Tahmasebi Nazanin, Boulanger Pierre, Yun Jihyun, Fallone Gino, Noga Michelle, Punithakumar Kumaradevan
1Department of Radiology and Diagnostic ImagingUniversity of AlbertaEdmontonABT6G 2R3Canada.
2Servier Virtual Cardiac CentreMazankowski Alberta Heart InstituteEdmontonABT6G 2B7Canada.
IEEE J Transl Eng Health Med. 2020 Apr 24;8:4300308. doi: 10.1109/JTEHM.2020.2989124. eCollection 2020.
This study intends to develop an accurate, real-time tumor tracking algorithm for the automated radiation therapy for cancer treatment using Graphics Processing Unit (GPU) computing. Although a previous moving mesh based tumor tracking approach has been shown to be successful in delineating the tumor regions from a sequence of magnetic resonance image, the algorithm is computationally intensive and its computation time on standard Central Processing Unit (CPU) processors is too slow to be used clinically especially for automated radiation therapy system.
A re-implementation of the algorithm on a low-cost parallel GPU-based computing platform is utilized to accelerate this computation at a speed that is amicable to clinical usages. Several components in the registration algorithm such as the computation of similarity metric are inherently parallel which fits well with the GPU parallel processing capabilities. Solving a partial differential equation numerically to generate the mesh deformation is one of the computationally intensive components which has been accelerated by utilizing a much faster shared memory on the GPU.
Implemented on an NVIDIA Tesla K40c GPU, the proposed approach yielded a computational acceleration improvement of over 5 times its implementation on a CPU. The proposed approach yielded an average Dice score of 0.87 evaluated over 600 images acquired from six patients.
This study demonstrated that the GPU computing approach can be used to accelerate tumor tracking for automated radiation therapy for mobile lung tumors. Clinical Impact: Accurately tracking mobile tumor boundaries in real-time is important to automate radiation therapy and the proposed study offers an excellent option for fast tumor region tracking for cancer treatment.
本研究旨在开发一种精确的实时肿瘤跟踪算法，用于利用图形处理单元（GPU）计算进行癌症治疗的自动放射治疗。尽管先前基于移动网格的肿瘤跟踪方法已被证明在从磁共振图像序列中勾勒肿瘤区域方面是成功的，但该算法计算量很大，在标准中央处理器（CPU）上的计算时间太慢，无法用于临床，尤其是自动放射治疗系统。
在基于低成本并行GPU的计算平台上对该算法进行重新实现，以加速计算，使其速度适合临床应用。配准算法中的几个组件，如相似性度量的计算，本质上是并行的，这与GPU的并行处理能力非常契合。数值求解偏微分方程以生成网格变形是计算量很大的组件之一，通过利用GPU上快得多的共享内存对其进行了加速。
在NVIDIA Tesla K40c GPU上实现时，所提出的方法在计算加速方面比在CPU上实现提高了5倍以上。在所提出的方法对从6名患者获取的600幅图像进行评估时，平均骰子系数得分为0.87。
本研究表明，GPU计算方法可用于加速移动性肺肿瘤自动放射治疗中的肿瘤跟踪。临床影响：实时准确跟踪移动肿瘤边界对于自动放射治疗很重要，所提出的研究为癌症治疗中的快速肿瘤区域跟踪提供了一个很好的选择。
