论文标题
更有效地优化基于知识的计划的新方法：受主导梁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)
论文作者
论文摘要
有一个巨大的问题和耗时的计算来优化IMRT治疗计划。从预测的3D3所谓优化KBP中提取优化计划也参与了这一挑战。近年来，已经提出了一些算法和方法用于聚类和下采样，以使问题较小。在当前的研究中，提出了一种新型的下采样方法，以更有效地优化基于知识的计划。 SVSIDB的概念和相应的下采样算法是根据SMP-2的标题提出的。该算法已在开放式KBP数据集的30名患者的数据上运行。对于每个患者，此数据集中有19套剂量预测数据。因此，通过在CVX框架中应用Quadlin模型来解决总共570 kbp优化的问题。评估并比较了两个主要领域，即治疗计划的质量以及计算效率。解决时间是后一个字段的评估标准，即计算效率。当前研究的结果表明，计算效率有了显着提高。因此，与Full Data Quadlin模型相比，提出的方法SMP-2将平均解决时间减少了46％。与先前的研究相比，结果还显示了求解时间的减少多达53％，临床标准提高了22％。对研究结果的评估表明，SVSIDB不仅减少了解决时间，而且改善了治疗计划的质量。与先前的研究相比，这是该模型的显着成就，并确认了SVSIDB方法的显着效率，该方法具有更大的计算效率提高。
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.
