更高效优化知识型计划的新方法：受主束元影响的每个结构的特定体素（SVSIDB）
Novel Method for More Efficient Optimizing the Knowledge-Based Planning: Specific Voxels of each Structure Influenced by Dominant Beamlets (SVSIDB)
AI总结：
针对IMRT知识型计划优化耗时问题，提出基于SVSIDB概念的下采样算法SMP-2，在Open-KBP数据集570个KBP优化问题上的实验表明，该方法较全数据模型平均减少46%求解时间，并在质量和效率上显著优于既往研究。
摘要：
优化IMRT治疗计划存在巨大问题且计算耗时。从预测的3D剂量中提取优化计划，即所谓的优化KBP，也面临这一挑战。近年来，已有一些算法和方法被提出用于对体素进行聚类和下采样以缩小问题规模。在当前研究中，提出了一种新颖的下采样方法以更高效地优化知识型计划。在SMP-2的名称下，提出了SVSIDB的概念及相应的下采样算法。该算法在Open-KBP数据集中30名患者的数据上运行。对于每位患者，该数据集包含19组剂量预测数据。因此，通过在CVX框架中应用QuadLin模型，共求解了570个KBP优化问题。所得计划在两个主要领域进行了评估和比较，即治疗计划质量和计算效率。求解时间是后一领域（即计算效率）的评估标准。当前研究的结果表明计算效率有显著提升。相应地，所提出的方法SMP-2与全数据QuadLin模型相比，将平均求解时间减少了46%。结果还显示，与先前研究相比，求解时间最多减少53%，临床标准最多改善22%。研究结果评估表明，SVSIDB不仅减少了求解时间，还提高了治疗计划的质量。这是所提出模型相较于先前研究的显著成就，并证实了SVSIDB方法的有效性，该方法在进一步提升计算效率方面具有潜力。
英文摘要：
There is a huge problem and time-consuming computation to optimize the IMRT treatment plan. Extracting the optimized plan from the predicted 3D3 so-called optimizing the KBP is also involved in this challenge. Some algorithms and methods have been presented for clustering and down-sampling the voxels to make the problem smaller, in recent years. In the current research, a novel down-sampling method is presented for optimizing the knowledge-based planning more efficiently. The concept of SVSIDB and corresponding down-sampling algorithm are proposed under title of SMP-2. The algorithm has been run on the data of 30 patients from the Open-KBP dataset. For each patient, there are 19 sets of dose prediction data in this dataset. Therefore, a total of 570 KBP-optimizing problems have been solved by applying the QuadLin model in the CVX framework. Resulted plans are evaluated and compared regarding two main fields which are the quality of the treatment plan as well as the computation efficiency. Solve time is the evaluation criteria for the latter field i.e. computation efficiency. The results of the current study indicated a remarkable improvement in the computation efficiency. Accordingly, the proposed method, SMP-2, reduced the average solving time by 46% in comparison to the full-data QuadLin model. The results also show an up to 53% reduction in solve time along with up to 22% improvement in clinical criteria compared to the previous research. Evaluation of the research results indicated that the SVSIDB has not only reduced the solve time but also improved the quality of the treatment plans. This is a remarkable achievement of the proposed model compared to the previous research and confirmed the significant effectiveness of the SVSIDB method which has the potential of even more improvement of the computation efficiency.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
