Purpose: The graphical processing unit (GPU) on modern graphics cards offers the possibility of accelerating arithmetically intensive tasks. By splitting the work into a large number of independent jobs, order-of-magnitude speedups are reported. In this article, the possible speedup of PLATO's ray tracing algorithm for dose calculations using a GPU is investigated.
Methods: A GPU version of the ray tracing algorithm was implemented using NVIDIA's CUDA, which extends the standard C language with functionality to program graphics cards. The developed algorithm was compared based on the accuracy and speed to a multithreaded version of the PLATO ray tracing algorithm. This comparison was performed for three test geometries, a phantom and two radiotherapy planning CT datasets (a pelvic and a head-and-neck case). For each geometry, four different source positions were evaluated. In addition to this, for the head-and-neck case also a vertex field was evaluated.
Results: The GPU algorithm was proven to be more accurate than the PLATO algorithm by elimination of the look-up table for z indices that introduces discretization errors in the reference algorithm. Speedups for ray tracing were found to be in the range of 2.1-10.1, relative to the multithreaded PLATO algorithm running four threads. For dose calculations the speedup measured was in the range of 1.5-6.2. For the speedup of both the ray tracing and the dose calculation, a strong dependency on the tested geometry was found. This dependency is related to the fraction of air within the patient's bounding box resulting in idle threads.
Conclusions: With the use of a GPU, ray tracing for dose calculations can be performed accurately in considerably less time. Ray tracing was accelerated, on average, with a factor of 6 for the evaluated cases. Dose calculation for a single beam can typically be carried out in 0.6-0.9 s for clinically realistic datasets. These findings can be used in conventional planning to enable (nearly) real-time dose calculations. Also the importance for treatment optimization techniques is evident.