GPU-based fast Monte Carlo simulation for radiotherapy dose calculation

Abstract

Monte Carlo (MC) simulation is commonly considered to be the most accurate dose calculation method in radiotherapy. However, its efficiency still requires improvement for many routine clinical applications. In this paper, we present our recent progress toward the development of a graphics processing unit (GPU)-based MC dose calculation package, gDPM v2.0. It utilizes the parallel computation ability of a GPU to achieve high efficiency, while maintaining the same particle transport physics as in the original dose planning method (DPM) code and hence the same level of simulation accuracy. In GPU computing, divergence of execution paths between threads can considerably reduce the efficiency. Since photons and electrons undergo different physics and hence attain different execution paths, we use a simulation scheme where photon transport and electron transport are separated to partially relieve the thread divergence issue. A high-performance random number generator and a hardware linear interpolation are also utilized. We have also developed various components to handle the fluence map and linac geometry, so that gDPM can be used to compute dose distributions for realistic IMRT or VMAT treatment plans. Our gDPM package is tested for its accuracy and efficiency in both phantoms and realistic patient cases. In all cases, the average relative uncertainties are less than 1%. A statistical t-test is performed and the dose difference between the CPU and the GPU results is not found to be statistically significant in over 96% of the high dose region and over 97% of the entire region. Speed-up factors of 69.1 ∼ 87.2 have been observed using an NVIDIA Tesla C2050 GPU card against a 2.27 GHz Intel Xeon CPU processor. For realistic IMRT and VMAT plans, MC dose calculation can be completed with less than 1% standard deviation in 36.1 ∼ 39.6 s using gDPM.

The feasibility of a regional CTDIvol to estimate organ dose from tube current modulated CT exams

“…As the CT community is trying to come up with improved models to estimate organ dose and obtain the best size metric to correlate dose with, different groups have been exploring graphics processing unit (GPU) applications in Monte Carlo simulations to reduce the computational time of such high computationally demanding simulations. [39][40][41] The results look very promising and might enable real-time Monte Carlo simulation on individual patients undergoing CT in the future; however, there are still significant remaining technical, logistical, and resource issues such as automated online segmentation of organs among others. In the interim, the methods described here hope to move our clinical practices away from CTDI vol and DLP to improved dose metrics such as SSDE and beyond.…”

Investigation on using high‐energy proton beam for total body irradiation (TBI)

“…The beam property, beam arrangement, and irradiation setup was the same as in the water phantom study. GPU‐based Monte Carlo codes for proton dose calculation (7) and for photon dose calculation 8 , 9 were used. The computation resolution was $0.137\text{cm}\times 0.137\text{cm}\times 0.5\text{cm}$ matching the CT resolution.…”

New concept on an integrated interior magnetic resonance imaging and medical linear accelerator system for radiation therapy

“…We performed dose calculation using an in-house GPU-based Monte Carlo (MC) dose calculation package gDPM. 22,23 Charged particle transport simulation in a magnetic field was enabled. We first performed the dose calculation for a 10 × 10 cm 2 open field normally impinging to a homogeneous water phantom along the x direction.…”