  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.
