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Online 3D EPID-based dose verification: Proof of concept
Hanno Spreeuw, Roel Rozendaal, Igor Olaciregui-Ruiz, Patrick González, Anton Mans, Ben Mijnheer, and
Marcel van Herk
Citation: Medical Physics 43, 3969 (2016); doi: 10.1118/1.4952729
View online: http://dx.doi.org/10.1118/1.4952729
View Table of Contents: http://scitation.aip.org/content/aapm/journal/medphys/43/7?ver=pdfcov
Published by the American Association of Physicists in Medicine
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Online 3D EPID-based dose verification: Proof of concept
Hanno Spreeuw,a) Roel Rozendaal,a),b) Igor Olaciregui-Ruiz, Patrick González,
Anton Mans, and Ben Mijnheer
Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam 1066 CX, The Netherlands
Marcel van Herk
The University of Manchester, Manchester Academic Health Science Centre, The Christie NHS Foundation
Trust, Manchester M20 4BX, United Kingdom
(Received 24 June 2015; revised 6 May 2016; accepted for publication 12 May 2016;
published 9 June 2016)
Purpose: Delivery errors during radiotherapy may lead to medical harm and reduced life expectancy
for patients. Such serious incidents can be avoided by performing dose verification online, i.e., while
the patient is being irradiated, creating the possibility of halting the linac in case of a large overdosage
or underdosage. The offline EPID-based 3D in vivo dosimetry system clinically employed at our
institute is in principle suited for online treatment verification, provided the system is able to complete
3D dose reconstruction and verification within 420 ms, the present acquisition time of a single EPID
frame. It is the aim of this study to show that our EPID-based dosimetry system can be made fast
enough to achieve online 3D in vivo dose verification.
Methods: The current dose verification system was sped up in two ways. First, a new software pack-
age was developed to perform all computations that are not dependent on portal image acquisition
separately, thus removing the need for doing these calculations in real time. Second, the 3D dose
reconstruction algorithm was sped up via a new, multithreaded implementation. Dose verification
was implemented by comparing planned with reconstructed 3D dose distributions delivered to two
regions in a patient: the target volume and the nontarget volume receiving at least 10 cGy. In both
volumes, the mean dose is compared, while in the nontarget volume, the near-maximum dose (D2)
is compared as well. The real-time dosimetry system was tested by irradiating an anthropomorphic
phantom with three VMAT plans: a 6 MV head-and-neck treatment plan, a 10 MV rectum treatment
plan, and a 10 MV prostate treatment plan. In all plans, two types of serious delivery errors were
introduced. The functionality of automatically halting the linac was also implemented and tested.
Results: The precomputation time per treatment was ∼180 s/treatment arc, depending on gantry
angle resolution. The complete processing of a single portal frame, including dose verification, took
266±11 ms on a dual octocore Intel Xeon E5-2630 CPU running at 2.40 GHz. The introduced
delivery errors were detected after 5–10 s irradiation time.
Conclusions: A prototype online 3D dose verification tool using portal imaging has been developed
and successfully tested for two different kinds of gross delivery errors. Thus, online 3D dose verifi-
cation has been technologically achieved. C2016 American Association of Physicists in Medicine.
[http://dx.doi.org/10.1118/1.4952729]
Key words: real-time, in vivo, EPID, dosimetry, QA
1. INTRODUCTION
With the introduction of amorphous-silicon (aSi) EPIDs, the
interest in EPID dosimetry has risen because of their favorable
characteristics such as fast image acquisition, high resolution,
digital format, and potential for in vivo measurements and 3D
dose verification.1Several groups have implemented dosim-
etry using EPIDs for 3D pretreatment verification2as well
as for (3D) in vivo dose verification, e.g., Refs. 3–7. Also, a
real-time EPID-based verification system based on comparing
predicted to measured portal images has been implemented
clinically.8Furthermore, a quasi real-time system also based
on comparing predicted to measured portal images, augmented
with an isocenter dose comparison, has been used in clinical
routine.9,10 In our study, a new method is proposed, which
combines all of these aspects: real-time EPID-based 3D in vivo
dose verification. The aim of this feasibility study is to show
that we are capable of reconstructing cumulative delivered 3D
dose distributions in real time and generating triggers to stop
the linac in case of a large deviation from the total planned dose
distribution.
2. METHODS AND MATERIALS
2.A. Clinical system overview
In the Netherlands Cancer Institute (NKI), aSi EPIDs
(Perkin Elmer RID 1680 AL5/Elekta iViewGT) mounted on
SL20i and Agility linear accelerators (Elekta, Crawley, UK)
are utilized for dose verification measurements. The software
for the acquisition of portal frames was developed in-house
and allows for the simultaneous extraction of gantry infor-
3969 Med. Phys. 43 (7), July 2016 0094-2405/2016/43(7)/3969/6/$30.00 ©2016 Am. Assoc. Phys. Med. 3969
3970 Spreeuw et al.: Online 3D EPID-based dose verification 3970
mation, e.g., the gantry angle. A backprojection algorithm
based on EPID transmission is applied for 3D in vivo dose
verification of all treatments3,11,12 which have treatment fields
that fit on the EPID and would not cause collisions between the
EPID and the couch. All dose reconstructions are compared
with the 3D dose distributions calculated by the clinically
used treatment planning system ( 9.8, Philips Medical
Systems, Eindhoven, The Netherlands).
In current clinical practice, the actual dose verification is
done offline, thus after a fraction has been delivered. This
implies that some very large delivery errors cannot be cor-
rected for in the remaining fractions by altering the treatment
plan, which is especially relevant when organs at risk (OARs)
have been pushed close to their respective maximum tolerated
doses. A complete overview of the portal dosimetry workflow
at our institution is given elsewhere.3,13
2.B. Online EPID-based 3D dose verification
Our prototype system for real-time dosimetry was de-
signed not only with the aim to perform dose verification
faster than the frame rate of our EPID (2.37 frames/s), but
also to immediately halt the linac in case of serious de-
livery errors as depicted in Fig. 1. During dose delivery,
frames are acquired using the EPID acquisition software;
acquired frames are saved to a network location. The real-
time dose reconstruction software reads the frames, recon-
structs the 3D dose distribution, and performs dose verifica-
tion. Whenever a dose deviation outside of tolerance levels
F. 1. Real-time EPID-based in vivo dosimetry procedure ideally performed
for all fractions of a treatment and complementary to the more sensitive
offline EPID-based in vivo dosimetry method used for the detection of smaller
errors. On the right the communication flow for treatment discontinuation is
depicted.
is detected, a signal is sent to a linac monitoring software
package. This monitoring software was also developed in-
house and is capable of breaking an interlock chain, which
halts the linac. The interlock chain is a physical chain of
electrically operated switches; the linac is physically incapable
of generating radiation whenever one or more of these switches
is not closed. On the linac used for testing, a custom-made
switch was added to the vendor-supplied interlock-chain. It is
this switch which was controlled by the monitoring software.
2.C. Accelerated dose reconstruction
Our 3D dose reconstruction method uses information
derived from the planning-CT scan. As these data are inde-
pendent of the acquired portal images, it is possible to compute
these inputs to the algorithm before the dose delivery starts. A
separate software package was written to generate these inputs.
The dose reconstruction is dependent on the gantry angle, so
inputs have to be generated for several gantry angles. It was
decided to use a resolution of one degree, generating 360 sets
of input data.
Additionally, the back-projection algorithm was an excel-
lent candidate for multithreading.This algorithm back-projects
the dose at the EPID level to a stack of planes through the
patient. As the scattered dose in the patient is modeled as
lateral scatter in each plane, the total dose in each plane
could be computed independent of the other planes, making
a multithreaded implementation straightforward. The imple-
mented module for multithreaded back-projection used as
many threads as available logical CPU cores. After back-
projection, the resulting dose distribution was in gantry-
coordinates and needed to be rotated and resampled to ma-
chine coordinates. This transformation was reimplemented in
a multithreaded way as well. All multithreaded codes were
implemented using OpenMP;14 the Intel OpenMP runtime
library 5.0 was used.
An extra constraint for the accelerated dose engine was
backward-compatibility with our existing dose reconstruc-
tion method. Given the same input data, both methods were
required to reconstruct the same dose in every voxel to an accu-
racy of 1 ppm. A consequence of this requirement is that all
previously published results11–13,15–19 are directly applicable to
the accelerated dose reconstruction system. Specifically, it has
been shown that the mean delivered dose in the planning target
volume (PTV) can be determined to an accuracy of about 3%
for various treatment sites.18,19
2.D. Real-time dose comparison to detect gross errors
In the real-time dose verification system, the linac will be
halted when large overdosage or underdosage is detected. The
method for quantifying a large dose deviation should prefer-
ably have a clear clinical interpretation, making the widely
adopted γ-analysis20 an unfavorable candidate. As the 3D in
vivo dose distribution was calculated, an obvious choice would
be to inspect dose parameters of specific subvolumes, similar
to plan evaluation criteria. The presented real-time dosimetry
Medical Physics, Vol. 43, No. 7, July 2016
3971 Spreeuw et al.: Online 3D EPID-based dose verification 3971
method is capable of dose verification in any defined sub-
volume of the reconstructed dose distribution. In this proof
of concept, the patient volume was divided into two subvol-
umes: target and nontarget volume. The PTV was used as
the target volume, and the nontarget volume consisted of the
volume outside of the PTV receiving at least 10 cGy. The
dose threshold of 10 cGy was added to prevent a large volume
receiving a very low dose from masking overdosages. For the
nontarget volume, both local and global dose deviations were
detected by inspecting the mean dose and the 98th percen-
tile (D2) in the dose–volume histograms (DVHs). It should
be noted that the DVH-parameters selected for inspection
do not affect the calculation speed: the complete DVH was
calculated for each specified volume, making every DVH-
parameter eligible for inspection. The dose distribution in
the relatively small PTV was simply verified by inspecting
only the mean dose. A priori, it is not obvious what would
constitute a large enough deviation to halt the linac. As a
starting point, tolerance levels for the mean dose in the PTV
and D2 of the nontarget volume are set to 10% of the prescribed
fraction dose. For the mean dose in the nontarget volume,
which is much lower than the previous two dose parameters,
a tolerance level of 5% of the prescribed fraction dose was
chosen.
The planned dose distribution, to which the reconstructed
dose was compared, was specified per control-point—in
contrast to the EPID frames which were read-out continuously
during treatment. The reconstructed 3D dose distribution was
compared to the accumulated planned 3D dose distribution,
which was updated every time the gantry reached the next
control-point.
2.E. Testing
The new system was tested by irradiating three different
VMAT plans on an anthropomorphic (Alderson) phantom:
a dual-arc 6 MV head and neck (H&N) plan, a single-arc
10 MV rectum plan, and a single-arc 10 MV prostate plan.
All plans were specifically created for the Alderson phantom:
a virtual tumor and OARs were delineated for each case and
treatment plans were created according to our clinical con-
straints by an experienced treatment planner. The H&N and
prostate plans were to deliver 2 Gy/fraction; the rectum plan
5 Gy/fraction. Modified plans were created by introducing
two types of serious delivery errors in these plans. First, the
number of monitor units to be delivered per control point
was doubled; second, all the moving MLC leaves and jaws
were retracted 10 cm on both sides, creating a large open
field.
All plans were irradiated and analyzed in real time using
the newly developed software. Before irradiation, the anthro-
pomorphic phantom was positioned using the clinical, CBCT-
based, protocol of our institute. The dose to the OARs at the
detection threshold was determined after irradiation by using
the same real-time dosimetry method, only now using the
already recorded EPID movies as input. These recorded EPID
movies consist of the exact same EPID frames recorded and
analyzed during plan delivery.
F. 2. Difference between reconstructed and planned mean dose in the
PTV for the three scenarios (no error, leaves open error, double MU error),
as a function of delivery time. The horizontal line indicates the detection
threshold.
3. RESULTS
3.A. Real-time dosimetry
Figures 2–4show differences between planned and recon-
structed dose distributions for the target and nontarget volumes
during delivery of the rectum VMAT plan. The introduced
errors are clearly discernible. The other two VMAT plans
showed similar results. Table Ilists key features of the irra-
diated plans. For the introduced errors and using the proposed
tolerance levels, the linac would have been halted after 5–10 s
irradiation time; for the deliveries without errors, no deviations
above the threshold were detected. The doses to OARs at the
earliest detection times for each introduced error are listed in
Table II.
3.B. Timing
An essential requirement for online dose verification is
that it should be done in real time, i.e., an EPID-based dose
F. 3. Difference between reconstructed and planned near-maximum (D2)
dose outside of the PTV for the three scenarios (no error, leaves open error,
double MU error), as a function of delivery time. The horizontal line indicates
the detection threshold.
Medical Physics, Vol. 43, No. 7, July 2016
3972 Spreeuw et al.: Online 3D EPID-based dose verification 3972
F. 4. Difference between reconstructed and planned mean doses outside of
the PTV for the three scenarios (no error, leaves open error, double MU error),
as a function of delivery time. The horizontal line indicates the detection
threshold.
measurement and verification should be completed before the
next portal image is taken. The rate at which portal images
are acquired (frame rate) depends on the acquisition mode,
but is about 420 ms for the irradiations investigated in this
research. Table III shows that the average per-frame processing
time (266 ms) is well below the frame rate. Calculation and
comparison of DVH-parameters for the PTV and all defined
OARs did not significantly change the time needed for dose
comparison. Generation of the precomputed data is not time-
critical: it only needs to be done once per treatment and can
be done offline, well in advance of the treatment start. At one
degree gantry angle resolution, generating the precomputed
data took about 3 min/treatment arc (0.5 s/gantry angle) on a
standard workstation; this dataset was typically 1–2 GB large.
The EPID-based 3D dose distribution was initially calcu-
lated on the conical backprojection grid, which rotates with the
gantry since it is fixed to the beam. This grid was comprised
of 256 ×256 pixel slabs parallel to the EPID. The number of
T I. Detection performance of the different dose parameters. Indicated
is the irradiation time after which the tolerance level is reached, with the
percentage of the full delivery time of the treatment arc in parentheses. When
the linac would be halted for each introduced error is indicated in bold. In the
no-error situation, the tolerance level is not reached.
Linac stopped after (s)
Site Dose parameter Tolerance (cGy) Leaves open MU ×2
H&N PTV Dmean 20 16 (23%) 16 (22%)
Non-PTV D2 20 22 (33%) 7 (9.2%)
Non-PTV Dmean 10 5 (6.9%) 5 (6.5%)
Rectum PTV Dmean 50 25 (28%) 12 (6.6%)
Non-PTV D2 50 16 (18%) 10 (5.2%)
Non-PTV Dmean 25 8 (8.8%) 12 (6.6%)
Prostate PTV Dmean 20 8 (15%) 13 (13%)
Non-PTV D2 20 6 (11%) 9 (8.6%)
Non-PTV Dmean 10 5 (8.2%) 9 (8.6%)
slabs is dependent on the gantry angle and varied between
51 and 83 for the arcs considered in this study, hence the
number of nodes in this grid varied between 3.3×106and
5.4×106. The backprojection grid had a spacing of 1 ×1 mm
in the midplane and 5 mm in the perpendicular direction.
Each backprojected dose distribution was transformed using
an affine transform which incorporated bilinear interpolation
and added to the cumulative reconstructed dose distribution
on the final computational grid in machine coordinates. In this
study, the final grids were equal to the TPS grids and had a
voxel size of 2 mm in all dimensions.
3.C. An automated linac halt
The new software package for real-time 3D dose verifica-
tion is able to send a signal to our in-house developed linac
monitoring software. This monitoring software then breaks a
hardware interlock to actually halt the linac. During testing of
the newly developed system, the sending of this halting signal
could be enabled or disabled at will. With the sending enabled,
the linac was successfully halted by the real-time 3D dose
verification system for each of the treatment plans with large
introduced errors included in this study. Note that in the results
shown in Figs. 2–4, the linac obviously was not halted.
4. DISCUSSION
It has been shown that real-time EPID-based 3D dose
reconstruction and verification is possible using our back-
projection system. The online dose verification system has
been successfully combined with a linac halting mechanism,
enabling linac halts whenever deviations exceeding the detec-
tion threshold are found. The deviations introduced for testing
purposes were intended to be large and easily discernible;
the 5% and 10% tolerance levels used in this phantom study
have proven to be able to detect the introduced deviations
early during delivery. Table II shows that none of the OARs
would have received high doses with the introduced delivery
errors: the absolute overdoses are maximally in the order of
90 (rectum), 40 (prostate), and 30 (H&N) cGy. Assuming that
such errors would have been detected at the first fraction, only
minor—if any—modifications to the intended treatment plan
would have been needed. A close inspection of the measured
dose differences shows that the tolerance levels could have
been even tighter, as Fig. 4clearly shows. However, analysis
of actual online in vivo data is needed to assess the variability
in the no-error dose reconstruction when a patient is in the
beam. Dose verification in any subvolume of the patient,
such as OARs, is supported by the method. However, finding
appropriate tolerance levels for halting the linac is not trivial
and will be subject of further investigations. This study has
presented the detection of overdosages, but the system is
equally capable of identifying underdosages.
A concern might be the impact of real-time dosimetry on the
life expectancy of the EPID. Compared to offline dosimetry,
the life expectancy of the EPID will be similar, as the EPID is
used for both methods in the same way. In our clinic, where
every treatment is verified using offline dosimetry, an EPID
Medical Physics, Vol. 43, No. 7, July 2016
3973 Spreeuw et al.: Online 3D EPID-based dose verification 3973
T II. Dose to organs at risk at the detection threshold for both introduced errors.
Dose at detection threshold (cGy)
Site Organ Parameter Leaves open MU ×2
H&N Base of tongue D50 19 6
Brainstem D0 22 16
Constrictor muscles D50 22 6
Larynx D50 21 6
Mandible D0 23 27
Oral cavity D50 16 7
Left parotid gland D50 16 16
Right parotid gland D50 8 1
Spinal cord D0 26 26
Rectum Bladder D0 69 82
Bladder D50 56 65
Bowel area D0 75 87
Prostate Anal sphincter D50 13 9
Bladder D0 13 39
Bladder D50 9 14
Bowel area D0 15 43
Rectal wall D0 15 28
Rectum D0 15 28
lasts on average for 32 months. A deterioration of the EPID
image quality, not a dosimetric failure, is generally the reason
for its replacement.13
Compared to other EPID-based real-time treatment verifi-
cation methods,8–10 the presented method is different in the
respect that the delivered 3D dose distribution is calculated
and verified, instead of a comparison of portal images. Though
both methods are suitable to detect the largest of deviations in
dose delivery, verification of the delivered 3D dose distribution
provides more insight into the relevance of detected deviations
as they can be expressed in differences in DVH-parameters.
Also, determination and interpretation of tolerance levels are
more straightforward when discussing patient 3D dose distri-
butions than, for example, pass rates at the EPID level, which
is the method used by Woodruff.8For example, a constant dose
deviation which is recorded at the EPID at a constant, off-axis
position during delivery, will appear as a deviation of constant
magnitude when comparing predicted and measured portal
images. In 3D, the observed dose deviation will not be constant
but rather be distributed over the patient volume, which aids
in determining the relevance of the delivered dose deviation.
Even so, further investigations are necessary to gain insight
into the sensitivity and specificity of both methods.
T III. Processing times (ms) per portal image for online dosimetry
averaged over all 1960 frames of all nine performed irradiations (no error,
leaves open error, double MU error for all three treatment plans). The PC
used was equipped with a dual octocore (16 physical cores, 32 logical cores)
Intel Xeon E5-2630 CPU, running at 2.40 GHz. Indicated uncertainty is one
standard deviation.
Precomputed data
read
3D dose
calculation
Dose
comparison Total
38±5 67±3 161 ±8 266 ±11
The presented method has been validated using VMAT
treatments, but other treatment techniques (IMRT, 3D CRT)
are equally well supported. In fact, the real-time dose verifi-
cation software developed is unaware of the specific treatment
technique used for dose delivery; it simply takes EPID frames
and reconstructs dose.
5. CONCLUSIONS
It is shown that 3D planned dose distributions from VMAT
treatments can be verified online, i.e., during treatment, by
means of DVH-analysis. This was done using 3D EPID transit
dosimetry in real time, i.e., performing a 3D dose verification
faster than the portal frame acquisition rate. This enables linac
halting without unnecessary delays, which was demonstrated
for two serious delivery errors and three VMAT treatment
plans.
ACKNOWLEDGMENTS
The authors thank René Tielenburg, Lennert Ploeger,
Emmy Lamers, Martijn Barsingerhorn, Nelly Kager, Maarten
Buiter, and Peter Remeijer for extensive support during this
study.
CONFLICT OF INTEREST DISCLOSURE
The authors have no relevant conflicts of interest to disclose.
a)H. Spreeuw and R. Rozendaal contributed equally to this work.
b)Author to whom correspondence should be addressed. Electronic mail:
r.rozendaal@nki.nl
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