.png){kind=icon}
返回
Towards real-time radiation therapy: GPU accelerated superposition/convolution
.png){kind=icon}
OA
AI
DOI：10.1016/j.cmpb.2009.07.004.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We demonstrate the use of highly parallel graphics processing units (GPUs) to accelerate the superposition/convolution (S/C) algorithm to interactive rates while reducing the number of approximations. S/C first transports the incident fluence to compute the total energy released per unit mass (TERMA) grid. Dose is then calculated by superimposing the dose deposition kernel at each point in the TERMA grid and summing the contributions to the surrounding voxels. The TERMA algorithm was enhanced with physically correct multi-spectral attenuation and a novel inverse formulation for increased performance, accuracy and simplicity. Dose deposition utilized a tilted poly-energetic inverse cumulative-cumulative kernel, with the novel option of using volumetric mip-maps to approximate solid angle ray casting. Exact radiological path ray casting decreased discretization errors. We achieved a speedup of 34x-98x over a highly optimized CPU implementation. (C) 2009 Elsevier Ireland Ltd. All rights reserved.
Keyword:
Convolution/superposition
Radiation therapy planning
Graphics processing unit (GPU)
Inverse planning
Adaptive radiotherapy
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4.8
论文数:
7.0K
被引数:
2.1W
机构
引用论文
.png){kind=icon}
分享收藏分享收藏
Anemia and neutrophil‐to‐lymphocyte ratio in laryngeal cancer treated with induction chemotherapy
PREAI
分享收藏
查看更多内容