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GPU-Based Monte Carlo Methods For Accelerating Radiographic and CT Imaging Dose Calculations: Feasibility and Scalability

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T Liu

T Liu*, A Ding, X Xu, Rensselaer Polytechnic Inst., Troy, NY

MO-F-213CD-1 Monday 4:30:00 PM - 6:00:00 PM Room: 213CD

Purpose: To develop a Graphics Processing Unit (GPU) based Monte Carlo (MC) code that uses a dual-GPU system to accelerate radiographic simulation and CT imaging dose calculations.

Methods: We considered two clinical cases, a chest x-ray radiography and an abdominal CT scan. In the first case, a voxelized VIP-Man phantom with detailed 3D anatomical information was used and an x-ray beam of 120kVp was simulated. In the second case, a voxelized abdomen phantom derived from 120 CT slices was used, and a GE LightSpeed 16-MDCT scanner was modeled. The CPU version of the MC code was written in C++ and run on Intel Xeon X5660 2.8GHz CPU, then translated into GPU code written in CUDA C and tested on a dual Tesla m²090 GPU system. The code was featured with automatic assignment of simulation task to multiple GPUs, as well as accurate calculation of energy- and material-dependent cross-sections.

Results: Double-precision floating point format was used for accuracy. In the first case, radiograph formation was simulated and doses to the organs listed in ICRP-60 were calculated. When running on a single GPU, the MC GPU code was found to be x13 times faster than the CPU code and x29 times faster than MCNPX. In the second case, doses to the rectum, prostate, bladder and femoral heads were calculated. A speedup of x19 was observed compared to CPU code. These speedup factors were doubled on the dual-GPU system. The imaging dose was benchmarked against MCNPX and a maximum difference of 1% was observed when the relative error is kept below 0.1%.

Conclusions: A GPU-based MC code was developed to simulate radiography and calculate imaging dose using detailed patient and CT scanner models. Efficiency and accuracy were both guaranteed in this code. Scalability of the code was confirmed on the dual-GPU system.

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