TomoTherapy Inc., Madison, WI 53717, USA.
Phys Med Biol. 2010 Dec 7;55(23):7175-210. doi: 10.1088/0031-9155/55/23/002. Epub 2010 Nov 16.
We present a novel framework that enables very large scale intensity-modulated radiation therapy (IMRT) planning in limited computation resources with improvements in cost, plan quality and planning throughput. Current IMRT optimization uses a voxel-based beamlet superposition (VBS) framework that requires pre-calculation and storage of a large amount of beamlet data, resulting in large temporal and spatial complexity. We developed a non-voxel-based broad-beam (NVBB) framework for IMRT capable of direct treatment parameter optimization (DTPO). In this framework, both objective function and derivative are evaluated based on the continuous viewpoint, abandoning 'voxel' and 'beamlet' representations. Thus pre-calculation and storage of beamlets are no longer needed. The NVBB framework has linear complexities (O(N(3))) in both space and time. The low memory, full computation and data parallelization nature of the framework render its efficient implementation on the graphic processing unit (GPU). We implemented the NVBB framework and incorporated it with the TomoTherapy treatment planning system (TPS). The new TPS runs on a single workstation with one GPU card (NVBB-GPU). Extensive verification/validation tests were performed in house and via third parties. Benchmarks on dose accuracy, plan quality and throughput were compared with the commercial TomoTherapy TPS that is based on the VBS framework and uses a computer cluster with 14 nodes (VBS-cluster). For all tests, the dose accuracy of these two TPSs is comparable (within 1%). Plan qualities were comparable with no clinically significant difference for most cases except that superior target uniformity was seen in the NVBB-GPU for some cases. However, the planning time using the NVBB-GPU was reduced many folds over the VBS-cluster. In conclusion, we developed a novel NVBB framework for IMRT optimization. The continuous viewpoint and DTPO nature of the algorithm eliminate the need for beamlets and lead to better plan quality. The computation parallelization on a GPU instead of a computer cluster significantly reduces hardware and service costs. Compared with using the current VBS framework on a computer cluster, the planning time is significantly reduced using the NVBB framework on a single workstation with a GPU card.
我们提出了一个新的框架，使大规模强度调制放射治疗（IMRT）计划在有限的计算资源中得以实现，并提高了成本、计划质量和规划效率。目前的 IMRT 优化使用基于体素的射束叠加（VBS）框架，需要预先计算和存储大量的射束数据，导致时间和空间复杂度大。我们开发了一种用于 IMRT 的无体素宽射束（NVBB）框架，能够进行直接治疗参数优化（DTPO）。在这个框架中，目标函数和导数都是基于连续观点进行评估的，放弃了“体素”和“射束”的表示。因此，不再需要预先计算和存储射束。NVBB 框架在空间和时间上都具有线性复杂度（O（N(3)））。该框架的低内存、完全计算和数据并行化特性使其能够在图形处理单元（GPU）上高效实现。我们实现了 NVBB 框架，并将其与 TomoTherapy 治疗计划系统（TPS）相结合。新的 TPS 在一个带有一个 GPU 卡的工作站上运行（NVBB-GPU）。我们在内部和通过第三方进行了广泛的验证/验证测试。通过与基于 VBS 框架并使用具有 14 个节点的计算机集群（VBS-cluster）的商业 TomoTherapy TPS 进行比较，对剂量准确性、计划质量和效率进行了基准测试。对于所有测试，这两个 TPS 的剂量准确性相当（在 1%以内）。除了在某些情况下 NVBB-GPU 可以实现更好的靶区均匀性外，大多数情况下的计划质量相当，没有明显的临床差异。然而，使用 NVBB-GPU 的规划时间比使用 VBS-cluster 减少了很多倍。总之，我们开发了一种用于 IMRT 优化的新型 NVBB 框架。该算法的连续观点和 DTPO 性质消除了对射束的需求，并导致更好的计划质量。在 GPU 上进行计算并行化而不是在计算机集群上进行，可以显著降低硬件和服务成本。与在计算机集群上使用当前 VBS 框架相比，在带有 GPU 卡的单个工作站上使用 NVBB 框架显著减少了规划时间。
