti .
When we only use templates in the gating
window for correlation calculation, what we know is
how close the tumor is to the reference position. If
we also use other parts of the phase cycle to
Page 34__
Jiang, Steve Bin
multivariate Gaussians. Note that in all cases,
training (or estimating the parameters and decision
surfaces) is done offline. During on-line treatment,
we simply look at where the test instance, R , is and
then make our decision. Each of these classifiers
provides the output: turn on or turn off. They also
have versions that provide the decision confidence.
For
example,
for
SVMs,
s  f ( R, Ti )  s supportvectors  i yi K ( si , R)  b , where
Principal Investigator/Program Director (Last, first, middle):
generate multiple templates, then we may have a
better idea of the tumor motion during the whole
breathing cycle and then generate gating signal in a
more robust way. If we know that the tumor image is
in the inhale state, then it cannot be in the EOE
state. We will develop techniques that combine
correlation scores from templates at various
breathing phases (e.g., templates from inhale,
exhale, and intermediate phases). As a simple
example, we can the correlation scores as:
where TEOE
s  [ R  f (TEOE )] [invert ( R  f (TIN ))] ,
and TIN are reference templates developed at EOE
and inhale phases, respectively, and invert means
multiply the correlation signal by 1 or invert the
signal. We plan to try even more templates and test
various ways to combine their scores to generate
robust gating signal.
Scheme 3: s  f ( R, Ti ) . Schemes 1 and 2
both use correlation scores. In general, we can take
advantage of other functions of R and Ti . In
Schemes 1 and 2, which are actually special cases
of Scheme 3, we apply a threshold to the final score
to generate gating signal. Apparently, the ultimate
goal is to determine the gating signal (when to turn
the radiation beam on or off). We either turn the
beam on when we are in the gating window or turn it
off otherwise. This exclusivity condition provides us
with a clue that the gating problem can actually be
recast as a classification problem. This opens a vast
resource of classification algorithms from the
machine learning literature that we can explore, such
as support vector machines (SVM) [120], decision
trees [121], Bayes classifier [122]. An SVM projects
instances into high dimensional space via kernels
and then learns a linear separator that maximizes the
margin between the two classes. Kernels allow SVM
to perform dot products in high dimensional space by
working on kernel functions in the low dimensional
space, thus avoiding the computational cost in high
dimensions. SVMs have good theoretical properties;
it learns an optimal bound on the expected error, and
finds an optimal solution as opposed to many
learning algorithms that provides local optima (such
as, neural networks [123]). SVM has shown success
and demonstrated good generalization performance
in several classification tasks, examples are gene,
text, and character recognition. We will also try
decision trees, which build axes parallel decision
boundaries of the feature space. A Bayes classifier
provides the optimal decision rule if we know the true
probability distributions of each class. Since we do
not know these distributions, we will estimate them
by assuming that each class comes from a mixture of
PHS 398 (Rev. 05/01)

i
yi is the class for template Ti in the training set,  i
is the Lagrange multiplier, K () is the kernel function,
and b is the offset. For decision trees, score can be
computed as the proportion of class ON in the leaf
node that R belongs to. For Bayes, score is equal
to the probability of class ON given R ,
s  p(classON R) .
For all score-based methods, the final score
used to generate gating signal will also be used for
treatment monitoring. The score, along with the preset threshold value, will be graphically displayed on
the screen of the control computer. If the score
cannot reach the threshold, i.e., beam cannot be
turned on, for several breathing cycles, the therapist
will interrupt the treatment and re-set the patient.
This is part of SA3.
D.2.3.2. Location-based gating:
Figure x. Twelve motion-enhanced tumor templates
built by averaging the images in ROI (as shown in
figure x) falling in the same bin.
The second strategy is to perform lung tumor
tracking so as to identify the tumor location and then
generate gating signal. The tumor location results
here serve as internal signal inputs for lung tumor to
Page 35__
Jiang, Steve Bin
match to the image, and among all the 12 templates,
Ti generates the highest correlation score. For each
frame of fluoroscopic images, we perform this
operation to identify tumor position, so the tumor
position can be tracked continuously.
Figure x is a plot of the best correlation
scores for each template with respect to each frame
of fluoroscopic images. The y axis is the template ID,
the x axis is fluoro frame ID (time), and the grayscale
value signifies the correlation score. The brighter the
pixel is, the higher is the score. Note that the
correlation score in general cycles through the
different templates as one would expect. At each
frame the tumor region is highly correlated with
several templates (bright intensity values along a
vertical line in the figure). This is due to the fact that
tumor shapes in neighboring phases are similar.
Therefore,
instead
of
calculating
( xt , yt )  ( xi   xi , yi   yi ) using only one template
Principal Investigator/Program Director (Last, first, middle):
the methods developed in SA2. A simple tracking
approach is to simply perform an exhaustive search
and to find the highest correlation between a tumor
template and an image region. The image region
with the highest correlation to the template is the
tracked result. Preliminary experiments show that
this leads to erratic locations. When we adaptively
update the tumor template at time t , T (t ) , to be
equal to the tracked region at time t 1 , R (t  1) , the
tracking results could be improved. The reason is
that the tumor's appearance changes with time.
However, we do not want to use the previous
estimate as the template to search for the tumor in
the next frame, because this is not robust to errors
made in previous frames and the tracking may drift.
Ti , a more robust way might be to weight the location
estimates from all templates that generate high
correlation scores (above a pre-set threshold):
( xt , yt ) 
Figure x. Left: the correlation score (in gray scale) as
functions of template ID (y-axis) and the measured
image frame ID (x-axis). Right: the estimated tumor
position as a function of time.
Tumor shape projected in the images may
vary throughout the breathing cycle. A common
problem in object recognition is to be able to detect
the object at various poses (due to changes in
rotation, scale, illumination, and other factors). One
way to deal with this problem is by building templates
for these different poses [124]. We propose to apply
multiple templates to represent every fix duration
intervals within one breathing cycle. In a preliminary
study, we sample twelve templates, T1 , T2 , T12 at
equal time intervals, based on the intensity signal
from the setup session, as shown in figure x. We
compute the cross-correlation between each
template, Ti (i  1, 2, 12) , and each measured
fluoroscopic image by allowing the template to shift
(  x ,  y ) along the x axis and y axis. The x and y
coordinates of the tumor centroid for template Ti ,
( xi , yi ), are determined during patient setup by
registering DRFs to setup fluoroscopic images. The
tumor location at this time point is then given as
( xt , yt )  ( xi   xi , yi   yi ) , where (  xi ,  yi ) is the
shift required for template Ti to produce the best
PHS 398 (Rev. 05/01)
1
 wi
 w (x   x , y   y )
i
i
i
i
i
i
where wi is the weighting factor for template Ti . We
will try various ways of computing the weighting
factor, such as the normalized correlation score, the
truncated Gaussian function of correlation score, etc.
A simple way is averaging, i.e., setting wi  1 for the
templates with scores above the threshold and
wi  0 for the templates with scores below the
threshold. Figure x shows the estimated x and y
coordinates of the tumor centroid versus time, by
averaging templates with correlation scores above
95% of the maximum score.
D.2.3.3.Several directions for improving the
basic approaches mentioned above
1. Methods to generate templates.
We plan to find out the optimal and practical
way of generating templates for each imaging angle.
We will investigate three options. The first option is to
use DRFs to generate templates. This method is
handy. However, the low spatial and temporal
resolution, as well as the different image quality, of
DRFs, will pose technical challenges when using
them to match fluoroscopic images to generate
scores or locations for gating. The second option is
to acquire some fluoroscopic images for each
imaging angle during the treatment simulation or
Page 36__
Jiang, Steve Bin
dimensions (irrelevant pixels can lead to poor
matches). For similar reasons, we will also apply
these dimensionality reduction techniques before the
classification techniques under score-based Scheme
3.
Principal Investigator/Program Director (Last, first, middle):
before the first treatment fraction. The third option is
to update the templates adaptively during the
treatment. For example, we can generalize f (Ti )
and include the current EOE templates for averaging
and tapering off the influence of EOE templates that
occurred earlier. All three methods will be
investigated.
2. Improve the speed for finding the tumor
region and dimensionality reduction.
Performing an exhaustive search of the entire
fluoroscopic image to find the best match for each
twelve template is time consuming. We can speed up
the search process by searching only within a
surrounding area of  pixels from the previous
location. Another strategy for speeding up is by multiscale search, such as in [125, 126].
A typical template is around 100 pixels x 100
pixels in dimensionality. Another means of reducing
the computational complexity (which translates to
increase in speed) is by applying dimensionality
reduction techniques (e.g., principal component
analysis (PCA), local linear embedding (LLE), kernelPCA). PCA finds a linear transformation, Y  AT X ,
that projects the original high-dimensional data X
with d dimensions to Y with q dimensions where
q  d , such that the mean squared error between X
and Y is as small as possible. X , here is d  n ,
where n is the number of data points, and A is a
d  q matrix. The solution is the transformation
matrix A whose columns correspond to the q
eigenvectors with the q largest eigenvalues of the
data covariance. PCA finds a global linear
transformation. LLE finds an optimal nonlinear
transformation to a low-dimensional space or
embedding that preserves the local neighborhood
structures. LLE exploits local symmetries by
minimizing the reconstruction mean squared error
between a data point xi and weighted combinations
of its neighbors. Kernel-PCA is a generalized version
of PCA that allows nonlinear transformations but
avoids the combinatorial explosion of time. After any
of these transformations, matching will then be
computed with Euclidean distance rather than crosscorrelation.
We will investigate each of these
methods. The idea for applying PCA to images or
eigen images was introduced by Turk and Pentland
for face recognition and has shown to be successful
in other object recognition tasks [127]. Not only do
the dimensionality reduction methods improve speed,
they can also improve the tracking performance
because they only keep the most informative
PHS 398 (Rev. 05/01)
3. Intelligent means for selecting multiple
templates.
The basic algorithm described earlier is to
generate multiple templates by sampling at a uniform
rate. We can intelligently pick representative
templates by clustering the possible set of templates.
Clustering is the process of grouping similar objects
together. The idea is to group similar templates and
use the group's mean to represent that cluster. We
will apply k-means clustering [128] or a finite
Gaussian mixture model [129, 130] to perform this
task. We can perform the task of dimensionality
reduction and clustering together through a finite
mixture of probabilistic PCA [131, 132]. When
clustering, we will apply techniques that also
automatically finds the number of clusters using
penalty methods, such as Bayesian information
criterion [133]. A penalty term is needed because the
maximum likelihood estimate increases as more
clusters are used. Without the penalty, the likelihood
is at a maximum when each data point is considered
as an individual cluster (a case of overfitting).
4. Probabilistic model for the breathing
cycle.
Another way to perform tumor tracking is to
build and take advantage of a model for the
breathing cycle. In our preliminary work, we showed
that the breathing cycle could be segmented into
different states: exhale, end-of-exhale, inhale and
irregular states, as shown in figure x. Observe that
given that the current state is exhale, the next state
can only be either exhale again, end-of-exhale, or
irregular state. Also, we do not need to know what
the other states are given the previous state to
predict the next state. We say, that this random
process observes a first-order Markov property,
P(qt 1 q1 , q2 , qt )  P(qt 1 qt ) , i.e., the current state
is only dependent on the previous state, where qt is
a random variable that represents the state at time t .
We can take advantage of this property to predict the
next state. The transitions from one state to another
can be modeled by a finite state machine as shown
in figure x. However, these states are not directly
observable (``hidden''); we only observe the image of
the tumor region. We will investigate hidden Markov
models (HMMs) [134] for modeling the sequence of
Page 37__
 
observed image regions. Let A  aij
transition
probability
matrix,
be the state
where
aij  P(qt 1  S j qt  Si ) , for 1  i, j  Q . Based on
the models shown in figure x, aij  0 only on
transitions represented by the arrows. We will model
the probability of the observed image region given
each state, P( Rt qt  Si ) , as a finite mixture of
multivariate Gaussians. In this case, we need to first
extract features from the image. One simple way is
to apply PCA presented earlier. We will automatically
find the number of mixture components through the
Bayesian information criterion described in (2) above.
The parameters will be estimated through the
expectation-maximization algorithm [135], and will be
computed during training only.
During the treatment session, we apply the
HMM model and the previous observation sequence
to predict the next state, qˆt 1  Snext . Then, we can
compute for the probability of observing a candidate
image region given the estimated next state,
P( Rt 1 qt 1  Snext ) . The image region with the
highest probability becomes the tracked result.
Predicting the next state, narrows down the possible
``templates'' to match. Unlike the previous models,
HMM takes advantage of previous states.
Computing P( Rt 1 qt 1  Snext ) for a finite mixture
model is analogous to a weighted correlation score
for each template within a state, s  f ( R  Ti ) . If we
model the HMM such that each state generates
image regions from a single Gaussian distribution,
then computing P( Rt 1 qt 1  Snext ) is analogous to a
correlation score for the single most likely template.
At the expense of speed, but with the gain in
accuracy, we will also examine computing
P( Rt 1 Rt , Rt 1 , , R1 , qt 1  Si ) for each candidate
region Rt 1 and state Si . And return the location for
the highest probability score among all the states Si .
This is the probabilistic formulation for finding the
best matching region for each possible next state
and is analogous to finding the best match among
different templates.
D.2.3.4. Validation for robustness
Like in most object tracking approaches,
there is a chance of losing track of the tumor. We
will, thus, validate the tracked results to make sure
that we are not drifting. We can use the correlation
score for validation. If the best correlation score is
PHS 398 (Rev. 05/01)
Jiang, Steve Bin
low, we warn the system, and ask the operator to recalibrate. We can also apply classification methods
pointed out in score-based gating (such as, support
vector machines, decision trees, Bayes classifier) to
check whether the selected region contains the
tumor or not, and again even provide confidence
scores to warn the operator when to re-calibrate.
This validation mechanism will be applied both in the
score-based and location-based gating.
Principal Investigator/Program Director (Last, first, middle):
D.2.4. Evaluation
We will build a training set with ``ground
truth'' location values by asking attending/resident
radiation oncologists to retrospectively mark the
tumor contours/centroids in measured fluoroscopic
images. Then, we can apply mean-squared-error to
measure performance. Labeling many frames of
fluoroscopic data can be very time consuming. To
minimize the number of image frames needed for
labeling, we will apply active learning techniques
[122, 136, 137]. Active learning deals with the
problem of selecting data points for labeling given a
large set of unlabeled data. Typical strategies are to
pick representative points and points that are difficult
to label. Representative points can be selected by
applying clustering and choose the points near the
cluster means to be labeled. Points that are difficult
to label are points near the boundary. These too will
be chosen for labeling. We will create an interactive
environment to facilitate this labeling process, and at
the same time take advantage of the few labeled
examples currently stored to query new samples to
be manually labeled through active and semisupervised learning methods [138, 139]. This
environment will be part of SA3. We will explore and
develop various strategies for active learning in the
context of location tracking.
D.3. SA2: To develop tools for combining internal
gating with external gating
D.3.1. Patient data
For this SA, we need patient data with
measured internal and external marker positions for
the development and test of the proposed tools. For
lung tumor, we have measured internal/external data
from our collaborators in Japan[113]. For abdominal
and pelvic tumors, we will collect data from serial
weekly 4D CT scans for patients undergoing
conventional radiation therapy with the abdominal
and pelvic malignancies including cancer of the
pancreas, liver, rectum, uterine cervix, and
endometrium. This is an IRB-approved clinical study
led by Dr. Theodore S. Hong, who is also a co-
Page 38__
Jiang, Steve Bin
calibration would be necessary in order to adapt to
changing breath patterns. Assuming that external
marker positions are correlated with internal tumor
positions to some degree, one might expect some
improvement in the predictability of internal position
by including the external signal in the above
formulation.
We propose to explore extending standard
tumor motion models to include an external signal in
order to improve future internal position predictability
and/or reduce the internal signal sampling rate.
However, standard tumor motion models exhibit one
general shortcoming: the model itself dictates the
form of the predicted tumor motion (linear,
sinusoidal, etc.). For regular breath patterns (such
as those obtained through the result of breath
coaching), such models may well approximate actual
breath patterns, given an appropriate breathing
model. However, for irregular or varying breath
patterns, such models would likely yield poor results
or require constant local re-calibration.
With the above in mind, we propose to
explore an alternative approach as well. Instead of
dictating a fixed (parametric) motion model and
finding those parameters which fit the data as well as
possible, we propose to explore general motion
models which are expressive and encompass many
natural motion behaviors and to let the data itself
dictate the exact motion model and associated
parameters. A promising recent approach of this type
was devised by Tao, et al[140]. The motion of an
object is treated as a sequence of points in time
(e.g., {( x1 , y1 , z1 ),( x2 , y2 , z2 ), ,( xt , yt , zt )} ), and the
general motion model is a linear recurrence over
retrospective points. For example, in one dimension
y using r  3 retrospective points, we would have
Principal Investigator/Program Director (Last, first, middle):
investigator of this proposed project. The study
includes 10 patients of abdominal tumors and 10
patients of pelvic tumors. For each patient, the 4D
CT scan is performed weekly for a maximum of 6
additional scans. For patients with pancreatic tumors,
oral contrast will be administered thirty minutes prior
to CT scan to allow visualization of the duodenum.
RPM signal will be acquired during to the 4D CT
scan. The RPM signals and contoured tumor
locations in 4D CT data will be used to study the
inter-fraction variation of internal/external correlation.
Some of these patients will be treated with gated
radiotherapy. During the treatment, synchronized
RPM data and fluoroscopic data will be acquired
weekly as part of treatment to assess tumor motion
and motion pattern change during the treatment
course. These data will also be used retrospectively
for this study.
D.3.2 A general framework for combining
internal and external signals
The correlation between the locations of an
external marker and an internal tumor can be studied
and modeled in a number of ways. By far, a linear
model is the simplest: an internal tumor position
( x , y ,or z ) is modeled as a linear function of the
external marker position. Such linear functions can
account for differences in shift and scale, but they
cannot account for differences in the phase between
the external and internal position signals, and it is
well known that such phase differences often
exist[71]. Modeling the internal tumor and external
marker positions by sinusoidal functions of time allow
one to account for shift, scale, and phase
differences, and such “cosine” models (and their
generalizations) are often used to model breath
patterns and hence internal tumor (and external
marker) positions[19, 114]. A generalized cosine
model is given below
x(t )  a1 cos (t  1 )  b1
2n
y (t )  a2 cos 2 n (t   2 )  b2
z (t )  a3 cos 2 n (t   3 )  b3
e(t )  a4 cos 2 n (t   4 )  b4
where  , a , b , and  correspond to angular
velocity, scale, shift, and phase parameters,
respectively, and n is a parameter dictating the
order of the cosine model. One could employ such a
model to predict future internal tumor positions by
first finding the model parameters which fit a set of
historical points as well as possible, and then using
these model parameters and the above equations to
predict future points.
Constant or periodic rePHS 398 (Rev. 05/01)
yt 1  c0  yt  c1  yt 1  c2  yt 2
Such recurrences can model quite complex
motion behavior. For example, given appropriate
constants c0 , c1 , and c2 , the above recurrence can
perfectly model any quadratic motion of y as a
function of t ; in general, any polynomial motion of
degree d can be perfectly modeled by a linear
recurrence using d  1 retrospective points. By
examining the Taylor series expansion of any
candidate motion function, one can find an
appropriate polynomial (and hence linear recursive)
approximation to any desired degree of accuracy,
given a sufficient number of retrospective points.
Simultaneous linear recurrences in multiple
dimensions can capture dependent motion in the
associated space; for example, the following two-
Page 39__
Jiang, Steve Bin
provided by one or more external signals can be
incorporated easily and naturally by simply adding
one or more dimensions (in addition to x , y ,or z ) to
the above formulation. (4) The formulation given
above can be adapted to predict positions at time
points arbitrarily far in the future (with concomitant
increasing prediction error, of course) and from
partial internal signals and mixed (partial internal, full
external) signals. We propose to investigate this
model in addition to standard motion models in order
to accomplish our specific aims: the reduction of
imaging dose through the use of an external signal.
A number of preliminary results obtained through the
use of this model are described in what follows.
Principal Investigator/Program Director (Last, first, middle):
dimensional recurrence using two retrospective
points models circular behavior in the x  y plane
 xt 1  1  cos( )  sin( )  cos( ) sin( )   xt 
 y   sin( ) 1  cos( )  sin( ) cos( )   y 
 t 1   
 t 
 xt  
1
0
0
0   xt 1 
  
 
0
1
0
0   yt 1 
 yt  
where the recurrence is now expressed in matrix
form. The d  r  d  r matrix on the right-hand side of
this equation is referred to as the motion matrix K ,
where d now refers to the dimensionality of the
space. By appropriately modifying the above motion
matrix K , one can perfectly model arbitrary elliptical
motion in the x  y plane, and thus a twodimensional recurrence with two retrospective points
encompasses the standard cosine motion models.
By using an appropriate number of retrospective
points, the above model can encompass (or
approximate) arbitrarily complex motion in twodimensions, and the model can be extended to threedimensional space by adding the appropriate
recurrences for z .
Finally, the form of the motion (and the exact
model parameters) can be inferred from the data by
solving for the K matrix using historical data. For
example, given h historical points, one could solve
for the motion matrix K which “best fits” the
following matrix equation:
 xt
 y
 t
 xt 1

 yt 1
xt h 3   k11
yt h 3   k21

xt h  2   k31
 
yt h  2   k41
k12
k13
k22
k32
k42
k23
k33
k43
k13   xt 1
k24   yt 1

k34   xt 2
 
k44   yt  2
xt h  2 
yt h  2 
xt h 1 

yt  h1 
If the quality of fit is measured by mean squared
prediction error, then the best fit motion matrix K
can be determined by simple matrix operations on
the above system.
Such an approach has many potential advantages
for our problem. (1) The form of the motion is
inferred from the data itself; it is not dictated by a
fixed motion model. Given a sufficient number of
retrospective points, a very general class of motion
models is subsumed by the above formulation, and
training on historical data infers the “best” motion
model. One can also limit the form or class of motion
models considered by placing constraints on the
motion matrix and solving for K using constrained
optimization techniques (cite). (2) Future tumor
positions are predicted from positions in the recent
past, not from a globally derived and fixed motion
equation. Thus, the model described can more easily
adapt to changing local behavior. (3) The information
PHS 398 (Rev. 05/01)
Image
et , i t
et , i t
Memory
Monitoring
( yes / no )
et , et1 , et2 ,…
it , it1’ , it2’ ,…
et ,
et ,et1 ,et2 ,…
et ,
et ,
Model
Learning
Treatment
parameters
Prediction
et+1 , it+1
Figure x. A block diagram showing a general scheme
of combining external signal with internal signal for
gated radiotherapy.
A block diagram of our proposed system is
shown in figure x. The historical (past) external and
internal signal is stored in a memory module. The
current external and internal signal (if present) is
passed to an operator monitor and graphically
displayed. This module serves the purpose of
treatment monitoring which is part of SA3. Based on
this current information, the operator may choose to
halt treatment and re-calibrate if current (external
and/or internal) signal is outside the tolerance range.
The historical external signal is passed directly to a
prediction module when using scheme G1 (external
gating with internal verification) wherein gating is
determined by the external signal alone. The
historical external and internal signals are passed to
a model learning module which infers a motion model
and its associated parameters. A future internal (and
external) position prediction is made using the
inferred motion model (and possibly current and past
position information). Depending on the type of
gating employed, these predictions can dictate
treatment and/or dictate whether a new internal
image is taken.
Page 40__
Jiang, Steve Bin
size should be the turning point on the curve shown
in figure x. More work will be done on the issue.
Principal Investigator/Program Director (Last, first, middle):
With this system diagram in mind, we next
describe possible implementations of each of the
four gating strategies described earlier: (1) external
gating with internal verification [G1], (2) double
gating [G2], (3) hybrid gating with minimized imaging
[G3], and (4) hybrid gating with adaptive imaging
[G4]. We propose to study these implementations
and others with the end goal of accurate treatment
using a minimal imaging dose.
D.3.5. G3: Hybrid gating with minimized
imaging
D.3.3. G1: External gating with internal
verification
The gating scheme G1 is similar to the IGRG
technique developed at MGH. The major difference
is the internal verification part. Images will be taken
within the external gating window. The minimal
imaging rate will be studied by simulating the
treatment using measured data. For treatment
monitoring and verification, the display of the
detected marker/tumor location or correlation score,
along with the reference position or score, on the
control computer’s monitor is part of SA1.
D.3.4. G2: Double gating
Figure x. The internal
(treatment) duty
cycle as a function of
external (imaging)
duty cycle for double
gating, computed
using measured
internal and external
marker data.
For double gating, the major development
work is the interface between the software
infrastructure and the x-ray generators and linac, for
gating the generators and linac. This is part of SA3.
We also need to determine the minimal imaging rate
within the external gating window, using methods
similar to G1. Another research issue is to determine
the optimal size of external gating window. If external
gating window is too small, then the treatment duty
cycle is low. On the other side, if external gating
window is too large, then the imaging duty cycle is
large which means too much imaging is given. The
treatment duty cycle is also limited by the internal
gating window. That means, after a threshold point,
the treatment duty cycle saturates and will not
increase with the increase of external gating window
size. Therefore, the optimal external gating window
PHS 398 (Rev. 05/01)
Page 41__
Figure x. Estimated internal tumor position using: (top)
internal signal (3Hz) alone, (middle) internal signal
(3Hz) and external signal (30Hz), and (bottom) external
signal (30Hz) alone.
Jiang, Steve Bin
to the lack of a regular internal signal acquisition
effectively used for re-calibration.
While
our
preliminary
results
are
encouraging, many question yet remain. For a given
prediction error target (e.g., RMS error threshold or
95 confidence interval), what is the minimal required
internal sampling rate?
In the context of the
recursive motion model described above, what
combination of retrospective points (used for
prediction) and historical points (used for training)
are required to achieve the minimal sampling rate?
More generally, how can other motion models be
extended to predict future tumor locations from a
mixed external and partial internal signal? We
propose to study these questions and others.
Principal Investigator/Program Director (Last, first, middle):
In this gating methodology, our goal is to use
the full external signal sampled at a fairly high rate
(e.g., 30Hz) while making use of an internal signal
sampled at a much lower, though constant, rate
(e.g., 3Hz). Lowering the rate at which the internal
signal is sampled will reduce the imaging dose, but at
a cost of increased tumor position prediction error
possibly leading to mistreatment. If the internal signal
is highly correlated with the external signal, then the
full or partial loss of the internal signal need not
increase prediction error greatly; if the internal and
external signals are not highly correlated, then the
presence of a full external signal cannot replace the
loss of much or all of the internal signal. Thus, one
of our research goals is to establish the correlation
between the internal and external signals for various
tumor and external marker locations. Our preliminary
experiments have shown that the internal and
external signals are correlated to some degree and
that the external signal can be effectively used to
compensate for a loss of internal signal. In figure X,
we show the results of predicting the internal y-axis
tumor motion 330ms in the future from an internal
signal captured at a rate of 3Hz. We used the
recursive motion model described earlier by inferring
the motion matrix from points within the past 660ms
(history) and predicting from points within the past
66ms (retrospect). (The motion model provides
predictions for internal x , y ,or z . For the data used,
the y motion (S-I direction) was most prominent, so
we describe those results.) Note that the sharp
peaks in the bottom-half of the plot correspond to the
transition between inhale and exhale, while the softer
peaks in the top-half of the plot correspond to end-ofexhale (EOE). It can be seen that the prediction of
tumor position is quite good except in this EOE
range. Unfortunately, this is the range where gated
treatment usually occurs; thus, it is most important to
make accurate predictions in this range. Conversely,
consider the results given in figure x. Here the
training and prediction is identical except that an
external signal sampled at a rate of 30Hz is added to
the model. Note that the predictions made in the
critical EOE range are vastly improved. However, the
external signal alone is insufficient to predict the
internal signal accurately (See figure Z.) Here a
motion matrix is trained in the manner described
above; however, future prediction are made from the
actual external signal and the inferred past internal
signal. Thus, an internal signal is only used for a
short time (during training), and future predictions are
effectively made from the external signal alone. Note
the eventual degradation in prediction accuracy due
PHS 398 (Rev. 05/01)
D.3.6. G4: Hybrid gating with adaptive
imaging
In this gating methodology, our goal is to use
the full external signal sampled at a fairly high rate
(e.g., 30Hz) while making use of the internal signal
only “when necessary” to re-calibrate the motion
model. How can one determine that re-calibration is
likely necessary without sampling the internal signal?
One approach using the recursive motion model
described above is as follows. Given the external and
(partial) internal signal captured in the past, the
recursive motion model predicts future internal and
external positions: the external signal is treated as
another “axis”, and the motion model effectively
predicts movement in the resulting four (or higher)
dimensional space. One can compare the predicted
external position with the actual external position to
infer the local accuracy of the motion model. By
correlating the internal and external prediction errors
on historical data, one could conceivably determine
an external error threshold beyond which a given
internal error is likely to have occurred. At such
times, one could then capture internal signal in order
to re-calibrate the motion model (by solving for a new
K matrix). One complication arises in the potential
use of recursive motion models as described.
Recursive motion models most often assume that the
input signals are sampled at uniform rates. If the
internal signal were sampled at a non-uniform rate,
there would be data “missing” for prediction and
training.
One possible solution is to use the
predicted internal positions as a surrogate for the
missing actual internal signal; thus, future positions
would be predicted from past external, internal, and
predicted internal data. We propose to study such
approaches and the use of other motion models as
well with adaptive internal imaging.
Page 42__
Jiang, Steve Bin
static ribcage. It will be used to exercise SA2 and
SA3.
Principal Investigator/Program Director (Last, first, middle):
D.4. SA3: To develop a software infrastructure
for gated radiotherapy
A
software
infrastructure
for
gated
radiotherapy will be built to integrate the developed
tools into a streamlined gated radiotherapy
procedure, to facilitate collaboration between project
members, and to allow easy dissemination of tools.
Our infrastructure will be based on the IRIS Software
Environment (ISE), a software framework currently
under development as part of a funded NIH R21
research collaborative between MGH and Varian
Medical systems. ISE provides a set of key services,
including real-time dual fluoroscopic image
acquisition, real-time display, and real-time fiducial
marker tracking on the IRIS physical hardware.
However, additional software services are needed to
support gated radiotherapy. These include a virtual
IRIS machine, communication with new physical
devices, DRF generation, registration of fluoroscopy
with DRFs, an architecture for safety-critical
software, treatment monitoring, and statistics and
logging.
D.4.1. Hardware abstraction layer and virtual IRIS
machine
Because physical access to the IRIS
hardware platform is limited for collaborator at
Northeastern University (NEU), we propose the
implementation of a hardware abstraction layer and
virtual IRIS machine. Software will be used to
emulate a subset of the physical hardware, including
the imaging device, external sensor, x-ray generator,
and linear accelerator. The use of a hardware
abstraction layer also allows the ISE infrastructure to
support a variety of physical devices. For example,
the abstraction of the external sensor will support
optical systems and spirometry. Thus the developed
tools and infrastructure can be readily useful for
other investigators who may have hardware
platforms different from Varian RPM and linacs, and
MGH IRIS system.
The implementation of the virtual IRIS
machine requires the production of appropriate
images and external sensor input. We will provide
two sources for this: synthetic and recorded. The
recorded input source will replay images and data
from previously recorded sessions. It is more
accurate and realistic than the synthetic source, and
will be used to exercise SA1, SA2 and SA3. The
synthetic input source will generate a tumor
trajectory from mathematical models of tumor
motion. Synthetic images will depict one or more
implanted markers moving against a realistic, but
PHS 398 (Rev. 05/01)
D.4.2. High-speed device communication
The software infrastructure will support
communication and control of all input and output
hardware devices required for gated treatment. In
addition to the imaging panels, communication with
the external sensor, x-ray generators, and linear
accelerator will be developed. A Varian RPM camera
system will be used for tracking the external
surrogate, and will be controlled through the RPM
serial interface provided in RPM version 1.7. Two
CPI Indico 100 x-ray generators will be gated on and
off for internal imaging, and will be controlled using
custom hardware and a digital I/O card located in the
IRIS computer. A Varian Clinac 21EX linear
accelerator will be gated on and off for treatment,
and will be controlled through an Ontrak ADU208
relay box and other stock hardware. In addition to
basic communication, the software infrastructure will
provide configuration management, status checking,
user interface, and display feedback for each
devices.
D.4.3.DRF generation
Digitally reconstructed fluoroscopy (DRF) will
be used to match the tumor isocenter in the
treatment plan with fluoroscopy in SA1. This project
requires routines for generating DRF from 4DCT.
The basic procedure is identical to generating
digitally reconstructed radiographs (DRR) from a CT
scan [141]. However, to support automatic
registration, the conversion table from Hounsfeld unit
to radiographic attenuation (HUA) will be calibrated
to match the IRIS hardware [142]. Lookup tables for
the HUA will be generated for a range of energy
values, from 40-120 kVp, using a CT density
phantom. Another technical challenge is the
choosing of the fluoroscopy kV setting for both DRF
generation and fluoroscopic image acquisition, which
is dependent on many factors such as imaging
angle, tumor site, patient size, etc.. During the
patient setup session of the first treatment fraction,
kV settings can be tuned iteratively to achieve the
optimal image quality (such as the highest tumor
contrast in the image). This process can be time
consuming and also gives the patient unnecessary
x-ray dose. Therefore, instead of tuning the kV
settings on IRIS using an initial guess based on
previous experience, during the treatment planning
stage, we could determine an initial setting by
examining the tumor contrast in the motionenhanced DRF templates while interactively
changing the kV setting for DRF generation. The
Page 43__
Jiang, Steve Bin
methods that conform to industry standard practice
[144]. This includes design review, code review, unit
testing, integration testing, regression testing, source
code control, and defect tracking.
Design review: Documents describing the
application programming interface (API) and
functional capabilities of the software framework and
all software modules shall be documented and
reviewed. A formal design review meeting will be
held to identify and correct design errors.
Code review: Software source code that
implements the framework and all software modules
will be reviewed. A formal code review meeting will
be held to identify and correct errors in
implementation.
Unit tests: A unit test is a test of a procedure
or module of code. Unit tests exercise the
implementation
and
ensure
that
design
specifications are met. Because a detailed
knowleG2e of the code is required for unit test, the
same developer who implements a design provides
this testing.
Integration tests: Combining several units or
modules together is the function of integration
testing. This phase tests the combined functionality
of a group of units, checking interface compatibility
across all the anticipated contexts and intended uses
of the module.
Regression tests: Regression tests ensure
that new versions of the software do not break
previously working functionality. A set of tests for
standard operations is run, and the test results are
verified against previously expected results. Only
after a software version passes regression testing
should it be considered stable and suitable for
release.
Source code control: For both code
management and release of a sequence of versions,
we will use a source code control system. This will
allow project members to develop on multiple
branches independently and then to merge changes
into a common main trunk for major releases. All old
code remains in the system so that recovery is
possible, as is fixing bugs on a previously released
version and re-releasing it without dealing with
changes made since the previous release.
Defect tracking: A centralized bug tracking
system will be used to organize and prioritize
software defects and feature requests. Using a
defect tracking system improves software quality by
allowing developers to see which problems are still
outstanding, and prioritize problem resolution.
Principal Investigator/Program Director (Last, first, middle):
initial setting can then fine tuning during the patient
setup session. Software tools to facilitate this
function will be developed.
D.4.4.Interactive setup with DRFs
In addition to the automatic setup procedure
described in SA1, we will implement an interactive
registration procedure for matching the planning
target volume with the tumor mass visible in
fluoroscopy. This will be done by dragging the
treatment planning contours, or its DRF, over the
fluoro using a mouse. In addition to its use during
initial setup, these visualization capabilities will be
available throughout the treatment, for easy
monitoring and adjustment by the therapists.
D.4.5. Safety critical system architecture
Automatic control of radiation delivery
requires careful attention to safety. The software
infrastructure will implement a comprehensive
strategy for safety-critical system software, including
a dual-channel architecture, watchdog processes,
and watchdog timers [143]. For gating the linac, our
architecture will include dual redundant relay
hardware and software control. For imaging and
sensing, we will implement analytic redundancy to
confirm proper operation. If any sensor or actuator
failure is detected, then the radiation is disabled and
the system is placed in a safe state. We will guard
against unexpected software design errors using a
watchdog process. The watchdog process monitors
the state of the primary process, and throws a
hardware interlock if the primary process is not
responding or responds unexpectedly. In addition,
we will implement watchdog timers in the hardware
interface to the linear accelerator. The primary
software process is required to send a heartbeat
signal to the external hardware at regular intervals. If
the software fails to send a message within the time
limit, the hardware will throw an interlock.
D.4.6. Treatment monitoring
D.4.7. Statistics and logging
Decision processes used for automatic
control of the treatment and imaging systems must
be recorded for proper record keeping, and for postmortem analysis of system performance. Our
software infrastructure will provide a comprehensive
real-time statistics and logging subsystem to perform
this task.
D.4.8 Software engineering
The software infrastructure for gated
radiotherapy be developed and maintained using
PHS 398 (Rev. 05/01)
D.4.9. Software dissemination
Page 44__
Principal Investigator/Program Director (Last, first, middle):
To make this software available to widest
audience, we will release the software developed for
this proposal under an open source license. Major
releases will be available for download in source
code form on the world wide web, together with all
required configuration and make files necessary to
create executable programs on a remote system. In
addition, each release of the software will include an
updated set of documentation, including release
notes, a user guide, installation instructions, and a
manual for developers who wish to extend the
capabilities of the system. For documentation to be
both accurate and contemporary, it must be an
integral part of the design, implementation, and
release processes. The design description is the first
element of documentation and from this comes the
more detailed explanation of the purpose,
algorithms, and use of that piece of software.
Documentation will be written in an open format,
such as XML, capable of producing output formats
appropriate for both printing and online browsing.
PHS 398 (Rev. 05/01)
Page 45__
Jiang, Steve Bin
Principal Investigator/Program Director (Last, first, middle):
Jiang, Steve Bin
D.5. Project Milestones
SA2: Gated treatment using internal and external signals
R21/ISE: Internal
targeting with markers
SA2: Gated treatment using internal and external signals
SA1: Internal targeting
without markers
R21/ISE: Internal
targeting with markers
SA3: Hardware Abstraction Layer
SA3: Clinac
21EX Linear
Accelerator
SA3:
CPI X-Ray
Generators
R21/ISE:
PaxScan
4030A Imagers
SA1: Internal targeting
without markers
SA3: Hardware Abstraction Layer
SA3:
RPM External
Surrogate
SA3: Virtual
Linear
Accelerator
SA3: Virtual
X-Ray
Generator
SA3: Virtual
Imaging
Panels
SA3: Virtual
External
Surrogate
Figure 2. The software infrastructure provides services for gated radiotherapy through a hardware
abstraction layer, which can be used on the IRIS physical hardware (left) or a virtual IRIS machine
(right).
PHS 398 (Rev. 05/01)
Page 46__
Principal Investigator/Program Director (Last, first, middle):
PHS 398 (Rev. 05/01)
Page 47__
Jiang, Steve Bin
Principal Investigator/Program Director (Last, first, middle):
PHS 398 (Rev. 05/01)
Page 48__
Jiang, Steve Bin
Principal Investigator/Program Director (Last, first, middle):
e.
Human Subjects
There will be no human subjects used in this work.
All patient data utilized in our system will be fully
anonymized, will conform to HIPPA standards and
will also be subject to the standards in place by the
submitting biomedical research laboratory at MGH.
f.
Vertebrate Animals
N/A
PHS 398 (Rev. 05/01)
Page 49__
Jiang, Steve Bin
Principal Investigator/Program Director (Last, first, middle):
11.
g.
Literature Cited
h.
Consortium/Contractual Arrangements
1.
Jacobs, I., J. Vanregemorter, and P. Scalliet,
Influence of respiration on calculation and delivery
of the prescribed dose in external radiotherapy.
Radiother Oncol, 1996. 39(2): p. 123-8.
Engelsman, M., E.M. Damen, K. De Jaeger, K.M.
van Ingen, and B.J. Mijnheer, The effect of
breathing and set-up errors on the cumulative
dose to a lung tumor. Radiother Oncol, 2001.
60(1): p. 95-105.
Langen, K.M. and D.T. Jones, Organ motion and
its management. Int J Radiat Oncol Biol Phys,
2001. 50(1): p. 265-78.
Ozhasoglu, C. and M.J. Murphy, Issues in
respiratory motion compensation during externalbeam radiotherapy. Int J Radiat Oncol Biol Phys,
2002. 52(5): p. 1389-99.
Bortfeld, T., K. Jokivarsi, M. Goitein, J. Kung, and
S.B. Jiang, Effects of intra-fraction motion on
IMRT dose delivery: statistical analysis and
simulation. Phys Med Biol, 2002. 47(13): p. 220320.
Jiang, S.B., C. Pope, K.M. Al Jarrah, J.H. Kung, T.
Bortfeld, and G.T. Chen, An experimental
investigation on intra-fractional organ motion
effects in lung IMRT treatments. Phys Med Biol,
2003. 48(12): p. 1773-84.
Chui, C.S., E. Yorke, and L. Hong, The effects of
intra-fraction organ motion on the delivery of
intensity-modulated field with a multileaf
collimator. Med Phys, 2003. 30(7): p. 1736-46.
Kung, J.H., P. Zygmanski, N. Choi, and G.T.
Chen, A method of calculating a lung clinical target
volume DVH for IMRT with intrafractional motion.
Med Phys, 2003. 30(6): p. 1103-9.
Ross, C.S., D.H. Hussey, E.C. Pennington, W.
Stanford, and J.F. Doornbos, Analysis of
movement of intrathoracic neoplasms using
ultrafast computerized tomography. International
Journal of Radiation Oncology, Biology, and
Physics, 1990. 18: p. 671-677.
Hanley, J., M.M. Debois, D. Mah, G.S. Mageras,
A. Raben, K. Rosenzweig, B. Mychalczak, L.H.
Schwartz, P.J. Gloeggler, W. Lutz, C.C. Ling, S.A.
Leibel, Z. Fuks, and G.J. Kutcher, Deep inspiration
breath-hold technique for lung tumors: the
potential value of target immobilization and
reduced lung density in dose escalation. Int J
Radiat Oncol Biol Phys, 1999. 45(3): p. 603-11.
2.
3.
4.
5.
6.
7.
8.
9.
10.
PHS 398 (Rev. 05/01)
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
Page 50__
Jiang, Steve Bin
Barnes, E.A., B.R. Murray, D.M. Robinson, L.J.
Underwood, J. Hanson, and W.H. Roa, Dosimetric
evaluation of lung tumor immobilization using
breath hold at deep inspiration. Int J Radiat Oncol
Biol Phys, 2001. 50(4): p. 1091-8.
Stevens, C.W., R.F. Munden, K.M. Forster, J.F.
Kelly, Z. Liao, G. Starkschall, S. Tucker, and R.
Komaki, Respiratory-driven lung tumor motion is
independent of tumor size, tumor location, and
pulmonary function. Int J Radiat Oncol Biol Phys,
2001. 51(1): p. 62-8.
Davies, S.C., A.L. Hill, R.B. Holmes, M. Halliwell,
and P.C. Jacson, Ultrasound quantitation of
respiratory organ motion in the upper abdomen.
British Journal of Radiology, 1994. 67: p. 10961102.
Bryan, P.J., S. Custar, J.R. Haaga, and V.
Balsara, Respiratory movement of the pancreas:
an ultrasonic study. J Ultrasound Med, 1984. 3(7):
p. 317-20.
Kubo, H.D. and B.C. Hill, Respiration gated
radiotherapy treatment: a technical study. Phys
Med Biol, 1996. 41(1): p. 83-91.
Malone, S., J.M. Crook, W.S. Kendal, and J.
Szanto, Respiratory-induced prostate motion:
quantification and characterization. Int J Radiat
Oncol Biol Phys, 2000. 48(1): p. 105-9.
Murphy, M.J., J.R. Adler, Jr., M. Bodduluri, J.
Dooley, K. Forster, J. Hai, Q. Le, G. Luxton, D.
Martin, and J. Poen, Image-guided radiosurgery
for the spine and pancreas. Comput Aided Surg,
2000. 5(4): p. 278-88.
Chen, Q.S., M.S. Weinhous, F.C. Deibel, J.P.
Ciezki, and R.M. Macklis, Fluoroscopic study of
tumor motion due to breathing: facilitating precise
radiation therapy for lung cancer patients. Med
Phys, 2001. 28(9): p. 1850-6.
Seppenwoolde, Y., H. Shirato, K. Kitamura, S.
Shimizu, M. van Herk, J.V. Lebesque, and K.
Miyasaka, Precise and real-time measurement of
3D tumor motion in lung due to breathing and
heartbeat, measured during radiotherapy. Int J
Radiat Oncol Biol Phys, 2002. 53(4): p. 822-34.
Weiss, P.H., J.M. Baker, and E.J. Potchen,
Assessment of hepatic respiratory excursion. J
Nucl Med, 1972. 13(10): p. 758-9.
Harauz, G. and M.J. Bronskill, Comparison of the
liver's respiratory motion in the supine and upright
positions: concise communication. J Nucl Med,
1979. 20(7): p. 733-5.
Giraud, P., Y. De Rycke, B. Dubray, S. Helfre, D.
Voican, L. Guo, J.C. Rosenwald, K. Keraudy, M.
Housset, E. Touboul, and J.M. Cosset, Conformal
radiotherapy (CRT) planning for lung cancer:
analysis of intrathoracic organ motion during
extreme phases of breathing. Int J Radiat Oncol
Biol Phys, 2001. 51(4): p. 1081-92.
Minohara, S., T. Kanai, M. Endo, K. Noda, and M.
Kanazawa, Respiratory gated irradiation system
Principal Investigator/Program Director (Last, first, middle):
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
for heavy-ion radiotherapy. Int J Radiat Oncol Biol
Phys, 2000. 47(4): p. 1097-103.
Ford, E.C., G.S. Mageras, E. Yorke, K.E.
Rosenzweig, R. Wagman, and C.C. Ling,
Evaluation of respiratory movement during gated
radiotherapy using film and electronic portal
imaging. Int J Radiat Oncol Biol Phys, 2002. 52(2):
p. 522-31.
Shimizu, S., H. Shirato, K. Kagei, T. Nishioka, X.
Bo, H. Dosaka-Akita, S. Hashimoto, H. Aoyama,
K. Tsuchiya, and K. Miyasaka, Impact of
respiratory movement on the computed
tomographic images of small lung tumors in threedimensional (3D) radiotherapy. Int J Radiat Oncol
Biol Phys, 2000. 46(5): p. 1127-33.
Korin, H.W., R.L. Ehman, S.J. Riederer, J.P.
Felmlee, and R.C. Grimm, Respiratory kinematics
of the upper abdominal organs: a quantitative
study. Magn Reson Med, 1992. 23(1): p. 172-8.
Wade, O.L., Movements of the thoracic cage and
diaphragm in respiration. J Physiol, 1954. 124(2):
p. 193-212.
Ekberg, L., O. Holmberg, L. Wittgren, G.
Bjelkengren, and T. Landberg, What margins
should be added to the clinical target volume in
radiotherapy treatment planning for lung cancer?
Radiother Oncol, 1998. 48(1): p. 71-7.
Shirato, H., S. Shimizu, T. Kunieda, K. Kitamura,
M. van Herk, K. Kagei, T. Nishioka, S. Hashimoto,
K. Fujita, H. Aoyama, K. Tsuchiya, K. Kudo, and
K. Miyasaka, Physical aspects of a real-time
tumor-tracking system for gated radiotherapy. Int J
Radiat Oncol Biol Phys, 2000. 48(4): p. 1187-95.
Shimizu, S., H. Shirato, S. Ogura, H. AkitaDosaka, K. Kitamura, T. Nishioka, K. Kagei, M.
Nishimura, and K. Miyasaka, Detection of lung
tumor movement in real-time tumor-tracking
radiotherapy. Int J Radiat Oncol Biol Phys, 2001.
51(2): p. 304-10.
Murphy, M.J., D. Martin, R. Whyte, J. Hai, C.
Ozhasoglu, and Q.T. Le, The effectiveness of
breath-holding to stabilize lung and pancreas
tumors during radiosurgery. Int J Radiat Oncol Biol
Phys, 2002. 53(2): p. 475-82.
Mah, D., J. Hanley, K.E. Rosenzweig, E. Yorke, L.
Braban, C.C. Ling, S.A. Leibel, and G. Mageras,
Technical aspects of the deep inspiration breathhold technique in the treatment of thoracic cancer
[In Process Citation]. Int J Radiat Oncol Biol Phys,
2000. 48(4): p. 1175-85.
Rosenzweig, K.E., J. Hanley, D. Mah, G. Mageras,
M. Hunt, S. Toner, C. Burman, C.C. Ling, B.
Mychalczak, Z. Fuks, and S.A. Leibel, The deep
inspiration breath-hold technique in the treatment
of inoperable non-small-cell lung cancer. Int J
Radiat Oncol Biol Phys, 2000. 48(1): p. 81-7.
Yorke, E.D., L. Wang, K.E. Rosenzweig, D. Mah,
J.B. Paoli, and C.S. Chui, Evaluation of deep
inspiration breath-hold lung treatment plans with
PHS 398 (Rev. 05/01)
35.
36.
37.
38.
39.
40.
41.
42.
43.
44.
45.
Page 51__
Jiang, Steve Bin
Monte Carlo dose calculation. Int J Radiat Oncol
Biol Phys, 2002. 53(4): p. 1058-70.
Wong, J.W., M.B. Sharpe, D.A. Jaffray, V.R. Kini,
J.M. Robertson, J.S. Stromberg, and A.A.
Martinez, The use of active breathing control
(ABC) to reduce margin for breathing motion. Int J
Radiat Oncol Biol Phys, 1999. 44(4): p. 911-9.
Stromberg, J.S., M.B. Sharpe, L.H. Kim, V.R. Kini,
D.A. Jaffray, A.A. Martinez, and J.W. Wong,
Active breathing control (ABC) for Hodgkin's
disease: reduction in normal tissue irradiation with
deep inspiration and implications for treatment. Int
J Radiat Oncol Biol Phys, 2000. 48(3): p. 797-806.
Remouchamps, V.M., N. Letts, F.A. Vicini, M.B.
Sharpe, L.L. Kestin, P.Y. Chen, A.A. Martinez, and
J.W. Wong, Initial clinical experience with
moderate deep-inspiration breath hold using an
active breathing control device in the treatment of
patients with left-sided breast cancer using
external beam radiation therapy. Int J Radiat
Oncol Biol Phys, 2003. 56(3): p. 704-15.
Remouchamps, V.M., N. Letts, D. Yan, F.A. Vicini,
M. Moreau, J.A. Zielinski, J. Liang, L.L. Kestin,
A.A. Martinez, and J.W. Wong, Three-dimensional
evaluation of intra- and interfraction immobilization
of lung and chest wall using active breathing
control: A reproducibility study with breast cancer
patients. Int J Radiat Oncol Biol Phys, 2003. 57(4):
p. 968-78.
Remouchamps, V.M., F.A. Vicini, M.B. Sharpe,
L.L. Kestin, A.A. Martinez, and J.W. Wong,
Significant reductions in heart and lung doses
using deep inspiration breath hold with active
breathing control and intensity-modulated radiation
therapy for patients treated with locoregional
breast irradiation. Int J Radiat Oncol Biol Phys,
2003. 55(2): p. 392-406.
Kubo, H.D., P.M. Len, S. Minohara, and H.
Mostafavi, Breathing-synchronized radiotherapy
program at the University of California Davis
Cancer Center. Med Phys, 2000. 27(2): p. 346-53.
Ohara, K., T. Okumura, M. Akisada, T. Inada, T.
Mori, H. Yokota, and M.J. Calaguas, Irradiation
synchronized with respiration gate. Int J Radiat
Oncol Biol Phys, 1989. 17(4): p. 853-7.
Ramsey, C.R., I.L. Cordrey, and A.L. Oliver, A
comparison of beam characteristics for gated and
nongated clinical x-ray beams. Med Phys, 1999.
26(10): p. 2086-91.
Ramsey, C.R., D. Scaperoth, D. Arwood, and A.L.
Oliver, Clinical efficacy of respiratory gated
conformal radiation therapy. Med Dosim, 1999.
24(2): p. 115-9.
Kubo, H.D. and L. Wang, Compatibility of Varian
2100C gated operations with enhanced dynamic
wedge and IMRT dose delivery. Med Phys, 2000.
27(8): p. 1732-8.
Vedam, S.S., P.J. Keall, V.R. Kini, and R. Mohan,
Determining parameters for respiration-gated
radiotherapy. Med Phys, 2001. 28(10): p. 2139-46.
Principal Investigator/Program Director (Last, first, middle):
46.
47.
48.
49.
50.
51.
52.
53.
54.
55.
56.
Keall, P.J., V.R. Kini, S.S. Vedam, and R. Mohan,
Potential radiotherapy improvements with
respiratory gating. Australas Phys Eng Sci Med,
2002. 25(1): p. 1-6.
Shirato, H., S. Shimizu, T. Shimizu, T. Nishioka,
and K. Miyasaka, Real-time tumour-tracking
radiotherapy. Lancet, 1999. 353(9161): p. 1331-2.
Shimizu, S., H. Shirato, K. Kitamura, N.
Shinohara, T. Harabayashi, T. Tsukamoto, T.
Koyanagi, and K. Miyasaka, Use of an implanted
marker and real-time tracking of the marker for the
positioning of prostate and bladder cancers. Int J
Radiat Oncol Biol Phys, 2000. 48(5): p. 1591-7.
Shirato, H., T. Harada, T. Harabayashi, K. Hida, H.
Endo, K. Kitamura, R. Onimaru, K. Yamazaki, N.
Kurauchi, T. Shimizu, N. Shinohara, M.
Matsushita, H. Dosaka-Akita, and K. Miyasaka,
Feasibility of insertion/implantation of 2.0-mmdiameter gold internal fiducial markers for precise
setup and real-time tumor tracking in radiotherapy.
Int J Radiat Oncol Biol Phys, 2003. 56(1): p. 2407.
Shirato, H., S. Shimizu, K. Kitamura, T. Nishioka,
K. Kagei, S. Hashimoto, H. Aoyama, T. Kunieda,
N. Shinohara, H. Dosaka-Akita, and K. Miyasaka,
Four-dimensional treatment planning and
fluoroscopic real-time tumor tracking radiotherapy
for moving tumor. Int J Radiat Oncol Biol Phys,
2000. 48(2): p. 435-42.
Harada, T., H. Shirato, S. Ogura, S. Oizumi, K.
Yamazaki, S. Shimizu, R. Onimaru, K. Miyasaka,
M. Nishimura, and H. Dosaka-Akita, Real-time
tumor-tracking radiation therapy for lung
carcinoma by the aid of insertion of a gold marker
using bronchofiberscopy. Cancer, 2002. 95(8): p.
1720-7.
Murphy, M.J., Tracking moving organs in real time.
Semin Radiat Oncol, 2004. 14(1): p. 91-100.
Adler, J.R., Jr., M.J. Murphy, S.D. Chang, and S.L.
Hancock, Image-guided robotic radiosurgery.
Neurosurgery, 1999. 44(6): p. 1299-306;
discussion 1306-7.
Schweikard, A., G. Glosser, M. Bodduluri, M.J.
Murphy, and J.R. Adler, Robotic motion
compensation for respiratory movement during
radiosurgery. Comput Aided Surg, 2000. 5(4): p.
263-77.
Ozhasoglu, C., M.J. Murphy, G. Glosser, M.
Bodduluri, A. Schweikard, K.M. Forster, D.P.
Martin, and J.R. Adler. Real-time tracking of the
tumor volume in precision radiotherapy and body
radiosurgery - A novel approach to compensate
for respiratory motion. in Proc. 14th Int. Conf. on
Computer Assisted Radiology and Surgery (CARS
2000). 2000. San Francisco, CA, USA.
Murphy, M.J., S.D. Chang, I.C. Gibbs, Q.T. Le, J.
Hai, D. Kim, D.P. Martin, and J.R. Adler, Jr.,
Patterns of patient movement during frameless
image-guided radiosurgery. Int J Radiat Oncol Biol
Phys, 2003. 55(5): p. 1400-8.
PHS 398 (Rev. 05/01)
57.
58.
59.
60.
61.
62.
63.
64.
65.
66.
67.
68.
69.
Page 52__
Jiang, Steve Bin
Murphy, M.J., Fiducial-based targeting accuracy
for external-beam radiotherapy. Med Phys, 2002.
29(3): p. 334-44.
Keall, P.J., V.R. Kini, S.S. Vedam, and R. Mohan,
Motion adaptive x-ray therapy: a feasibility study.
Phys Med Biol, 2001. 46(1): p. 1-10.
Neicu, T., H. Shirato, Y. Seppenwoolde, and S.B.
Jiang, Synchronized moving aperture radiation
therapy (SMART): average tumour trajectory for
lung patients. Phys Med Biol, 2003. 48(5): p. 58798.
Suh, Y., B. Yi, S. Ahn, J. Kim, S. Lee, S. Shin, and
E. Choi, Aperture maneuver with compelled breath
(AMC) for moving tumors: a feasibility study with a
moving phantom. Med Phys, 2004. 31(4): p. 7606.
Papiez, L., The leaf sweep algorithm for an
immobile and moving target as an optimal control
problem in radiotherapy delivery. Mathematical
and Computer Modelling, 2003. 37(7-8): p. 735745.
Rangaraj, D. and L. Papiez, Synchronized delivery
of DMLC intensity modulated radiation therapy for
stationary and moving targets. Med Phys, 2005.
32(6): p. 1802-17.
Papiez, L. and D. Rangaraj, DMLC leaf-pair
optimal control for mobile, deforming target. Med
Phys, 2005. 32(1): p. 275-85.
Keall, P.J., S. Joshi, S.S. Vedam, J.V. Siebers,
V.R. Kini, and R. Mohan, Four-dimensional
radiotherapy planning for DMLC-based respiratory
motion tracking. Med Phys, 2005. 32(4): p. 94251.
Wijesooriya, K., C. Bartee, J.V. Siebers, S.S.
Vedam, and P.J. Keall, Determination of maximum
leaf velocity and acceleration of a dynamic
multileaf collimator: implications for 4D
radiotherapy. Med Phys, 2005. 32(4): p. 932-41.
Webb, S., The effect on IMRT conformality of
elastic tissue movement and a practical
suggestion for movement compensation via the
modified dynamic multileaf collimator (dMLC)
technique. Phys Med Biol, 2005. 50(6): p. 116390.
Webb, S., Limitations of a simple technique for
movement compensation via movement-modified
fluence profiles. Phys. Med. Biol., 2005. 50(14): p.
N155-N161.
Okumara, T., H. Tsuji, and Y. Hayakawa.
Respiration-gated irradiation system for proton
radiotherapy. in Proceedings of the 11th
international conference on the use of computers
in radiation therapy. 1994. Manchester: North
Western Medical Physics Dept. Christie Hospital.
Tada, T., K. Minakuchi, T. Fujioka, M. Sakurai, M.
Koda, I. Kawase, T. Nakajima, M. Nishioka, T.
Tonai, and T. Kozuka, Lung cancer: intermittent
irradiation synchronized with respiratory motion-results of a pilot study. Radiology, 1998. 207(3): p.
779-83.
Principal Investigator/Program Director (Last, first, middle):
70.
71.
72.
73.
74.
75.
76.
77.
78.
79.
80.
Hara, R., J. Itami, and T. Kondo, Stereotactic
single high dose irradiation of lung tumors under
respiratory gating. Radiother Oncol, 2002. 63: p.
159-163.
Vedam, S.S., V.R. Kini, P.J. Keall, V.
Ramakrishnan, H. Mostafavi, and R. Mohan,
Quantifying the predictability of diaphragm motion
during respiration with a noninvasive external
marker. Med Phys, 2003. 30(4): p. 505-13.
Kini, V.R., S.S. Vedam, P.J. Keall, S. Patil, C.
Chen, and R. Mohan, Patient training in
respiratory-gated radiotherapy. Med Dosim, 2003.
28(1): p. 7-11.
Mageras, G.S., E. Yorke, K. Rosenzweig, L.
Braban, E. Keatley, E. Ford, S.A. Leibel, and C.C.
Ling, Fluoroscopic evaluation of diaphragmatic
motion reduction with a respiratory gated
radiotherapy system. J Appl Clin Med Phys, 2001.
2(4): p. 191-200.
Wagman, R., E. Yorke, E. Ford, P. Giraud, G.
Mageras, B. Minsky, and K. Rosenzweig,
Respiratory gating for liver tumors: use in dose
escalation. Int J Radiat Oncol Biol Phys, 2003.
55(3): p. 659-68.
Mageras, G.S. and E. Yorke, Deep inspiration
breath hold and respiratory gating strategies for
reducing organ motion in radiation treatment.
Semin Radiat Oncol, 2004. 14(1): p. 65-75.
Jiang, S.B., R.I. Berbeco, J. Wolfgang, G.C.
Sharp, K. Doppke, T. Neicu, Y. Chen, P. Busse,
and G.T.Y. Chen, Image-Guided RespirationGated Treatment. Medical Physics, 2005.
Onimaru, R., H. Shirato, H. Aoyama, K. Kitakura,
T. Seki, K. Hida, K. Fujita, K. Kagei, T. Nishioka,
T. Kunieda, Y. Iwasaki, and K. Miyasaka,
Calculation of rotational setup error using the realtime tracking radiation therapy (RTRT) system and
its application to the treatment of spinal
schwannoma. Int J Radiat Oncol Biol Phys, 2002.
54(3): p. 939-47.
Kitamura, K., H. Shirato, S. Shimizu, N.
Shinohara, T. Harabayashi, T. Shimizu, Y.
Kodama, H. Endo, R. Onimaru, S. Nishioka, H.
Aoyama, K. Tsuchiya, and K. Miyasaka,
Registration accuracy and possible migration of
internal fiducial gold marker implanted in prostate
and liver treated with real-time tumor-tracking
radiation therapy (RTRT). Radiother Oncol, 2002.
62(3): p. 275-81.
Kitamura, K., H. Shirato, Y. Seppenwoolde, R.
Onimaru, M. Oda, K. Fujita, S. Shimizu, N.
Shinohara, T. Harabayashi, and K. Miyasaka,
Three-dimensional intrafractional movement of
prostate measured during real-time tumor-tracking
radiotherapy in supine and prone treatment
positions. Int J Radiat Oncol Biol Phys, 2002.
53(5): p. 1117-23.
Kitamura, K., H. Shirato, N. Shinohara, T.
Harabayashi, R. Onimaru, K. Fujita, S. Shimizu, K.
Nonomura, T. Koyanagi, and K. Miyasaka,
PHS 398 (Rev. 05/01)
81.
82.
83.
84.
85.
86.
87.
88.
Page 53__
Jiang, Steve Bin
Reduction in acute morbidity using
hypofractionated intensity-modulated radiation
therapy assisted with a fluoroscopic real-time
tumor-tracking system for prostate cancer:
preliminary results of a phase I/II study. Cancer J,
2003. 9(4): p. 268-76.
Shirato, H., Y. Seppenwoolde, K. Kitamura, R.
Onimura, and S. Shimizu, Intrafractional tumor
motion: Lung and liver. Semin Radiat Oncol, 2004.
14(1): p. 10-8.
Yamamoto, R., A. Yonesaka, S. Nishioka, H.
Watari, T. Hashimoto, D. Uchida, H. Taguchi, T.
Nishioka, K. Miyasaka, N. Sakuragi, and H.
Shirato, High dose three-dimensional conformal
boost (3DCB) using an orthogonal diagnostic Xray set-up for patients with gynecological
malignancy: a new application of real-time tumortracking system. Radiother Oncol, 2004. 73(2): p.
219-22.
Shirato, H., M. Oita, K. Fujita, S. Shimizu, R.
Onimaru, S. Uegaki, Y. Watanabe, N. Kato, and
K. Miyasaka, Three-dimensional conformal setup
(3D-CSU) of patients using the coordinate system
provided by three internal fiducial markers and two
orthogonal diagnostic X-ray systems in the
treatment room. Int J Radiat Oncol Biol Phys,
2004. 60(2): p. 607-12.
Ahn, Y.C., S. Shimizu, H. Shirato, T. Hashimoto,
Y. Osaka, X.Q. Zhang, T. Abe, M. Hosokawa, and
K. Miyasaka, Application of real-time tumortracking and gated radiotherapy system for
unresectable pancreatic cancer. Yonsei Med J,
2004. 45(4): p. 584-90.
Shirato, H., M. Oita, K. Fujita, Y. Watanabe, and
K. Miyasaka, Feasibility of synchronization of realtime tumor-tracking radiotherapy and intensitymodulated radiotherapy from viewpoint of
excessive dose from fluoroscopy. Int J Radiat
Oncol Biol Phys, 2004. 60(1): p. 335-41.
Onimaru, R., H. Shirato, M. Fujino, K. Suzuki, K.
Yamazaki, M. Nishimura, H. Dosaka-Akita, and K.
Miyasaka, The effect of tumor location and
respiratory function on tumor movement estimated
by real-time tracking radiotherapy (RTRT) system.
Int J Radiat Oncol Biol Phys, 2005. 63(1): p. 1649.
Imura, M., K. Yamazaki, H. Shirato, R. Onimaru,
M. Fujino, S. Shimizu, T. Harada, S. Ogura, H.
Dosaka-Akita, K. Miyasaka, and M. Nishimura,
Insertion and fixation of fiducial markers for setup
and tracking of lung tumors in radiotherapy. Int J
Radiat Oncol Biol Phys, 2005.
Hashimoto, T., H. Shirato, M. Kato, K. Yamazaki,
N. Kurauchi, T. Morikawa, S. Shimizu, Y.C. Ahn,
Y. Akine, and K. Miyasaka, Real-time monitoring
of a digestive tract marker to reduce adverse
effects of moving organs at risk (OAR) in
radiotherapy for thoracic and abdominal tumors.
Int J Radiat Oncol Biol Phys, 2005. 61(5): p. 155964.
Principal Investigator/Program Director (Last, first, middle):
89.
90.
91.
92.
93.
94.
95.
96.
97.
98.
99.
100.
Lagerwaard, F.J., S. Senan, J.P. van Meerbeeck,
and W.J. Graveland, Has 3-D conformal
radiotherapy (3D CRT) improved the local tumour
control for stage I non-small cell lung cancer?
Radiother Oncol, 2002. 63(2): p. 151-7.
Onimaru, R., H. Shirato, S. Shimizu, K. Kitamura,
B. Xu, S. Fukumoto, T.C. Chang, K. Fujita, M.
Oita, K. Miyasaka, M. Nishimura, and H. DosakaAkita, Tolerance of organs at risk in small-volume,
hypofractionated, image-guided radiotherapy for
primary and metastatic lung cancers. Int J Radiat
Oncol Biol Phys, 2003. 56(1): p. 126-35.
Lawrence, T.S., R.K. Ten Haken, M.L. Kessler,
J.M. Robertson, J.T. Lyman, M.L. Lavigne, M.B.
Brown, D.J. DuRoss, J.C. Andrews, and W.D.
Ensminger, The use of 3-D dose volume analysis
to predict radiation hepatitis. Int J Radiat Oncol
Biol Phys, 1992. 23(4): p. 781-8.
Jiang, S.B., R.I. Berbeco, G.T.Y. Chen, A. Jeung,
H. Mostafavi, and G.C. Sharp, A Tumor Tracking
System for Image Guided Radiotherapy. NIH/NCI
R21 grant, 2005-2007.
BERBECO, r., h. MOSTAFAVI, g. SHARP, and s.
JIANG. Tumor Tracking in the Absence of
Radiopaque Markers. in The 14th International
Conference on the Use of Computers in Radiation
Therapy. 2004. Seoul, Korea.
Balter, J.M., J.N. Wright, L.J. Newell, B. Friemel,
S. Dimmer, Y. Cheng, J. Wong, E. Vertatschitsch,
and T.P. Mate, Accuracy of a wireless localization
system for radiotherapy. Int J Radiat Oncol Biol
Phys, 2005. 61(3): p. 933-7.
Sharp, G.C., S.B. Jiang, S. Shimizu, and H.
Shirato, Prediction of respiratory tumour motion for
real-time image-guided radiotherapy. Phys Med
Biol, 2004. 49(3): p. 425-40.
Murphy, M.J., J. Jalden, and M. Isaksson.
Adaptive filtering to predict lung tumor breathing
motion during image-guided radiation therapy. in
Proc. 16th Int. Conf. on Computer Assisted
Radiology (CARS 2002). 2002.
Vedam, S.S., P.J. Keall, A. Docef, D.A. Todor,
V.R. Kini, and R. Mohan, Predicting respiratory
motion for four-dimensional radiotherapy. Med
Phys, 2004. 31(8): p. 2274-83.
Berbeco, R.I., S.B. Jiang, G.C. Sharp, G.T. Chen,
H. Mostafavi, and H. Shirato, Integrated
radiotherapy imaging system (IRIS): design
considerations of tumour tracking with linac
gantry-mounted diagnostic x-ray systems with flatpanel detectors. Phys Med Biol, 2004. 49(2): p.
243-55.
Sharp, G.C., S. Kollipara, T. Madden, S.B. Jiang,
and S. Rosenthal, Anatomic feature-based
registration for patient setup in head and neck
cancer radiotherapy. Phys Med Biol, 2005. to be
published.
Berbeco, R.I., T. Neicu, E. Rietzel, G.T. Chen, and
S.B. Jiang, A technique for respiratory-gated
radiotherapy treatment verification with an EPID in
PHS 398 (Rev. 05/01)
101.
102.
103.
104.
105.
106.
107.
108.
109.
110.
111.
112.
113.
114.
Page 54__
Jiang, Steve Bin
cine mode. Phys Med Biol, 2005. 50(16): p. 366979.
Pan, T., T.Y. Lee, E. Rietzel, and G.T. Chen, 4DCT imaging of a volume influenced by respiratory
motion on multi-slice CT. Med Phys, 2004. 31(2):
p. 333-40.
Rietzel, E., S.J. Rosenthal, D.P. Gierga, C.G.
Willet, and G.T. Chen, Moving targets: detection
and tracking of internal organ motion for treatment
planning and patient set-up. Radiother Oncol,
2004. 73 Suppl 2: p. S68-72.
Rietzel, E., G.T. Chen, N.C. Choi, and C.G. Willet,
Four-dimensional image-based treatment
planning: Target volume segmentation and dose
calculation in the presence of respiratory motion.
Int J Radiat Oncol Biol Phys, 2005. 61(5): p. 153550.
Rietzel, E., T. Pan, and G.T. Chen, Fourdimensional computed tomography: image
formation and clinical protocol. Med Phys, 2005.
32(4): p. 874-89.
Neicu, T., R.I. Berbeco, J. Wolfgang, and S.B.
Jiang, Synchronized Moving Aperture Radiation
Therapy (SMART): Investigation of the breathing
pattern reproducibility under respiratory coaching.
Phys Med Biol, 2005. submitted.
Trofimov, A., E. Rietzel, H.M. LU, B. Martin, S.B.
Jiang, C.T.Y. Chen, and T. Bortfeld, Temporospatial IMRT optimization: Cocepts and first
results. Phys Med Biol, 2004. submitted.
Gierga, D.P., G.T. Chen, J.H. Kung, M. Betke, J.
Lombardi, and C.G. Willett, Quantification of
respiration-induced abdominal tumor motion and
its impact on IMRT dose distributions. Int J Radiat
Oncol Biol Phys, 2004. 58(5): p. 1584-95.
Betke, M., J. Ruel, G.C. Sharp, S.B. Jiang, D.P.
Gierga, and C.T.Y. Chen, Tracking and prediction
of tumor movment in the abdomen. International
Journal of Radiation Oncology, Biology, and
Physics, 2005(submitted).
Reid, D.B., An algorithm for tracking multiple
targets. IEEE Transactions on Automatic Control,
1979. AC-24(6)(6): p. 843-854.
Bar-Shalom, Y. and T.E. Fortmann, Tracking and
Data Association. 1988: Academic Press.
Sharp, G.C., S.B. Jiang, S. Shimizu, and H.
Shirato, Tracking errors in a prototype real-time
tumour tracking system. Phys Med Biol, 2004.
49(23): p. 5347-56.
Berbeco, R.I., H. Mostafavi, G.C. Sharp, and S.B.
Jiang, Towards fluoroscopic respiratory gating for
lung tumours without radiopaque markers. Phys
Med Biol, 2005. 50(19): p. 4481-90.
Berbeco, R.I., S. Nishioka, H. Shirato, G.T. Chen,
and S.B. Jiang, Residual motion of lung tumours in
gated radiotherapy with external respiratory
surrogates. Phys Med Biol, 2005. 50(16): p. 365567.
Lujan, A.E., E.W. Larsen, J.M. Balter, and R.K.
Ten Haken, A method for incorporating organ
Principal Investigator/Program Director (Last, first, middle):
115.
116.
117.
118.
119.
120.
121.
122.
123.
124.
125.
126.
127.
128.
129.
130.
131.
motion due to breathing into 3D dose calculations.
Med Phys, 1999. 26(5): p. 715-20.
Wu, H., G.C. Sharp, B. Salzberg, D. Kaeli, H.
Shirato, and S.B. Jiang, A finite state model for
respiratory motion analysis in image guided
radiation therapy. Phys Med Biol, 2004. 49(23): p.
5357-72.
Jiang, S.B., Compensating for Tumor Motion
Using A Computer-Controlled Multi-leaf Collimator
in Radiation Therapy. Whitaker Foundation
Research Grant, 2001-2004.
Sharp, G.C., S. Kollipara, T. Madden, S.B. Jiang,
and S.J. Rosenthal, Anatomic feature-based
registration for patient set-up in head and neck
cancer radiotherapy. Phys Med Biol, 2005. 50(19):
p. 4667-79.
Freedman, D., R.J. Radke, T. Zhang, Y. Jeong,
D.M. Lovelock, and G.T. Chen, Model-based
segmentation of medical imagery by matching
distributions. IEEE Trans Med Imaging, 2005.
24(3): p. 281-92.
Breiman, L., Bagging Predictors. Machine
Learning, 1996. 24(2): p. 123-140.
Vapnik, V., Statistical Learning Theory. 1998, New
York: Wiley.
Quinlan, J.R., Induction of decision trees. Machine
Learning, 1986. 1(1): p. 81-106.
Duda, R.O., P.E. Hart, and D.G. Stork, Pattern
Classification (Second Edition). 2001, NY: Wiley &
Sons.
Bishop, C.M., Neural Networks for Pattern
Recognition. 1995: Oxford University Press.
Murase, H. and S. Nayar, Visual Learning and
recognition of 3-D objects from appearance.
International Journal of Computer Vision, 1995.
14: p. 5-24.
Black, M.J. and A.D. Jepson, EigenTracking:
Robust Matching and Tracking of Articulated
Objects Using a View-Based Representation.
International Journal of Computer Vision, 1998.
26(1): p. 63-84.
Sista, S., C.A. A. Bouman, and J.P. P. Allebach,
Fast image search using a multiscale stochastic
model, in IEEE International Conference on Image
Processing. 1995: Washington, DC. p. 22-25.
Turk, M. and A. Pentland, Face recognition using
eigenfaces, in Proceedings in Computer Vision
and Pattern Recognition. 1991: Maui. p. 586-591.
Jolliffe, I.T., Principal Component Analysis. 2002:
Springer.
McLachlan, G.J. and K.E. Basford, Mixture
Models, Inference and Applications to Clustering.
1988, New York: Marcel Dekker.
Dy, J.G. and C.E. Brodley, Feature selection for
unsupervised learning. Journal of Machine
Learning Research, 2004. 5: p. 845-889.
Tipping, M.E. and C.M. Bishop, Mixtures of
probabilistic principal component analysers.
Neural Computation, 1999. 11(2): p. 443-482.
PHS 398 (Rev. 05/01)
132.
133.
134.
135.
136.
137.
138.
139.
140.
141.
142.
143.
144.
Page 55__
Jiang, Steve Bin
Su, T. and J.G. Dy, Automated Hierarchical
Mixtures of Probabilistic Principal Component
Analyzers, in Proceedings of the Twenty-First
International Conference on Machine Learning.
2004: Banff, Alberta, Canada. p. 775-782.
Schwarz, G., Estimating the Dimension of a
Model. The Annals of Statistics, 1978. 6(2): p.
461-464.
Rabiner, L.R., A tutorial on Hidden Markov Models
and selected applications in speech recognition.
Proceedings of the IEEE, 1989. 77(2).
Dempster, A.P., N.M. Laird, and D.B. Rubin,
Maximum likelihood from incomplete data via the
EM algorithm. Journal Royal Statistical Society,
Series B, 1977. 39(1): p. 1-38.
Cohn, D., L. Atlas, and R. Ladner, Improving
generalization with active learning. Machine
Learning (Historical Archive), 1994. 15(2): p. 201221.
Cohn, D.A., Z. Ghahramani, and M.I. Jordan,
Active Learning with Statistical Models. JOURNAL
OF ARTIFICIAL INTELLIGENCE RESEARCH,
1996. 4: p. 129.
Zhu, X., Semi-Supervised Learning Literature
Survey, in Computer Sciences TR 1530. 2005,
University of Wisconsin-Madison.
Zhu, X., J. Lafferty, and Z. Ghahramani,
Combining Active Learning and Semi-Supervised
Learning Using Gaussian Fields and Harmonic
Functions, in International Conference on Machine
Learning 2003 workshop on The Continuum from
Labeled to Unlabeled Data in Machine Learning
and Data Mining. 2003.
Tao, Y., C. Faloutsos, D. Papadias, and B. Liu,
Prediction and indexing of moving objects with
unknown motion patterns, in Proceedings of the
2004 ACM SIGMOD international conference on
Management of data. 2004, ACM Press: Paris,
France. p. 611-622.
Siddon, R.L., Fast calculation of the exact
radiological path for a three-dimensional CT array.
Medical Physics, 1985. 12: p. 252-255.
Killoran, J.H., E.H. Baldini, C.J. Beard, and L.
Chin, A technique for optimization of digitally
reconstructed radiographs of the chest in virtual
simulation. Int J Radiat Oncol Biol Phys, 2001.
49(1): p. 231-9.
Siewiorek, D.P. and R.P. Swarz, Reliable
Computer Systems Design and Evaluation. 3rd
ed. 1998, Natick, MA: AK Peters.
Pressman, R.S., Software Engineering, A
Practitioner's Approach. 2nd ed. 2004: McGrawHill.