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There is a huge problem and time-consuming computation to optimize the IMRTtreatment plan. Extracting the optimized plan from the predicted 3D3 so-calledoptimizing the KBP is also involved in this challenge. Some algorithms andmethods have been presented for clustering and down-sampling the voxels to makethe problem smaller, in recent years. In the current research, a noveldown-sampling method is presented for optimizing the knowledge-based planningmore efficiently. The concept of SVSIDB and corresponding down-samplingalgorithm are proposed under title of SMP-2. The algorithm has been run on thedata of 30 patients from the Open-KBP dataset. For each patient, there are 19sets of dose prediction data in this dataset. Therefore, a total of 570KBP-optimizing problems have been solved by applying the QuadLin model in theCVX framework. Resulted plans are evaluated and compared regarding two mainfields which are the quality of the treatment plan as well as the computationefficiency. Solve time is the evaluation criteria for the latter field i.e.computation efficiency. The results of the current study indicated a remarkableimprovement in the computation efficiency. Accordingly, the proposed method,SMP-2, reduced the average solving time by 46% in comparison to the full-dataQuadLin model. The results also show an up to 53% reduction in solve time alongwith up to 22% improvement in clinical criteria compared to the previousresearch. Evaluation of the research results indicated that the SVSIDB has notonly reduced the solve time but also improved the quality of the treatmentplans. This is a remarkable achievement of the proposed model compared to theprevious research and confirmed the significant effectiveness of the SVSIDBmethod which has the potential of even more improvement of the computationefficiency.

Novel Method for More Efficient Optimizing the Knowledge-Based Planning: Specific Voxels of each Structure Influenced by Dominant Beamlets (SVSIDB)