Institute of Medical Physics, University of Erlangen-Nuremberg, Erlangen D-91052, Germany.
Med Phys. 2012 Jun;39(6):2985-96. doi: 10.1118/1.4711748.
Monte Carlo (MC) simulation is an established technique for dose calculation in diagnostic radiology. The major drawback is its high computational demand, which limits the possibility of usage in real-time applications. The aim of this study was to develop fast on-site computed tomography (CT) specific MC dose calculations by using a graphics processing unit (GPU) cluster.
GPUs are powerful systems which are especially suited to problems that can be expressed as data-parallel computations. In MC simulations, each photon track is independent of the others; each launched photon can be mapped to one thread on the GPU, thousands of threads are executed in parallel in order to achieve high performance. For further acceleration, the authors considered multiple GPUs. The total computation was divided into different parts which can be calculated in parallel on multiple devices. The GPU cluster is an MC calculation server which is connected to the CT scanner and computes 3D dose distributions on-site immediately after image reconstruction. To estimate the performance gain, the authors benchmarked dose calculation times on a 2.6 GHz Intel Xeon 5430 Quad core workstation equipped with two NVIDIA GeForce GTX 285 cards. The on-site calculation concept was demonstrated for clinical and preclinical datasets on CT scanners (multislice CT, flat-detector CT, and micro-CT) with varying geometry, spectra, and filtration. To validate the GPU-based MC algorithm, the authors measured dose values on a 64-slice CT system using calibrated ionization chambers and thermoluminesence dosimeters (TLDs) which were placed inside standard cylindrical polymethyl methacrylate (PMMA) phantoms.
The dose values and profiles obtained by GPU-based MC simulations were in the expected good agreement with computed tomography dose index (CTDI) measurements and reference TLD profiles with differences being less than 5%. For 10(9) photon histories simulated in a 256 × 256 × 12 voxel thorax dataset with voxel size of 1.36 × 1.36 × 3.00 mm(3), calculation times of about 70 and 24 min were necessary with single-core and multiple-core central processing unit (CPU) solutions, respectively. Using GPUs, the same MC calculations were performed in 1.27 min (single card) and 0.65 min (two cards) without a loss in quality. Simulations were thus speeded up by factors up to 55 and 36 compared to single-core and multiple-core CPU, respectively. The performance scaled nearly linearly with the number of GPUs. Tests confirmed that the proposed GPU-based MC tool can be easily adapted to different types of CT scanners and used as service providers for fast on-site dose calculations.
The Monte Carlo software package provides fast on-site calculation of 3D dose distributions in the CT suite which makes it a practical tool for any type of CT-specific application.
蒙特卡罗（MC）模拟是一种用于诊断放射学中剂量计算的成熟技术。主要缺点是其计算需求高，限制了其在实时应用中的使用可能性。本研究的目的是通过使用图形处理单元（GPU）集群开发快速的现场计算机断层扫描（CT）专用 MC 剂量计算。
GPU 是一种功能强大的系统，特别适合可以表示为数据并行计算的问题。在 MC 模拟中，每个光子轨迹都与其他轨迹无关；每个发射的光子都可以映射到 GPU 上的一个线程，数千个线程并行执行以实现高性能。为了进一步加速，作者考虑了多个 GPU。总计算分为不同的部分，可以在多个设备上并行计算。GPU 集群是一个 MC 计算服务器，与 CT 扫描仪相连，并在图像重建后立即在现场计算 3D 剂量分布。为了估计性能增益，作者在配备了两个 NVIDIA GeForce GTX 285 卡的 2.6 GHz Intel Xeon 5430 四核工作站上对剂量计算时间进行了基准测试。该现场计算概念已在具有不同几何形状、光谱和过滤的 CT 扫描仪（多层 CT、平板探测器 CT 和微 CT）上的临床和临床前数据集上得到验证。为了验证基于 GPU 的 MC 算法，作者使用经过校准的电离室和热发光剂量计（TLD）在标准的圆柱形聚甲基丙烯酸甲酯（PMMA）体模内测量了 64 层 CT 系统上的剂量值。
基于 GPU 的 MC 模拟得到的剂量值和分布与 CT 剂量指数（CTDI）测量和参考 TLD 分布一致，差异小于 5%。对于在具有 1.36×1.36×3.00mm³的体素大小的 256×256×12 体素胸部数据集模拟 109 个光子历史，使用单核和多核中央处理单元（CPU）解决方案分别需要约 70 和 24 分钟的计算时间。使用 GPU，相同的 MC 计算在单核和多核 CPU 中分别在 1.27 分钟（单卡）和 0.65 分钟（双卡）内完成，而不会降低质量。因此，模拟速度提高了单核和多核 CPU 的 55 倍和 36 倍。性能与 GPU 的数量几乎呈线性比例缩放。测试证实，所提出的基于 GPU 的 MC 工具可以轻松适应不同类型的 CT 扫描仪，并用作快速现场剂量计算的服务提供商。
蒙特卡罗软件包提供了在 CT 套件中快速现场计算 3D 剂量分布的功能，使其成为任何类型的 CT 特定应用的实用工具。
