Radiotherapy Planning Parameter Optimization for Dose and Delivery Accuracy
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Solution Overview
Problem
Existing radiotherapy planning methods struggle to simultaneously optimize dosimetric quantities and delivery accuracy, relying heavily on manual experience, leading to inefficiencies and subjective errors, and requiring tedious database establishment for different tumor sites and medical institutions.
Innovation Solution
A method that utilizes a GPR prediction model and meta-heuristic algorithms to iteratively optimize planning parameters, integrating dosimetric indicators and delivery accuracy through a multifunctional cost function, reducing reliance on manual experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If existing automatic planning methods (KBP, PBP, MCOP) are used to optimize dosimetric quantities, then dosimetric indicators can be improved, but delivery accuracy cannot be optimized simultaneously
Solution Approach 1:
The patent merges dosimetric optimization and delivery accuracy optimization into a unified framework. The GPR prediction model is integrated into the cost function, allowing simultaneous optimization of both dosimetric indicators and delivery accuracy through a single optimization process rather than separate sequential steps.
Solution Approach 2:
The GPR prediction model serves as an intermediary that bridges the gap between planning parameters and delivery accuracy. Instead of directly measuring delivery accuracy (which requires physical measurement), the model predicts GPR values based on planning parameters, enabling optimization without actual measurement during the planning phase.
2Manufacturing precision
If manual trial and error optimization is performed by clinical staff, then dosimetric indicators can be adjusted, but clinical workload increases significantly
Solution Approach 1:
The system performs self-service optimization by automatically adjusting planning parameters to optimize both dosimetric indicators and delivery accuracy. The algorithm independently iterates through parameter adjustments using the cost function and GPR prediction model, eliminating the need for manual trial and error by clinical staff.
Solution Approach 2:
The optimization process incorporates feedback loops where the GPR prediction model evaluates the predicted delivery accuracy, and the cost function guides parameter adjustments. This closed-loop feedback system automatically refines planning parameters without human intervention, improving efficiency while maintaining optimization quality.
3Reliability
If plan redesign is performed after verification failure, then delivery accuracy can be improved, but treatment time increases
Solution Approach 1:
The system performs preliminary optimization of delivery accuracy during the planning phase by integrating GPR prediction into the cost function. This preliminary action ensures that plans are optimized for both dosimetric indicators and delivery accuracy before verification, reducing the likelihood of verification failure and the need for redesign.
Solution Approach 2:
The optimization process continuously refines both dosimetric and delivery accuracy parameters simultaneously through iterative optimization. This continuous optimization ensures that improvements in one area do not compromise the other, and the plan reaches optimal state before verification, eliminating time-wasting redesign cycles.
4Manufacturing precision
If multiple databases or templates are established for different tumor sites and institutions, then plan quality can be maintained, but system complexity increases
Solution Approach 1:
The optimization framework is designed to be universal and adaptable to different tumor sites, medical institutions, and clinical requirements. Rather than requiring separate databases or templates, the system uses a unified cost function and GPR prediction model that can accommodate various scenarios through parameter adjustments, reducing system complexity while maintaining plan quality.
Data Source
AI summary
A tumor radiotherapy planning design method and apparatus, an electronic device, and a computer storage medium are provided, including: obtaining a current optimization parameter vector set, and calculating a current cost function value; randomly correcting the current optimization parameter vector set to generate alternative optimization parameter vector sets; then performing planning parameter optimization, and calculating corresponding total cost function values; determining current optimal or suboptimal alternative optimization parameter vector sets according to the total cost function values, sampling to update the current optimization parameter vector set and the current cost function value according to the total cost function values of the current alternative optimization parameter vector sets, and then performing an iteration repeatedly until a convergence condition is satisfied; and outputting an optimal optimization parameter vector set after the iteration, determining planning parameters, and calculating and outputting planning MLC leaf positions and dose distributions.