Objective: 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. Method: 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. Results: 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. Conclusion: 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.

中文翻译：

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使用支持 CUDA 的非刚性配准算法进行 MRI 实时肺肿瘤追踪

目标：本研究旨在开发一种准确、实时的肿瘤跟踪算法，用于使用图形处理单元 (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可以成功地从一系列磁共振图像中勾勒出肿瘤区域，但该算法的计算量很大，而且其在标准中央处理单元 (CPU) 处理器上的计算时间太慢，无法临床上特别适用于自动化放射治疗系统。方法：在基于 GPU 的低成本并行计算平台上重新实现该算法，以适合临床使用的速度加速该计算。配准算法中的几个组件（例如相似性度量的计算）本质上是并行的，这非常适合 GPU 并行处理能力。以数值方式求解偏微分方程以生成网格变形是计算密集型组件之一，它已通过利用 GPU 上更快的共享内存进行了加速。结果：在 NVIDIA Tesla K40c GPU 上实施，所提出的方法产生了其在 CPU 上实施的 5 倍以上的计算加速改进。所提出的方法产生了 0.87 的平均 Dice 分数，评估了从 6 名患者获得的 600 幅图像。结论：本研究表明，GPU 计算方法可用于加速肿瘤跟踪，用于移动肺肿瘤的自动放射治疗。临床影响：