Wendy Harris1, Ergys Subashi1, Victoria Yu1, Eric Aliotta1, Ricardo Otazo1, and Laura Cervino1
1Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States
Synopsis
Current
target localization for prostate radiotherapy on a conventional LINAC consists
of acquiring a cone-bean CT (CBCT) image and matching it to implanted fiducial
markers inside the prostate due to poor soft-tissue contrast of CBCT. MRI
provides better soft tissue contrast and no ionization radiation, but there are
very few MR-LINACs, and conventional LINACs with x-ray imaging capabilities are
much more ubiquitous. This study proposes
to use prior MRI acquisitions, PCA motion modeling, and on-board CBCT from a
conventional LINAC to obtain on-board pseudo-MRI for target localization in prostate
radiotherapy.
Background
The
gold standard for target localization in prostate radiotherapy is on-board
cone-beam CT (CBCT), but soft tissue contrast in CBCT is poor, so often times
gold fiducial markers are implanted into the prostate to be utilized for
on-board imaging and target localization [1]. MRI offers superior soft tissue
contrast in the prostate region and no ionizing radiation compared to CT and
CBCT. The combined MRI-LINAC system offers capabilities for MRI-guided
radiotherapy for prostate cancer, but few clinics have an MRI-LINAC due to the
high cost. Conventional LINACs with x-ray capabilities are much more common,
and the ability to generate on-board pseudo-MRI images using a conventional LINAC
would substantially improve target localization accuracy for prostate
radiotherapy and potentially eliminate the need for invasive fiducial
implantation. A previous study used prior 4D MRI, motion modeling, and on-board
kV projections from a conventional LINAC to generate on-board pseudo-4D-MRI to
improve target localization in liver radiotherapy [2]. Other studies have modeled
prostate motion on the basis on Principal Component Analysis (PCA) using
multiple CTs acquired at various time points to analyze the intrafractional
motion and deformation of the prostate, rectum, and bladder [3, 4]. The purpose
of this study is to use prior MRI images, PCA-motion modeling, and on-board
CBCT to estimate on-board pseudo-MRI for prostate radiotherapy target
localization.Methods
The overall workflow is shown in Figure 1.
Prior MRI data: Multiple 3D MRI acquisitions were performed during
the simulation process (MRI in the treatment position) to represent the patient
anatomy and motion (bladder filling and prostate position) at various
timepoints throughout a 30-minute period. One MRI was selected as the reference
MRI (MRIref) and a synthetic CT (sCTref) was generated [5]
from the MRIref.
Motion modeling: Deformable image registration was performed
from the MRIref to all other MRIs. PCA was applied to the DFMs and
the DFM was considered a weighted linear combination of the principal motion
modes.
Pseudo-MRI estimation using the on-board CBCT: The weighting
coefficients from PCA motion modeling were optimized based on minimizing the
difference between the deformed and the on-board CBCT. The
estimated on-board pseudo-MRI is considered a deformation of the MRIref and the goal was to solve for the deformation field map (DFM) that best
estimates the on-board pseudo-MRI based on the deformed sCTref and
the on-board CBCT acquired during patient set-up.
Numerical phantom and prostate patient studies: The method
was evaluated using an anthropomorphic XCAT phantom simulating various bladder
volume and prostate motion based on literature [6] and a prostate patient data.
Table 1 shows the patient imaging properties and Table 2 shows the XCAT
simulation properties and two on-board scenarios for the study: one with 6mm
motion magnitude and 20% bladder volume increase and a second one with 10mm
motion magnitude and 33% bladder volume increase. For the patient data, all
images were post processed to 1.03mm x 1.03mm x 2mm resolution and image size
256 x 256 x 86. For the XCAT data, the resolution was 1.88mm x 1.88mm x 3mm and
image size was 256 x 256 x 102. For the XCAT data, the bladder and prostate
structures were contoured in the estimated and ground-truth on-board MRIs for quantitative
analysis. Volume difference was calculated for the bladder structure and center
of mass shift (COMS) was calculated for the prostate structures. The workflow
was tested for the patient data, and the COMS of the fiducial markers between
the estimated on-board pseudo-MRI and the on-board CBCT was calculated.Results
For
the XCAT data, the volume difference between ground truth and estimated bladder
contours was 1.28% and 7.85% for the two XCAT scenarios, respectively. The COMS
between ground truth and estimated prostate contours was 1.37 and 1.31mm for
the two XCAT scenarios, respectively. Figure 2 shows the MRIref, sCTref, estimated on-board MRI, ground-truth on-board MRI and ground-truth
on-board CBCT for XCAT scenario 2, which corresponds to 33% increase in bladder
volume and prostate position shift of 8mm and 6mm in the anterior-posterior
(AP) and superior-inferior (SI) directions, respectively, from simulation to
treatment. The estimated and
ground-truth bladder and prostate contours are shown in Figure 2, as well. For
the patient data, the COMS between the estimated on-board MRI and the acquired
on-board CBCT were 2.30mm, 1.45mm, and 2.30mm for the 3 fiducials separately. Figure
3 shows MRIref, sCTref, estimated on-board
MRI, and ground-truth on-board CBCT for the prostate patient.Conclusions
Preliminary
results suggest that prior MRI from simulation, motion modeling, and on-board
CBCT acquired for patient set-up can be used to estimate a pseudo-MRI for
on-board target localization in prostate radiotherapy using a conventional
LINAC with only x-ray imaging capabilities. This method can be expanded to
deform other MRI-contrast images acquired during simulation to achieve multiple
estimated on-board pseudo-MRIs with varying contrast. The proposed method may
result in eliminating the need to invasively implant fiducial markers for
prostate localization, since the prostate and other soft tissue contrast is
substantially improved in MRI compared to CBCT.Acknowledgements
No acknowledgement found.References
[1] O’Neill, A., Jain, S., Hounsell, A., O’Sullivan, J., “Fiducial
marker guided prostate radiotherapy: a review,” Br. J. Radiol. 2016
Dec;89(1068):20160296
[2] Harris, W., Wang, C., Yin, FF., Cai, J., Ren, L., “A
Novel method to generate on-board 4D MRI using prior 4D MRI and on-board kV
projections from a conventional LINAC for target localization in liver SBRT” Med.
Phys. 2018 Jul; 45(7):3238-3245
[3] Sohn, M., Birkner, M., Yan, D., Alber, M., “Modelling
individual geometric variation based on dominant eigenmodes of organ
deformation: implementation and evaluation” Phys. Med. Biol. 2005
Dec 21;50(24):5893-908
[4] Sohn, M., Sobotta, B., Alber, M., “Dosimetric treatment
course simulation based on a statistical model of deformable organ motion,” Phys.
Med. Biol. 2012 Jun 21;(57)12:3693-709
[5] Tyagi, N., Fontenla, S., Zelefsky, M., Chong-Ton, M.,
Ostergren, K., Shah, N., Warner, L., Kadbi, M., Mechalakos, J., Hunt, M., “Clinical
workflow for MR-only simulation and planning in prostate” Radiat. Oncol. 2017
Jul 17;12(1):119
[6] Pommer, T., Hun Oh, J., Rosenshold, M., Deasy, J.,
“Simulating intrafraction prostate motion with a random walk model” Adv.
Radiat. Oncol. 2017 Jul-Sep;2(3):429-436