Monte Carlo Simulation of Radiation Transport and its Interactions with Biological Systems

Monte Carlo (MC) simulation is commonly regarded as the most accurate method for radiation transport simulation because of accurate modeling of transport physics and geometry. However, the high computational burden limits its wide applications. Over the years, we have performed systematic developments of Graphics Processing Unit (GPU)-based ultra-fast MC simulation tools on different spatial scales (from nm to m), over various energy ranges (from eV to GeV), and of popular particle types (photon, electron, proton, and heavy ion) and phantom geometries (voxelized geometry and parameterized geometry). With the GPU platform and novel parallelization schemes, substantial acceleration factors (typically over 100x) over conventional CPU based MC simulations have been achieved. Complex simulations were performed for a variety of clinical applications, including but not limited to x-ray imaging, radiation dose calculation, cone-beam CT scattering, and radiotherapy treatment planning. As presented in a point-counterpoint article in Med. Phys., “the GPU technology has enabled near real-time MC calculation.”

Understanding the impacts of radiation in biological systems requires simulation of not only radiation transport, but also the interactions between radiation and the biological systems at multiple spatial and temporal scales. To this end, we have extended our GPU-based MC expertise to developing a microscopic radiation transport simulation tool. Coupled with a multi-scale DNA model of a human lymphocyte nucleus, we can study DNA damages caused by radiation.

  Recent Publications:
  1. Yuan Xu, Yusi Chen, Zhen Tian, Xun Jia(co-senior author), Linghong Zhou, “Metropolis Monte Carlo Simulation Scheme for Fast Scattered X-ray Photon Calculation in CT,” Optical Express, 27(2), 1262 (2019).
  2. Nan Qin, Chenyang Shen, Min-Yu Tsai, Marco Pinto, Zhen Tian, George Dedes, Arnold Pompos, Steve B. Jiang, Katia Parodi, Xun Jia, “Full Monte Carlo-based biological treatment plan optimization system for intensity modulated carbon ion therapy on GPU,” International Journal Radiation Oncology, Biology, Physics, 235, 243, (2018).
  3. Zhen Tian, Steve B. Jiang, Xun Jia, “Accelerated Monte Carlo Simulation on the Chemical Stage in Water Radiolysis using GPU,” Physics in Medicine Biology, 62, 3081, (2017).
  4. Nan Qin, Marco Pinto, Zhen Tian, Georgios Dedes, Arnold Pompos, Steve B. Jiang, Katia Parodi, and Xun Jia, “Initial Development of goCMC: A GPU-oriented Fast Cross-Platform Monte Carlo Engine for Carbon Ion Therapy,” Physics in Medicine Biology, 62, 3682 (2017).
  5. Yongbao Li, Zhen Tian, Ting Song, Zhaoxia Wu, Yaqiang Liu, Steve Jiang, Xun Jia, “A New Approach to Integrate GPU-based Monte Carlo Simulation into Inverse Treatment Plan Optimization for Proton Therapy,” Physics in Medicine Biology, 62, 289 (2017).
  Funding support:
  1. “Monte Carlo based biological treatment plan optimization,” Project#2of CPRIT Multi-investigator Research Awards “Towards Carbon Beam StereotacticBody Radiation Therapy (C-SBRT) for Higher Risk Early Stage Lung Cancer,” RP160661, 08/2016 - 07/2021.