A GPU‐accelerated Monte Carlo dose calculation platform and its application toward validating an MRI‐guided radiation therapy beam model

Abstract

Purpose: The clinical commissioning of IMRT subject to a magnetic field is challenging. The purpose of this work is to develop a GPU-accelerated Monte Carlo dose calculation platform based on  and then use the platform to validate a vendor-provided MRIdian head model toward quality assurance of clinical IMRT treatment plans subject to a 0.35 T magnetic field. Methods:  was first translated from  to ++ and the result was confirmed to produce equivalent results to the original code. The ++ code was then adapted to  in a workflow optimized for GPU architecture. The original code was expanded to include voxelized transport with Woodcock tracking, faster electron/positron propagation in a magnetic field, and several features that make g highly user-friendly. Moreover, the vendor-provided MRIdian head model was incorporated into the code in an effort to apply g as both an accurate and rapid dose validation system. A set of experimental measurements were performed on the MRIdian system to examine the accuracy of both the head model and g. Ultimately, g was applied toward independent validation of patient doses calculated by MRIdian's . Results: An acceleration factor of 152 was achieved in comparison to the original single-thread  implementation with the original accuracy being preserved. For 16 treatment plans including stomach (4), lung (2), liver (3), adrenal gland (2), pancreas (2), spleen(1), mediastinum (1), and breast (1), the MRIdian dose calculation engine agrees with g with a mean gamma passing rate of 99.1% ± 0.6% (2%/2 mm). Conclusions: A Monte Carlo simulation platform was developed based on a GPU-accelerated version of . This platform was used to validate that both the vendor-provided head model and fast Monte Carlo engine used by the MRIdian system are accurate in modeling radiation transport in a patient using 2%/2 mm gamma criteria. Future applications of this platform will include dose validation and accumulation, IMRT optimization, and dosimetry system modeling for next generation MR-IGRT systems. C

Development and evaluation of machine learning models for voxel dose predictions in online adaptive magnetic resonance guided radiation therapy

“…Detailed descriptions of the specific workflows and planning strategies for adaptive MRgRT using the ViewRay linac system (Viewray, Cleveland, OH) have been presented previously. 12,13,21,[38][39][40][41] A brief summary, with particular focus on the details related to the online adaptive process, follows. Only if target coverage can clearly be improved or any critical OAR dose constraint is not met will a new plan be created.…”

Relative dosimetry with an MR‐linac: Response of ion chambers, diamond, and diode detectors for off‐axis, depth dose, and output factor measurements

“…The shift of the EPOM is likely to be a function of the magnetic field strength and thus the effect should be lower for low‐field strength MRIgRT systems such as the ViewRay system (0.35 T). These effects were not observed by Wang et al . from profile and PDD measurements performed in a nonmotorized water phantom on a 60 Co ViewRay system.…”

“…This Monte Carlo value needs to be verified independently; however, a shift in the dose distribution measured with diodes was predicted by Gargett et al 22 The shift of the EPOM is likely to be a function of the magnetic field strength and thus the effect should be lower for low-field strength MRIgRT systems such as the ViewRay system (0.35 T). These effects were not observed by Wang et al 28 from profile and PDD measurements performed in a nonmotorized water phantom on a 60 Co ViewRay system. However, the shift in the EPOM would have been difficult to detect with manually positioned detectors (due to the short build-up region of 60 Co beams) and the profiles were measured with EBT2 radiochromic film which is unlikely to misrepresent the position of the dose distribution due to its near water equivalency.…”

MOSFET dosimeter characterization in MR‐guided radiation therapy (MRgRT) Linac

“…The Advanced Dose Setting feature of the TPDS was used to set the grid resolution (1 mm), the number of histories (2 400 000) for the optimization, and the uncertainty value (0.2%). The MC algorithm was validated independently by Wang et al for IMRT treatment verification in the presence of 0.35 T magnetic field.…”