Abstract
•The objective is to cover the maximum part of target without affecting the OARs.•An iterative reweighting algorithm based approach is used to achieve optimality.•09 equally separated beams with dose-volume limits on the OAR have used to treat PTV.•Considered multiple samples from CORT dataset to make a judgment.•Optimal leaf trajectory matching by keeping radiation delivery fixed for each samples.
For a Fluence map optimization problem, we present a unique iterative reweighting algorithm-based method to achieve optimality. The purpose of planning is to determine a dose distribution in such a manner that it should cover the maximum part of the target without affecting the functionality of organ at risk. We suggest a unique methodology to solve dose-volume bounds while maintaining their non-convexity, as compared to earlier approaches that dedicated to convex estimation. The suggested approach is cooperative to competent procedures centered on partial minimization and certainly adjusts to tackle maximum-dose bounds. Fluence mapping is used for the inverse planning which determines the intensity profile of each beam. For the analysis, 09 equally separated beams with dose-volume limits on the organ at risk have used to treat malignancies in the prostate. Cumulative dose-volume histogram is used for the treatment plans quality measurement. We considered 4-different samples from CORT dataset named prostate, Liver, TG119 phantom, and Head & Neck to make a judgment about the generalizability of the performance of the proposed algorithm, and after getting this we finally compared our result with other method using prostate sample to validate our approach. After comparing with other methods centered on dose volume limits we found that in our approach maximum dose can deliver within minimum time across the planned target. For the optimization of trajectory we kept, the radiation delivery rate fixed to its extreme level and employed the independence property of each single leaf pair equally. By making this provision for all total T delivery times, trade-off between distribution time and fluency mapping quality is produced.