Download Methods for improving limited field-of

Methods for improving limited field-of-view radiotherapy reconstructions
using imperfect a priori images
Kenneth J. Ruchalaa)
TomoTherapy, Inc., Madison, Wisconsin 53717-1954
Gustavo H. Olivera
TomoTherapy, Inc., Madison, Wisconsin 53717-1954
and Department of Medical Physics, University of Wisconsin–Madison, Madison, Wisconsin 53706-1532
Jeffrey M. Kapatoes and Paul J. Reckwerdt
TomoTherapy, Inc., Madison, Wisconsin 53717-1954
Thomas R. Mackie
TomoTherapy, Inc., Madison, Wisconsin 53717-1954
and Department of Medical Physics and Department of Human Oncology,
University of Wisconsin–Madison, Madison, Wisconsin 53706-1532
!Received 10 January 2002; accepted for publication 19 August 2002; published 25 October 2002"
There are many benefits to having an online CT imaging system for radiotherapy, as it helps
identify changes in the patient’s position and anatomy between the time of planning and treatment.
However, many current online CT systems suffer from a limited field-of-view !LFOV" in that
collected data do not encompass the patient’s complete cross section. Reconstruction of these data
sets can quantitatively distort the image values and introduce artifacts. This work explores the use
of planning CT data as a priori information for improving these reconstructions. Methods are
presented to incorporate this data by aligning the LFOV with the planning images and then merging
the data sets in sinogram space. One alignment option is explicit fusion, producing fusion-aligned
reprojection !FAR" images. For cases where explicit fusion is not viable, FAR can be implemented
using the implicit fusion of normal setup error, referred to as normal-error-aligned reprojection
!NEAR". These methods are evaluated for multiday patient images showing both internal and
skin-surface anatomical variation. The iterative use of NEAR and FAR is also investigated, as are
applications of NEAR and FAR to dose calculations and the compensation of LFOV online MVCT
images with kVCT planning images. Results indicate that NEAR and FAR can utilize planning CT
data as imperfect a priori information to reduce artifacts and quantitatively improve images. These
benefits can also increase the accuracy of dose calculations and be used for augmenting CT images
!e.g., MVCT" acquired at different energies than the planning CT. © 2002 American Association
of Physicists in Medicine. #DOI: 10.1118/1.1513163$
Key words: CT, adaptive radiotherapy, radiotherapy verification, image reconstruction, therapy
imaging
I. INTRODUCTION
1,2
The process of adaptive radiotherapy !ART" is being investigated at many sites for the improvement of radiotherapy.
A particularly useful tool for the development of ART is
online CT imaging, and together these techniques are commonly referred to as image-guided adaptive radiotherapy. It
is particularly useful to have CT capability integrated into
the radiotherapy delivery system since this minimizes patient
motion between imaging and treatment and potentially enables image acquisition during treatment.
There are several approaches to online CT presently under
development. One option is to use the megavoltage linear
accelerator !linac" as the CT radiation source in conjunction
with a detector system to measure the transmitted
fluences.3–22 Online kVCT has been explored with an additional kV x-ray tube,23–25 using CT-on-rails,26 and with a
dual-energy-capable linac head. 27
Their numerous differences aside, one characteristic com2590
Med. Phys. 29 „11…, November 2002
mon amongst many of these systems is a limited field-ofview !LFOV". This is a lateral truncation of collected data
and reconstruction of these incomplete data sets will not
typically show the patient’s full extent. The images produced
from LFOV data sets can also contain artifacts, commonly
known as ‘‘cupping’’ or ‘‘bowl’’ artifacts,28 –32 that may impair the utility of the images for both quantitative and qualitative purposes. The severity of the artifacts depends upon
the hardware used and the patient specifics like patient size,
anatomy inside and outside the FOV, and position within the
scanner. Furthermore, the limitations imposed by these artifacts ultimately depend upon not only the artifact severity,
but also on the location and characteristics of the structures
of interest, the ability of the observer to ‘‘read-through’’ the
distortion, and the desired imaging task. Some of the possible imaging tasks include subjective visual inspection, patient repositioning,26,33 delivery verification,34 dose
reconstruction,35,36 treatment adjustment #Kapatoes, 2001
0094-2405Õ2002Õ29„11…Õ2590Õ16Õ$19.00
© 2002 Am. Assoc. Phys. Med.
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#543,37 contouring,9,38 – 40 deformable fusion,40– 43 and deformable dose mapping.40
There are two prevalent reasons for FOV limits in the
context of radiotherapy imaging. One limiting problem is the
field size of any multileaf collimators !MLCs" attached to the
linac. For example, with all leaves open, the NOMOS
MIMiC has a field size of approximately 20 cm at isocenter.
The MLC of the University of Wisconsin TomoTherapy Prototype has a FOV of approximately 40 cm at the axis, but
this is still not sufficiently large to collect complete FOV
data for all sites of all patients. The other common cause of
LFOV images is the detector size. Some experimental online
CT systems use detectors around 20!20 cm2 , providing
around 15 cm of image FOV at isocenter. Systems utilizing
EPIDs and FPIs around 40!40 cm2 provide FOVs of approximately 30 cm at isocenter. Use of a wider detector can
improve the FOV, as can use of a laterally offset detector
with overscanning over a larger angular range.6,44,45
It would be impractical to require that an online imaging
system be able to completely encompass every possible slice
of every patient. This is the motivation behind the development of LFOV data compensation techniques. One set of
these techniques is geared toward collection of the missing
data. The overscanning methods mentioned previously exemplify this. Analogously, on systems in which the MLC
limits the FOV, overscanning could be performed with an
offset MLC, or multiple scans could be performed for offset
patient positions.
Another set of techniques attempts to estimate the missing
data based upon a priori knowledge or assumptions about
the images or data sets, such as known physical characteristics or redundancies in the measured data. These techniques
include: symmetric mirroring,46 use of redundant rays,47
polynomial extrapolation !potentially incorporating known
boundaries",48,49 use of known internal geometry,50,51 and iterative techniques in image, sinogram, or Fourier space.52–58
Related techniques also exist for region-of-interest tomography !ROIT" in which the scan FOV is intentionally limited
to reduce patient dose, even though complete FOV data sets
are potentially available.59– 63 A variation of this theme has
been applied to using selectively localized transmission data
from radiotherapy treatments for image reconstruction.
These data can be reconstructed in conjunction with other
imaging data or a slightly modified treatment delivery,19,64,65
or by using other LFOV compensation techniques.9 The
same principle can be applied to using treatment data to
compensate for a reduced number of imaging angles as a
means of reducing imaging dose.66
The goal of this work is to investigate new opportunities
for the reconstruction of truncated data sets, whether systematically or intentionally limited. In previous studies, planning
CT data have been evaluated for patient alignment67–71 and
dose verification.72 Here it is evaluated as a potential source
of a priori information for image reconstruction. Yet, if the
planning CT images were always a perfect source of a priori
information in that the patient’s anatomy never changed, then
online CT would be extraneous. Therefore, this work focuses
on methods for utilizing the planning CT data for LFOV
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FIG. 1. Flowchart of the fusion-aligned reprojection !FAR" process.
reconstruction with particular regard for a patient’s daily anatomical variations. Image improvement is evaluated for a
range of indicative FOV sizes to demonstrate the opportunities for improvement when LFOV compensation is desired.
II. MATERIALS AND METHODS
A. Fusion-aligned reprojection
and normal-error-aligned reprojection
field-of-view compensation techniques
In order to use the planning image data in conjunction
with the online CT data it is helpful to know how the data
sets relate. This can often be accomplished by automatic fusion, which will be discussed further in the following. Using
this alignment, a method of image improvement called
fusion-aligned reprojection !FAR" is investigated.
The inputs to the FAR process !Fig. 1" are typically a
planning CT image and a LFOV sinogram. This sinogram is
initially reconstructed into a LFOV image set and then registered with the complete FOV planning CT. Based upon this
result, the planning CT is aligned to the LFOV online image
and then reprojected back into a sinogram. The sinogram
data are used to estimate the data missing from the LFOV
scan, yielding an augmented LFOV sinogram. This augmented sinogram is reconstructed into the FAR image.
This completion process can be seen in sinogram space in
Fig. 2. A truncated sinogram is shown in Fig. 2!a" for a FOV
of 19.9 cm. The missing data are estimated from the aligned
planning sinogram shown in Fig. 2!b". The resulting FAR
sinogram is shown in Fig. 2!c". Figure 2!d" shows the difference between the complete FOV daily sinogram !not shown"
and the aligned planning sinogram. The difference between
the complete FOV daily sinogram and the FAR sinogram is
shown in Fig. 2!e". There is little difference in the interior
regions of this sinogram because the same data are being
collected in both cases !aside from possible differences in
scatter not included in this study". The peripheral regions of
Fig. 2!e" necessarily resemble Fig. 2!d" because the FAR
sinogram estimates those regions from the aligned planning
sinogram. Figure 2!f" shows another type of FAR sinogram
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FIG. 2. The components of FAR in sinogram space, with lateral position
representing detector position, vertical position representing gantry angle,
and the sinogram values representing integral attenuation. !a" A 20 cm limited field-of-view !LFOV" online sinogram, !b" the planning CT sinogram,
!c" the FAR sinogram obtained by merging !a" and !b". Differences are
shown between the complete FOV online sinogram and !d" the planning
sinogram, !e" the FAR sinogram, and !f" a FAR sinogram created with an
inferior registration.
for an inferior registration that will be discussed further in
the following. The interior differences are again minimal because that data still come from the online sinogram, but the
peripheral data have larger differences due to the inferior
planning image alignment.
Figure 2!f" underscores the principle that FAR ultimately
depends upon an alignment between the planning and LFOV
online images. Previous study of this topic has identified
several automatic fusion algorithms that are generally successful under these circumstances, especially for FOVs that
are approximately half the patient’s size or larger.73 The same
study found that as the FOV decreased, the success rate of
registrations with larger displacements was more impacted
than for small displacements, so even using a very coarse
manual fusion could improve automatic fusion results. Since
FAR is not specific to the registration technique, other automatic, manual, or hybrid methods could also be used if desired, and FAR images could be created for multiple registrations if the fusion was inconclusive. Previous studies have
demonstrated circumstances under which iterative reconstruction techniques could reduce image noise,3,18,74 and
these might be helpful in cases where noise hindered the
fusion.
Any useful LFOV registration for FAR !aside from trialand-error", whether manual or automatic, implies that there
is some information in those images in spite of any quantiMedical Physics, Vol. 29, No. 11, November 2002
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tative and qualitative degradation. In these cases, the goal of
FAR is to quantitatively and qualitatively improve upon the
information present by incorporating additional a priori information. Yet, as FOVs become more severely reduced, images may lose their utility for automatic fusion, manual fusion, and visual inspection. There are also a number of other
reasons why automatic fusion may not provide the desired
result, such as finding a local minimum. Another problem
with fusion is that in the presence of anatomical changes
there may not be an unambiguous correct alignment, as some
structures may align well at the expense of others.
It is important to investigate the application of FAR
amidst these complications. One possibility is to substitute
an implicit fusion for the explicit one. For example, the patient is often positioned within a few millimeters and a few
degrees of the intended position using common radiotherapy
patient setup protocols. This normal setup error is regarded
as an implicit fusion and used for a type of FAR referred to
as normal-error-aligned reprojection !NEAR".
NEAR may be practical in cases where explicit fusion is
unavailable or inaccurate. It is also useful, conceptually, for
evaluating the robustness of FAR with regard to imperfect
fusion results !e.g., cases that have misregistrations, or lack
an unambiguous correct answer due to anatomical changes".
A third benefit of NEAR is that it may enable a two #or
more$ iteration version of FAR, referred to as NEAR2FAR.
After creating a NEAR image, the quantitatively improved
voxel values in the FOV might then enable an explicit fusion
with the planning image, and a FAR image could be generated. NEAR and NEAR2FAR may be particularly beneficial
when a LFOV causes severe quantitative and qualitative degradation of the images, whether because of a large patient, a
small detector or MLC, or because a ROIT strategy is being
pursued.
B. Data sets and image generation
As mentioned previously, a motivation of online imaging
is that patient anatomy can change from the time of the planning CT to a given fraction. Thus, planning CT data are best
evaluated as an imperfect source of a priori information due
to anatomical variations and setup errors. It was also desired
to have complete FOV images that could be truncated in
sinogram space to create LFOV images, so that the results
could be compared against a known standard. Appropriate
multiday data were provided by William Beaumont
Hospital.2 There were 17 daily prostate CT image sets, the
first of which was designated the planning CT and the others
were designated as daily online image volumes. Contours of
the prostate, bladder, and rectum were also provided for each
day.
These image volumes were acquired at the William Beaumont Hospital on separate CT scanner and did not incorporate precise radiotherapy setup protocols. In order to realign
these data sets to resemble normal setup errors for radiotherapy, 16 six-parameter offsets were randomly generated.
Each of the three translational offsets were selected from a
normal distribution with a zero-mean and standard deviation
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FIG. 3. The intentional misalignments of the planning image relative to the
16 online images, representing randomized normal setup error with standard
deviations of 2 mm in each translational direction and 2° around each rotational axis.
of 2 mm, and the three rotational errors were selected from a
zero-mean normal distribution with a standard deviation of
2°. The net translational and rotational displacements are
shown in Fig. 3. The planning CT image was then realigned
16 times, to create a planning CT image offset from each of
the online images by one of the randomized six-parameter
displacements.
The 16 offset planning images were then forward projected, or reprojected, into sinograms with dimensions of 851
gantry angles by 512 detector positions. The 16 online images were also reprojected into sinograms, and then truncated in sinogram space to FOV sizes of 38.6, 29.3, and 19.9
cm. The missing sinogram data from the truncated LFOV
images were completed using the corresponding offset planning sinograms. Phasing-in the planning CT sinogram data
over the peripheral 15 pixels of each online sinogram’s FOV
was heuristically found to reduce artifacts at the junction
between the data sets by reducing high-frequency discontinuities at the boundary. This process produced NEAR sinograms for each of the 16 patient-days at the three different
FOVs. All of the LFOV and NEAR sinograms were reconstructed using filtered back-projection into CT volumes. The
entire process currently requires around 1 min/slice using
nonoptimized PV-Wave !Visual Numerics, Houston, TX"
codes on a 1.5 GHz Pentium III, primarily due to the reprojection and reconstruction times. Optimized CT reconstruction can currently be performed in subsecond times, and
could reduce the time to approximately 1 s/slice.
The FAR images were created by using automatic fusion
to determine the alignment between each LFOV online image and the corresponding offset planning image. This fusion
was done separately for each patient-day and each LFOV
size. The particular automatic registration technique used
was cropped mutual information !CMI",73 which is a variaMedical Physics, Vol. 29, No. 11, November 2002
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tion on the well-known mutual information algorithm.42,75– 80
The primary difference between CMI and mutual information is that peripheral points are discarded from the calculation, to avoid image points that generally have the most severe artifacts. In this case, the voxels within 1.9 cm of the
FOV edge were disregarded. However, FAR is not specific to
a single fusion algorithm !e.g., CMI" and others could be
used as desired.
Based upon these explicit fusion results, each offset planning image was realigned. For example, the planning image
in randomized offset position 4 was automatically fused to
the three sizes of LFOV online images from day 4 and then
realigned to three different positions, based upon those results. Each of the 48 realigned planning images !16 patientdays times 3 FOVs" was then reprojected into a sinogram
and used to augment the respective LFOV sinogram. The
augmented LFOV sinograms were reconstructed into FAR
images.
The LFOV, NEAR, and FAR image volumes for each
patient-day and FOV were then analyzed and compared to
the complete FOV online images. Reconstructed images illustrate the qualitative improvements, while quantitative improvements are demonstrated with profile-plots, error histograms, and tabulated rms errors.
C. Extensions and applications of NEAR and FAR
Using the same basic methodology to create LFOV,
NEAR, and FAR images, three additional experiments were
performed: testing NEAR and FAR for dose calculations,
testing iterative application of NEAR and FAR for severely
limited FOVs, and testing FAR for a combination of kilovoltage and megavoltage CT images.
1. Dose calculations using FAR
Dose calculations are typically based upon CT images and
require reconstructed values that can be calibrated to electron
densities. The artifacts and quantitative distortions introduced by FOV truncation may degrade this calibration, while
the lack of peripheral information can impair the scatter and
attenuation calculations often performed when computing
dose.
A treatment was optimized for the planning CT image,
and the resulting dose distribution was calculated. The same
treatment plan was then delivered to an aligned online image, as well as LFOV, NEAR, and FAR images. All dose
calculations were performed with convolution/superposition
using the CT images converted to electron density. The regions of interest contoured at the William Beaumont Hospital were used to generate DVH curves for the different image
sets.
2. Iterative application of NEAR and FAR
The use of NEAR and FAR was tested for a FOV of only
10.5 cm. This could reflect either a very small detector or
MLC, or represent an intentionally limited field. NEAR images were created for a subset of four patient-days, and the
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FIG. 4. !a" Planning CT image slice with contours for the prostate !solid" and
rectum !dotted"; !b" same image slice of an online CT, showing daily !white"
and planning !black" contours; !c" another planning CT image slice showing
the prostate and bladder !dashed", !d" corresponding slice of an online CT.
results were used to test the creation of two-iteration
NEAR2FAR images. Images, error histograms, rms values,
and DVHs are presented.
3. Application of FAR to cross-energy CT images
Since many online imaging systems being investigated
use MVCT while planning CT images are almost universally
acquired on scanners operating at kilovoltage energies, additional studies were performed to test the feasibility of FAR
with cross-energy CT. In principle, FAR should enable the
improvement of LFOV MVCT images with kVCT planning
images to the extent that both represent the same physical
characteristic, namely electron density. However, this assumption is not perfect, especially for kVCT in the presence
of high-Z materials, so the goal was to investigate the opportunities for FAR under these circumstances.
A CT performance phantom was first imaged to gauge the
performance of the TomoTherapy Prototype MVCT. An
anesthetized dog was then imaged first on a Siemens Hi-Q
kilovoltage CT scanner and then using the TomoTherapy
MVCT. The method was otherwise the same as described
previously, with the one exception that since different CT
systems were involved, the values in each image were converted from Hounsfield values to electron density values
based on each scanner’s own calibration curve. The converted electron density images were reprojected and combined in sinogram space. The second cross-energy FAR test
was for a human lung patient. Planning kVCT images were
obtained on a General Electric PET-CT system, and online
images were acquired with the TomoTherapy MVCT.
III. RESULTS
A. Prostate images using NEAR and FAR
Figure 4 shows two representative images from the planning CT and the corresponding slices in two different online
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FIG. 5. LFOV, normal-error-aligned reprojection !NEAR", and FAR images
for field-of-view sizes of 38.6, 29.3, and 19.9 cm based upon the online
image in Fig. 4!b". The contours from the complete FOV online image are
overlaid for reference.
CT image sets. The contours for the planning images are
shown in black. The online images also show their own contours in white.
The first online image #Fig. 5!b"$ is shown for three
LFOVs in Fig. 5. The NEAR and FAR reconstructions from
these same sinograms are also shown. The contours from the
complete FOV image are overlaid for reference. As the FOV
decreases, the artifacts become more severe in the LFOV
images, while the NEAR and FAR images are less affected.
The extent to which any of these images are sufficient for a
given imaging task depends upon that task and possibly on a
subjective judgment by a viewer. Central structures generally
have less severe artifacts than peripheral structures, and patient size, anatomy, and position all affect artifacts, as does
the imaging hardware. Thus, these images are not intended
as a cataloging of the precise circumstances under which
LFOV artifacts impair a given observer’s ability to perform
any particular imaging task. Instead, these images are representative of how NEAR and FAR can utilize available information to qualitatively improve the reconstructions for a
range of FOV sizes.
In this particular case, there is little visual difference between the NEAR and FAR images. The similarity of NEAR
and FAR images can occur for several reasons, such as because the normal setup error is small, the explicit fusion did
not improve much upon the normal error, or because anatomical differences between the planning CT and the online
CT are a more significant factor than the alignment between
those images.
Sometimes the differences between NEAR and FAR are
more conspicuous, as can be seen in the similar layout of
results !Fig. 6" for the online image from Fig. 4!d". In this
case, the normal error included a larger rotation about the
transverse axis, so the peripheral data in the NEAR image
appear rotated relative to the complete FOV online image.
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FIG. 6. Limited field-of-view, normal-error-aligned reprojection, and fusionaligned reprojection images for field-of-view sizes of 38.6, 29.3, and 19.9
cm based upon the online image in Fig. 4!d". The contours from the complete FOV online image are overlaid for reference.
This dissonance between the LFOV data and the offset planning image data results in artifacts, and these are most conspicuous in the 19.9 cm NEAR image. The FAR images
improve upon the NEAR images in this case because the
explicit fusion better aligned the planning data with the
LFOV data. For both NEAR and FAR the junctioning
method described earlier tempers these artifacts, but some
remain due to anatomical differences between the online and
planning images. This is the imperfection of the a priori
knowledge and will be addressed more in the discussion.
Profiles through the image slice shown in Fig. 5 are plotted in Fig. 7. Each plot contains profiles through the center of
the LFOV, NEAR, FAR, and complete FOV images. For the
38.6 cm FOV, all four curves overlap almost indistinguishably aside from a narrow spike in apparent attenuation for
the LFOV image at the outer extent of the imaged FOV.
There are also differences between the online image and the
NEAR and FAR images beyond this FOV extent, representing the imperfection of the planning CT data for estimating
the missing peripheral data in the online image.
For the FOV of 29.3 cm, the NEAR and FAR curves are
again indistinguishable from the complete FOV online image
profile inside the FOV. The LFOV image is seen to differ
marginally at the center and more pronouncedly toward the
limits of the FOV. Outside of the FOV, the NEAR and FAR
images again differ from the complete online image, illustrating the differences between the planning and online images. This same pattern is amplified for the 19.9 cm FOV.
The cupping artifacts in the LFOV image are increased,
while the NEAR and FAR images remain indistinguishable
from the complete online image within the scan FOV. However, the differences between the NEAR and FAR images
and the complete online image increase outside of the FOV
due to the increased reliance on planning CT data in lieu of
actual scan data.
Medical Physics, Vol. 29, No. 11, November 2002
FIG. 7. Profiles through a limited field-of-view image #black dotted$, complete FOV online image #dark gray solid$, fusion-aligned reprojection image
#medium gray chain$, and normal-error-aligned reprojection image #light
gray dashes$.
Figures 5–7 present results for individual images. Additional quantitative analysis is shown in Fig. 8 in the form of
histograms tallied over all 16 patient-days. Image value differences were taken between each LFOV, NEAR, and FAR
image and the corresponding complete FOV online image.
These difference values were accumulated for each type of
reconstruction at each FOV for each ROI. As mentioned earlier, each ROI was defined by contours previously drawn at
the William Beaumont Hospital, and the online image’s ROIs
were applied to the corresponding LFOV, FAR, and NEAR
images. Histograms generated with all ROIs shifted laterally
by 2.8 mm were virtually identical to the nonshifted histograms, indicating a low sensitivity to the precise locations of
the ROIs and interobserver variability.
Figures 8!a" and 8!b" show that at 38.6 cm FOV there is
only a marginal difference between the FAR histograms and
LFOV histograms, approximately 5 Hounsfield units !HU" or
0.5%. The NEAR curves are indistinguishable from the FAR
curves in both cases. There is a slightly greater separation
between the curves at 29.3 cm FOV, seen in Figs. 8!c" and
8!d". The NEAR and FAR curves are still not differentiable,
but the LFOV curves have shifted right, representing image
values in the different ROIs increased by an average of 50–
100 HU !5%–10%" due to the cupping artifacts. For the 19.9
cm FOV #Figs. 8!e" and 8!f"$ this shift is approximately 250
HU, which is consistent with the profiles shown in Fig. 7.
The dotted NEAR curve is vaguely differentiable from the
FAR curve in Fig. 8!f" in that it is modestly shorter and
wider.
Additional quantitative analysis of these multiday images
is presented in Table I, which contains the mean and standard
deviation root-mean-squared !rms" values. The rms differ-
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FIG. 8. Histograms of the voxel differences between the
complete field-of-view online CT !CFOV" and the
LFOV and FAR images, tallied over 16 patient-days.
The dotted curve for NEAR is only distinguishable
from the FAR curves in !f". All horizontal axes have the
same scale and units.
ence was calculated between each complete FOV online image and the corresponding LFOV, NEAR, and FAR images at
each FOV. The rms was scored in each contoured ROI along
with the sets of all points in the FOV and all points. The
means and standard deviations were taken over the set of 16
daily images. The mean and standard deviation rms values
were also computed for the differences between the complete
FOV online images and the sets of aligned and misaligned
planning images.
One important point about Table I is that it provides another evaluation of the quantitative limitations of LFOV reconstructions. For the largest FOV, all three reconstruction
methods have very similar rms differences in the three contoured ROIs. The differences are larger for the sets of all
points and all FOV points because these sets include the rind
where the LFOV artifacts are most severe, whereas the contoured ROIs are further removed from the worst artifacts in
this series of images. As the FOV is reduced, the rms is only
increased by a few HU in the contoured ROIs for NEAR and
FAR, yet the LFOVs rms increase by about 40 HU at 29.3
cm and over 250 HU at 19.9 cm. The rms values for all of
the reconstruction methods are worse for the sets of all FOV
points than for the contoured ROIs, yet even at the 19.9 cm
FOV, the rms for FAR has only increased by about 10 HU,
and for NEAR by about 15 HU. In comparison, the rms of
the LFOV reconstructions increases from 109 to 383 to 949
as the FOV reduces from 38.6 to 29.3 to 19.9 cm.
Table I also demonstrates the quantitative discrepancies
between the online images and the planning images !both
aligned and normal-error-misaligned". This underscores the
benefits on online imaging over relying solely upon the planning CT, while also showing that even a LFOV online image
augmented with NEAR or FAR can produce images that are
quantitatively closer to the complete FOV online image than
the planning image alone. The benefits of NEAR and FAR
over the planning image were a reduction in rms error by
TABLE I. The mean and standard deviation root-mean-square !rms" values average over 16 patient-days for three different reconstruction methods of three
different image field-of-view !FOV" sizes. rms values were scored in rectal, bladder, and prostate regions, and in the sets of all FOV points and all points. The
rms values between the aligned and misaligned planning images vs the complete FOV online images are shown for reference.
Rectum points
Prostate points
FOV !cm"
Reconstruction
38.6
LFOV
NEAR
FAR
23.3
22.6
22.5
3.2
3.4
3.4
16.3
16.1
16.1
1.9
2.0
2.0
21.8
21.8
21.8
29.3
LFOV
NEAR
FAR
76.1
23.2
23.0
4.3
3.4
3.6
54.3
16.8
16.8
2.8
1.9
1.9
19.9
LFOV
NEAR
FAR
425.7
26.3
24.7
32.6
4.8
4.2
297.5
22.2
21.6
123.3
124.8
116.4
114.2
45.1
48.0
Planning vs aligned daily
Planning vs normal error daily
a
Bladder points
Avg rms Std rms Avg rms Std rms Avg rms Std rms
For 38.6, 29.3, 19.9 cm FOV, respectively.
Medical Physics, Vol. 29, No. 11, November 2002
All points in FOV
All points
Avg rms
Std rms
2.1
2.1
2.1
109.1
24.3
23.1
6.3
2.4
1.8
197.0
85.1
80.9
8.6
13.7
12.5
61.2
22.5
22.4
2.4
2.1
2.2
383.0
27.7
23.5
6.4
6.7
2.1
430.7
139.6
123.1
5.1
11.3
13.4
12.5
5.5
4.5
287.9
24.8
24.4
8.4
3.0
3.0
948.5
39.5
32.5
11.7
8.6
5.4
644.1
172.4
148.9
7.0
14.2
18.2
3.9
4.6
58.1
58.2
2.3
2.8
156.5
200.0
18.2
16.3
139.2, 108.2, 72.0a 23.4, 20.9, 13.6a
199.3, 179.1, 143.6a 20.8, 19.3, 14.1a
Avg rms Std rms
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FIG. 9. Comparison of the DVHs for the dose optimized using the planning
CT image !dashes" and the potential treatment dose, that would result from
that plan being delivered to the regions of interest amidst anatomical
changes !solid lines" based upon the complete FOV online CT.
23–32 HU in the bladder and 34 –36 HU in the prostate. The
error was reduced by around 100 HU in the rectum because
the NEAR and FAR images better reflected the anatomical
changes between the planning and online images. The improvement was only marginal for the set of all points with
29.3 and 19.9 FOVs because the values beyond the scan
FOV estimated by NEAR and FAR are essentially only as
good as the planning values, so the net improvement is diluted.
Finally, Table I quantifies the comparison between NEAR
and FAR. For the three contoured regions, the differences are
negligible. This indicates that even coarsely aligned peripheral data are generally sufficient to improve the reconstruction of internal regions. For the set of FOV points, the NEAR
method results in slightly higher rms values and standard
deviations, indicating that NEAR can be a little less reliable
for peripheral points within the scan FOV. The biggest difference between NEAR and FAR is for the set of all points,
indicating that for the outer points estimated from a version
of the planning image, there is some benefit to having an
aligned planning image over a normal-error misaligned version.
B. Application of NEAR and FAR to dose calculations
One important aspect of NEAR and FAR is the opportunity for quantitative improvements and artifact reduction.
These benefits may also apply to applications that use these
images, such as dose calculations. A dose distribution was
optimized for the planning CT volume and the resulting
DVHs are shown as the dashed ‘‘Opt’’ curves in Fig. 9. Also
shown are the solid ‘‘Tx’’ curves indicating how this same
plan would be delivered to one of the treatment CT volumes
based upon rigid-body alignment of bony-anatomy. The dose
calculations used convolution/superposition, the accuracy of
which has been reviewed in many studies.81,82 Changes in
the internal anatomy cause discrepancies between the sets of
curves. This is both because the anatomical changes affect
the dose distribution in physical space and because indiMedical Physics, Vol. 29, No. 11, November 2002
FIG. 10. Bivariate histograms tallying the percentage of error in dose vs
percentage of error in image value for 19.9 cm limited field-of-view images
and fusion-aligned reprojection images.
vidual anatomical structures move relative to this dose distribution. However, DVHs !not shown" created using the two
different dose calculations !for the planning and online images" but using the same ROIs !from the planning CT", indicate that the calculated dose distributions are very similar
and that ROI changes are the predominant source of discrepancies seen in Fig. 9. The effects of organ motion on dose
distribution are not unique to a particular delivery system,
but instead underscore the importance of online CT for verification.
Dose calculations were run for LFOV image volumes and
FAR volumes at the three FOV sizes. By tallying the dosimetric error in each voxel versus the image value error in each
voxel, bivariate histograms were created. These are shown
for the LFOV and FAR dose distributions for the 19.9 cm
images in Fig. 10. There is little correlation between dosimetric error and the image value error, indicating that image
value errors analyzed earlier in this paper do not reliably
predict the dosimetric error. This is attributable to the systematic changes in attenuation and scatter. Both the dosimetric and image value errors are generally smaller for FAR
than for the LFOV image, but there is again no clear correlation between the two axes. The bivariate histogram for
NEAR is not shown becomes of its closeness to the FAR
histogram. For LFOV, FAR, and NEAR, errors were generally smaller for the larger FOVs, but this did not introduce
obvious correlation between the axes.
Ideally, the DVHs for the dose calculations to the LFOV
and FAR images would exactly match the solid curves in
Fig. 9, indicating that the dose delivered !or to-be-delivered"
would be precisely known and not degraded by the LFOV.
Even though this dose might not match the optimized dose
!and in this case it does not due to internal organ motion" the
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FIG. 12. !a" LFOV reconstruction for a 10.5 cm FOV, !b" the NEAR reconstruction, and !c" the 2-iteration NEAR2FAR reconstruction. The contours
from the complete FOV online image are overlaid for reference.
strategy being pursued, and is a subject of future investigations in adaptive radiotherapy.
FIG. 11. Comparison of the DVHs using the complete FOV online CT
!CFOV" vs the three LFOV images at !a" 38.6 cm FOV, !b" 29.3 cm FOV,
!c" 19.9 cm FOV, and !d" vs the FAR image based upon the 19.9 cm FOV.
goal is to be correctly apprised of any problems so that appropriate modifications can be made based upon reliable information.
The results of these dose calculations are shown in Fig.
11, comparing the dose calculated using the complete FOV
online CT image with the dose calculation based upon LFOV
and FAR images. The DVHs all used the same ROIs, from
the complete FOV online image. The differences between the
curves do not represent different doses being delivered, as
the dose actually received by a patient is not affected by the
CT FOV. Instead, the differences represent the error in the
dose calculation from using LFOV or FAR images as opposed to the complete FOV online CT.
It can be seen that there are discrepancies in the dose
distributions calculated using the LFOV online images.
These differences are small for the 38.6 cm LFOV image
#Fig. 11!a"$, but become more substantial for the 29.3 cm
#Fig. 11!b"$ and 19.9 cm #Fig. 11!c"$ LFOV images. In these
three cases the dose is systematically overestimated for all
structures with increased distortion as the FOV decreases.
For this particular case, if only a LFOV online image was
available, the resulting dose calculation might lead one to
believe that the tumor was receiving an unnecessarily high
dose, at the expense of overdosing the bladder and rectum.
This could prompt modifications to the patient’s position or
the treatment delivery in order to rectify the illusory overdosing.
On the other hand, the DVHs based on the dose calculations to the FAR images are virtually indiscernible from the
doses to the complete FOV planning image. This held true
for all tested FOVs so only the DVHs for the smallest FOV
are shown #Fig. 11!d"$. Dose distributions based upon NEAR
images were also indistinguishable from the complete FOV
calculations, an example of which is shown later. Thus,
NEAR and FAR can improve upon the accuracy of dose
calculations. The impact on clinical practice depends on the
magnitude and nature of the inaccuracy and on the adaptive
Medical Physics, Vol. 29, No. 11, November 2002
C. Iterative NEAR2FAR results
An iterative application of NEAR and FAR was tested for
a FOV of 10.5 cm that encompasses the entire prostate in
each daily image, but not necessarily the rectum or bladder.
The LFOV reconstruction of this data set is shown in Fig.
12!a", and the NEAR reconstruction is shown in Fig. 12!b".
The contours from the complete FOV online image are again
overlaid for reference. FAR was not immediately possible
because the distortion of image values precluded a successful
fusion. A NEAR image was created, and by fusing the interior scan region to the planning CT a NEAR2FAR image
could be generated. The NEAR2FAR image is shown in Fig.
12!c". Streak and junctioning artifacts are reduced with the
improved alignment.
Histograms were tallied for the differences between the
LFOV, NEAR, and NEAR2FAR images over the 4 patientdays for which NEAR2FAR images were generated. Figure
13 shows these for the prostate ROI and the set of all FOV
points. Again the NEAR and NEAR2FAR histograms are
virtually identical but the LFOV histograms are shifted by
over 1000 HU !100%". The other ROIs are not included
since they were not in the online scan FOV.
FIG. 13. Histograms of the voxel differences between the complete field-ofview online CT and the LFOV, NEAR, and NEAR2FAR images tallied over
four patient-days.
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TABLE II. The mean and standard deviation rms values average over four patient-days for three different
reconstruction methods with 10.5 cm FOV. rms values were scored in the prostate region, and in the sets of all
FOV points and all points. Other ROIs were not contained within this FOV. The rms values between the aligned
and misaligned planning images vs the complete FOV online images are shown for reference.
Prostate points
All points in FOV
All points
FOV !cm"
Reconstruction
Avg rms
Std rms
Avg rms
Std rms
Avg rms
Std rms
10.5
LFOV
NEAR
NEAR2FAR
1443.8
49.7
50.1
24.7
5.5
5.5
2037.5
96.0
111.0
15.4
11.9
38.8
758.7
182.9
150.5
4.0
16.0
37.6
58.1
58.2
2.3
2.8
76.7
138.4
19.1
16.5
156.5
200.0
18.2
16.3
Planning vs aligned daily
Planning vs normal error daily
Additional quantitative results can be seen in Table II.
These values indicate the severity of the LFOV artifacts for
the 10.5 cm FOV, and show the improvements when using
NEAR or NEAR2FAR. The rms errors for these images are
also lower than for the planning image given the normal
setup error. The doses calculated using this LFOV image,
NEAR, and NEAR2FAR are shown in Fig. 14. The DVHs
use the known contours from the complete FOV online image. The LFOV dose calculation overestimates the prostate
dose by approximately 15%, and the rectum and bladder
doses have areas of both overestimation and underestimation. The dose distributions calculated using NEAR and
NEAR2FAR produce DVHs indistinguishable from the full
FOV dose calculation.
D. FAR with cross-energy CT
The CT performance phantom, imaged with the University of Wisconsin TomoTherapy Prototype is shown in Fig.
15. The dose was 2 cGy, and the complete scan FOV of 40
cm was reconstructed to a 512!512 matrix. This image
shows a 20 cm solid water phantom !Gammex-RMI, Middleton, WI" that contains five different inserts and is surrounded
by a bone-equivalent annulus. All air-holes sized 1.2 mm and
larger are clearly differentiated. All three 29 mm contrast
inserts are visible, with the lowest contrast being "3%.
Slightly tighter plugs would remove any air that artificially
enhances some of the contrast edges. The nominal slice
thickness was 0.5 cm and the scan-pitch was 1.6. A solid
FIG. 14. Comparisons of a dose calculation for complete FOV online images !CFOV" and !a" the 10.5 cm
LFOV image, !b" the 10.5 cm-based NEAR image, and
!c" the 10.5 cm-based NEAR2FAR image, for the rectal
points, bladder points, and prostate points.
Medical Physics, Vol. 29, No. 11, November 2002
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Ruchala et al.: Methods for improving limited field-of-view
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FIG. 15. A 2 cGy MVCT image on the University of Wisconsin Tomotherapy Prototype shows contrasts of 3% !top" and resolutions of 1.2 mm for
air holes !bottom". These regions are expanded on the left with alternate
window/level settings. The maximum energy was 3.5 MeV and the mean
energy was 1.03 MeV.
water plug accommodated an ion chamber !Exradin Model
A12s, Standard Imaging, Middleton, WI" used to measure
the dose to a slice accumulated during a 20-slice MVCT
scan. Ion chamber measurements in the center of the phantom were within several percent of those taken in a peripheral portal of the phantom. Measurements with thermoluminescent dosimeters corroborated these dose values.
The performance of this MVCT is further demonstrated
by the lung images of a human patient shown in Fig. 16. One
slice uses a window and level to accentuate soft-tissue con-
FIG. 16. Two lung images from a human patient using 3 cGy TomoTherapy
MVCT. The window and level are set to emphasize soft-tissue contrast in
one image, and lung brachiation in the other.
Medical Physics, Vol. 29, No. 11, November 2002
FIG. 17. !a" A slice from a canine CT on a commercial kilovoltage CT
scanner, !b" the corresponding slice using 2 cGy MVCT, !c" a LFOV version
of the same MVCT slice, and !d" the FAR reconstruction from the LFOV
data, augmented with planning CT data. By reducing the LFOV artifacts,
one of the eyes that was occluded in the LFOV reconstruction becomes
visible in the FAR reconstruction.
trasts, the other uses a wider window to include the brachiation in the lungs. In both images, the patient exceeded the 40
cm FOV. The pitch was 1.0 and the dose was 3 cGy, as
confirmed by measurements in phantoms and TLDs on the
patient.
The canine images are shown in Fig. 17. Figure 17!a"
shows a slice from a conventional kilovoltage CT of the dog.
The same slice is shown in Fig. 17!b", acquired with a 2 cGy
MVCT. A LFOV MVCT data set was created from the complete data set by discarding approximately half of the nonzero ray integrals !one-quarter from each side of the sinogram". The reconstructed LFOV MVCT slice is seen in Fig.
17!c" and the FAR image is shown in Fig. 17!d".
Of particular interest is that these canine data sets were
not only acquired on different CT systems but at different
energies, requiring that FAR combine MV and kV data. The
differences between these data sets are most pronounced for
higher-Z materials !e.g., bone" because of the increased
prevalence of photoelectric interactions at kV energies,
which inflates the reconstructed values relative to electron
density. This effect is also seen for high-Z endotrachial tube
ventilating the dog during anesthesia: it is very bright in the
kV image, but barely visible for the same window/level in
the MV image. These results show that FAR can be used to
augment MV data sets with kV planning CT data, even in the
presence of high-Z structures inside, outside, and straddling
the online FOV.
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Ruchala et al.: Methods for improving limited field-of-view
2601
FIG. 19. !a" A slice from the human lung CT on a commercial kilovoltage
CT scanner, !b" the corresponding slice using 3 cGy MVCT, limited by the
40 cm FOV, !c" a 20.9 cm FOV version of the same MVCT slice, and !d" the
FAR reconstruction from the 20.9 cm FOV data, augmented with planning
CT data.
FIG. 18. !a" Histograms of the voxel differences between the planning kVCT
and the limited field-of-view #black dashed$, fusion-aligned reprojection
#black solid$, and complete FOV MVCT #gray solid$ images. !b" Histograms
of the voxel differences between the complete FOV online MVCT and the
limited field-of-view #black dashed$ and fusion-aligned reprojection #black
solid$ images.
Another interesting structure is crystalline of eye that is
clearly visible in the kVCT image and is also visible, albeit
noisier, in the 2 cGy MVCT. The visibility of this structure is
impaired by the limited FOV, but some of this loss is recovered by using FAR. In particular, one eye is again seen just
inside the scan FOV through the use of FAR. Visibility of the
other eye remains impaired because it lies on the junction of
the two data sets. This illustrates the case that even if interior
artifacts may not be considered problematic for visualization
of central structures, the cupping artifacts may impair conspicuity of more peripheral structures of interest.
Figure 18 presents difference histograms for these canine
images. The differences in Fig. 18!a" are between the kVCT
planning image and the complete FOV !solid gray curve",
LFOV !dashed black curve", and FAR MVCT !solid black
curve" images. The long negative tails are attributable to increased presence of photoelectric interactions in the kVCT
images. However, the goal is not to match the planning CT
image, but to better represent the complete FOV online CT in
spite of differences relative to the planning CT. Figure 18!b"
histograms the differences between the FAR and LFOV images versus the complete FOV MVCT image, showing the
improvements with FAR. The rms difference between the
complete FOV online MVCT and the planning kVCT is
237.3 HU, but use of this planning CT data in conjunction
with the LFOV online MVCT data reduces the rms from
1152.8 HU for the LFOV image to 33.0 for the FAR image.
The FAR results for the human lung images are shown in
Fig. 19. A planning kVCT image containing the tumor is
shown in Fig. 19!a" and the corresponding slice of the
Medical Physics, Vol. 29, No. 11, November 2002
MVCT is seen in Fig. 19!b". There are some artifacts in this
image as the patient exceeded the 40 cm FOV, and truncating
the data to represent a 20.9 cm FOV exacerbates these artifacts #Fig. 19!c"$. FAR was applied to the 20.9 cm FOV
image volume and the corresponding slice is shown in Fig.
19!d". The lack of a complete FOV online image for comparison precluded the computation of rms values and error
histograms for this FAR image.
IV. DISCUSSION
As demonstrated previously, NEAR and FAR can be useful for qualitatively reducing cupping artifacts in LFOV images and quantitatively improving the reconstructed values.
Like other LFOV compensation techniques, this method is
useful in situations where the LFOV reconstruction artifacts
qualitatively or quantitatively affect a desired imaging task
or application. The quantitative benefits of NEAR and FAR
have also been found to apply to the use of LFOV images for
dose calculations.
There are also several other benefits to NEAR and FAR.
These methods can be applied automatically, if desired, or
initiated at the request of a user. They do not require the
collection of additional online data, which could increase
patient dose, nor do they preclude use of the original LFOV
images. There is no need to anticipate the extent to which a
given LFOV reconstruction will be considered sufficient by a
particular user, but instead NEAR and/or FAR can be applied
after the scan in situations where improvements are sought.
The majority of the computational time is for forward projection and reconstruction, and these steps can potentially be
optimized to subsecond times per slice.
Another benefit is that NEAR and FAR only require a
priori information in the form of the planning CT images.
The processing is performed in sinogram space but the current implementation uses reprojected data sets. Access to the
original planning CT sinogram data is not necessary and may
not be available for commercial CT scanners, and was not
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2602
TABLE III. Applicability of FAR.
Internal anatomy
!from online CT,
inside FOV"
Peripheral anatomy
!from planning CT,
outside FOV"
FAR applicability
Same
Same
FAR unnecessary
Patient unchanged so planning CT accurately
represents anatomy and is applicable to quantitative
applications !e.g., dose calculations"
Same
Different
FAR marginal
Online CT data provide little new information
relative to planning CT, so FAR offers only limited
improvement over planning CT
Different
Same
Ideal FAR case—planning CT misrepresents internal anatomy, but
FAR patient representation potentially perfect
FAR-based quantitative information
potentially perfect
Different
Different
FAR beneficial !most real cases"—planning CT
misrepresents internal and external anatomy, but
FAR can reduce LFOV artifacts
FAR can quantitatively improve image values
Dose calculation with FAR can be more
accurate than using LFOV CT or planning CT
available for this study. Whether NEAR and FAR might benefit from access to this raw sinogram data is a topic for
further investigation. Such a study would likely need to account for the differences in scan geometry, preprocessing
steps, beam spectra, and calibration that are typically
handled within a CT system.
One limitation of these methods is that like with other
LFOV compensation techniques using a priori information,
the results depend upon the validity of those estimations.
Since the LFOV online CT typically lacks peripheral data,
the reconstruction of outer regions with FAR is estimated
based upon the planning CT images and is only meant to
provide a context of the patient’s shape. To the extent that it
is imperfect a priori information, the outer contour and other
soft-tissue structures outside the scan FOV in the FAR image
may more closely resemble the planning CT than those structures at time of treatment. This problem is mitigated by positioning the structures of interest within the available FOV,
as application of NEAR or FAR can reduce artifacts and
quantitatively improve the reconstruction of this scan region,
even given imperfect a priori information. It was also found
that the peripheral regions, albeit imperfect, are sufficiently
representative to enable improved dose calculations.
Another limitation is that FAR requires the combination
of different data sets, so artifacts can result. These are most
commonly rings or arcs in the vicinity of where the online
CT FOV ends !Fig. 6", and are commonly minor as compared to the LFOV artifacts. Different types of smoothing
and junctioning were attempted to mitigate this effect, and
the preferred method was a linear phase-in of the planning
CT sinogram data over the peripheral 15 points of the online
sinogram. Alternate filters or other improvements in FAR
may further reduce artifacts, but their complete elimination
for all cases is unlikely. As mentioned earlier, artifacts arise
Medical Physics, Vol. 29, No. 11, November 2002
from inconsistencies in the data sinograms. To the extent that
the patient’s outer anatomy has changed between the planning CT and the online CT or the data sets are misaligned
there will likely be inconsistencies in the NEAR or FAR
sinogram.
This raises the issue of when FAR will be most and least
useful. The applicability is summarized in Table III, in the
context of whether inner and/or peripheral anatomy has
changed from the time of the planning CT to that of the
treatment-time CT. Obviously, anatomical changes are much
more complex than the four categorizations used, but the aim
is to illustrate the utility of FAR at the logical extrema.
NEAR will be useful under similar conditions to FAR, but
the normal error can impact results. The first row makes that
point that if there are changes in neither the internal anatomy
nor peripheral anatomy, then FAR is unnecessary, because
the patient’s anatomy is identical to the planning CT. Similarly, if there are changes only to the peripheral anatomy
beyond the FOV of the online CT system #Table III, row 2$,
then FAR does not provide many benefits over using the
planning CT since the collected data only contain marginal
information about the peripheral regions. FAR would still be
successful at improving the LFOV images but offers few
benefits over using the planning CT itself, since the latter is
sufficiently representative.
In practice, a patient’s anatomy can change, which is precisely why online CT is beneficial, and these are the cases
where FAR is most applicable. If only a patient’s internal
anatomy changed, but the peripheral anatomy remained rigid
#Table III, row 3$, then FAR could work perfectly. It could
provide exact quantitative and qualitative information, benefiting both the patient representation and applications like
dose calculations. This is because the data collected by the
online CT could completely replace all of the changed por-
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Ruchala et al.: Methods for improving limited field-of-view
tions of the planning CT sinogram. Yet, like the two cases
mentioned previously, this is more of an illustrative extreme
than a common clinical occurrence.
Instead, there will commonly be some combination of internal and peripheral anatomy changes #Table III, row 4$.
This is the situation for the cases evaluated here. The presence of internal anatomical changes motivates using the online CT data instead of the original planning CT. These anatomical changes also introduce inconsistencies between the
online CT and planning CT sinograms, so the FAR image
reconstruction will typically contain some artifacts, the magnitude of which can vary, as seen in Figs. 5, 6, 17, and 19.
Yet, the images can still provide a substantial quantitative
improvement and artifact reduction relative to the LFOV images. Likewise, since the images are not a perfect representation of the patient, the dose calculation will not be perfect;
but in the cases studied !Figs. 11 and 14" this discrepancy
was imperceptible.
In terms of Table III, the biggest conceptual difference
between NEAR and FAR is for row 3 !changed internal
anatomy, identical peripheral anatomy", as the normal setup
error would detract from this theoretically perfect case. For
the multiday results presented, the differences between
NEAR and FAR were less distinct, because anatomical
changes were not localized to internal regions and the notion
of a single ‘‘gold standard’’ image registration is compromised by these anatomical changes. There were some cases
where FAR resulted in fewer artifacts than NEAR and FAR
often had slightly better rms values. Nonetheless, NEAR was
found to be a viable variation of FAR for situations where
explicit fusion is not possible. These NEAR results likewise
demonstrate the results that would be obtained if FAR was
attempted with an inaccurate fusion result. NEAR could also
be selectively followed by FAR iterations, if desired. Since
neither NEAR nor FAR requires additional data collection,
the choice between them does not need to be predetermined.
Both can be tried, they can be combined with an iterative
method, or FAR can even be tried multiple times with different registration results.
A. Going FARther
Finally, there are additional ways in which NEAR and
FAR could be applied or extended. There are many other
qualitative and quantitative methods for adaptive radiotherapy and these may benefit from LFOV compensation
techniques such as the ones presented. NEAR and FAR could
also be used for purposes beyond radiotherapy in cases
where #potentially imperfect$ a priori information is available.
In addition, while this study focused on a priori information in the form of a kilovoltage planning CT, it may be
feasible to apply NEAR and FAR to multimodality images,
such as creating a FAR image by combining an online CT
!MV or kV" data set with a planning MRI image. In such
cases, the MRI or other-modality image would need to be
converted to values compatible with the LFOV data set. A
complex mapping of values might provide the best results,
but even using the alternate modality image to describe the
Medical Physics, Vol. 29, No. 11, November 2002
2603
patient’s outer contour and using a water-equivalency assumption may provide benefits. This is particularly true considering the demonstrated robustness of FAR with regard to
anatomical changes, imperfect alignments, and even the systematic differences in reconstructed values between MV- and
kVCT images. This could present a practical method for developing
upon
long-standing
LFOV
completion
principles.48,49,63 NEAR and FAR may also be extendable to
other types of limited-data situations, such as limited-slice,
limited-projection, or more complex patterns of limited data,
such as reconstructing images collected during modulated
treatment deliveries.19
V. CONCLUSION
The use of planning CT data has been investigated as a
means to improve limited field-of-view !LFOV" online CT
images. The methods of fusion-aligned reprojection !FAR"
and normal-error-aligned reprojection !NEAR" have been
found to be successful at reducing artifacts and quantitatively
improving image values with regards to the complete FOV
images. These benefits can be helpful in cases where LFOV
artifacts impair the desired imaging task.
NEAR and FAR have also been found to improve dose
calculations for LFOV images, as DVHs for the NEAR and
FAR images are virtually identical to those based on complete FOV images. Tests of these methods with canine and
and human data demonstrate their benefits to cross-energy
cases, such as using a MVCT online imaging system with
kVCT planning images.
ACKNOWLEDGMENTS
The authors are very thankful to Dr. Di Yan for providing
the multiday patient CT images, structure sets, and accompanying manual fusion data collected by himself and others
at the William Beaumont Hospital. We also wish to extend
our appreciation to Dr. David Pearson, Eric Schloesser, Dr.
Lisa Forrest, and Stewart Becker for their efforts in the collection of veterinary data at the UW hospital and to Dr.
James Welsh, Dr. Minesh Mehta, Dr. John Balog, and Dr.
Susanta Hui for their participation in the human patient clinical trials.
a"
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1
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