title: A GPU-accelerated Monte Carlo dose calculation platform and its application toward validating an MRI-guided radiation therapy beam model.
abstract: OBJECTIVE
  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 penelope 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
  penelope was first translated from fortran to c++ and the result was confirmed to produce equivalent results to the original code. The c++ code was then adapted to cuda 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 gpenelope highly user-friendly. Moreover, the vendor-provided MRIdian head model was incorporated into the code in an effort to apply gpenelope 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 gpenelope. Ultimately, gpenelope was applied toward independent validation of patient doses calculated by MRIdian's kmc.
  
  
  RESULTS
  An acceleration factor of 152 was achieved in comparison to the original single-thread fortran 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 gpenelope 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 penelope. 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.
authors.affiliation: Department of Radiation Oncology, Washington University School of Medicine, 4921 Parkview Place, Campus Box 8224, St. Louis, Missouri 63110.
authors.forename: Yuhe
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Department of Radiation Oncology, Washington University School of Medicine, 4921 Parkview Place, Campus Box 8224, St. Louis, Missouri 63110.
authors.forename: Thomas R
authors.initials: TR
authors.lastname: Mazur
authors.affiliation: Department of Radiation Oncology, Washington University School of Medicine, 4921 Parkview Place, Campus Box 8224, St. Louis, Missouri 63110.
authors.forename: Olga
authors.initials: O
authors.lastname: Green
authors.affiliation: Department of Radiation Oncology, Washington University School of Medicine, 4921 Parkview Place, Campus Box 8224, St. Louis, Missouri 63110.
authors.forename: Yanle
authors.initials: Y
authors.lastname: Hu
authors.affiliation: Department of Radiation Oncology, Washington University School of Medicine, 4921 Parkview Place, Campus Box 8224, St. Louis, Missouri 63110.
authors.forename: Hua
authors.initials: H
authors.lastname: Li
authors.affiliation: Department of Radiation Oncology, Washington University School of Medicine, 4921 Parkview Place, Campus Box 8224, St. Louis, Missouri 63110.
authors.forename: Vivian
authors.initials: V
authors.lastname: Rodriguez
authors.affiliation: Department of Radiation Oncology, Washington University School of Medicine, 4921 Parkview Place, Campus Box 8224, St. Louis, Missouri 63110.
authors.forename: H Omar
authors.initials: HO
authors.lastname: Wooten
authors.affiliation: Department of Radiation Oncology, Washington University School of Medicine, 4921 Parkview Place, Campus Box 8224, St. Louis, Missouri 63110.
authors.forename: Deshan
authors.initials: D
authors.lastname: Yang
authors.affiliation: Department of Radiation Oncology, Washington University School of Medicine, 4921 Parkview Place, Campus Box 8224, St. Louis, Missouri 63110.
authors.forename: Tianyu
authors.initials: T
authors.lastname: Zhao
authors.affiliation: Department of Radiation Oncology, Washington University School of Medicine, 4921 Parkview Place, Campus Box 8224, St. Louis, Missouri 63110.
authors.forename: Sasa
authors.initials: S
authors.lastname: Mutic
authors.affiliation: Department of Radiation Oncology, Washington University School of Medicine, 4921 Parkview Place, Campus Box 8224, St. Louis, Missouri 63110.
authors.forename: H Harold
authors.initials: HH
authors.lastname: Li
mesh_terms: D000465:Algorithms; D003196:Computer Graphics; D006257:Head; D006801:Humans; D008168:Lung; D060526:Magnetic Fields; D008279:Magnetic Resonance Imaging / Q000295:instrumentation / Q000379:methods*; D008953:Models, Anatomic; D009010:Monte Carlo Method; D019047:Phantoms, Imaging; D011879:Radiotherapy Dosage; D011880:Radiotherapy Planning, Computer-Assisted / Q000295:instrumentation / Q000379:methods*; D061089:Radiotherapy, Image-Guided / Q000295:instrumentation / Q000379:methods*; D050397:Radiotherapy, Intensity-Modulated / Q000295:instrumentation / Q000379:methods; D012984:Software