GPU-accelerated ray-tracing for real-time treatment planning

17th International Conference on the Use of Computers in Radiation Therapy

物理学;计算机科学

Heinrich, H.^1 ; Ziegenhein, P.^1 ; Kamerling, C.P.^1 ; Froening, H.^2 ; Oelfke, U.^1

German Cancer Research Center (DKFZ), Heidelberg 69120, Germany^1

Institute of Computer Engineering, University of Heidelberg, Heidelberg 69120, Germany^2

关键词: Depth information;    GPU-based algorithms;    Graphics Processing Unit (GPU);    Radiotherapy treatment planning;    Real time constraints;    Real time performance;    Time consumption;    Treatment planning;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/489/1/012050/pdf
DOI  :  10.1088/1742-6596/489/1/012050

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

Dose calculation methods in radiotherapy treatment planning require the radiological depth information of the voxels that represent the patient volume to correct for tissue inhomogeneities. This information is acquired by time consuming ray-tracing-based calculations. For treatment planning scenarios with changing geometries and real-time constraints this is a severe bottleneck. We implemented an algorithm for the graphics processing unit (GPU) which implements a ray-matrix approach to reduce the number of rays to trace. Furthermore, we investigated the impact of different strategies of accessing memory in kernel implementations as well as strategies for rapid data transfers between main memory and memory of the graphics device. Our study included the overlapping of computations and memory transfers to reduce the overall runtime using Hyper-Q. We tested our approach on a prostate case (9 beams, coplanar). The measured execution times for a complete ray-tracing range from 28 msec for the computations on the GPU to 99 msec when considering data transfers to and from the graphics device. Our GPU-based algorithm performed the ray-tracing in real-time. The strategies efficiently reduce the time consumption of memory accesses and data transfer overhead. The achieved runtimes demonstrate the viability of this approach and allow improved real-time performance for dose calculation methods in clinical routine.