Radiotherapy Plan Optimization via Scenario-Specific Functions
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Solution Overview
Problem
Existing radiotherapy treatment planning methods are limited by the need to optimize plans for worst-case scenarios, which can result in suboptimal quality and inefficient resource use, as they often ignore or compromise on 'poor performance' scenarios to meet goals in all possible configurations.
Innovation Solution
The method involves using distinct optimization functions for each scenario, allowing for tailored goal achievement and constraint satisfaction in each case, even for challenging scenarios, by defining separate optimization functions for different uncertainties such as tumour shrinkage, patient positioning, and radiobiological parameters, and incorporating prioritized optimization to manage goal priorities effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single optimization function is used for all scenarios to meet goals in every configuration, then reliability is improved, but plan quality deteriorates due to compromise on poor performance scenarios
Solution Approach 1:
The patent divides the optimization process into separate optimization functions for different scenarios. Each scenario (representing different uncertainty realizations) has its own optimization function, allowing tailored optimization rather than a single compromise solution. This segmentation enables each scenario to be optimized independently according to its specific characteristics.
Solution Approach 2:
The patent applies different optimization functions to different scenarios based on their performance characteristics. High-performance scenarios can use stricter optimization criteria while poor-performance scenarios use more lenient criteria, allowing each local scenario to receive appropriate optimization attention without being constrained by worst-case requirements.
2Reliability
If optimization is performed for all possible scenarios including poor performance cases, then reliability is improved, but productivity deteriorates due to inefficient resource use
Solution Approach 1:
The patent applies partial optimization action by using different optimization functions for different scenarios. Instead of applying full optimization effort uniformly to all scenarios, it selectively applies optimization based on scenario performance classification, reducing unnecessary computational effort on poor-performance scenarios while maintaining adequate coverage.
Solution Approach 2:
The patent changes optimization parameters (such as goal strictness, constraint weights, or objective function coefficients) depending on the scenario performance classification. This parameter adaptation allows the system to adjust optimization intensity and resource allocation based on each scenario's characteristics, improving overall efficiency.
3Reliability
If goals are set to be met in all scenarios, then reliability is improved, but adaptability deteriorates as poor performance scenarios limit overall plan flexibility
Solution Approach 1:
The patent introduces dynamic adaptation by classifying scenarios into performance categories and applying different optimization functions accordingly. This dynamic approach allows the optimization strategy to adapt to scenario characteristics, enabling high-performance scenarios to achieve stricter goals while poor-performance scenarios maintain reasonable flexibility through lenient optimization.
Solution Approach 2:
Different goal strictness levels are applied to different scenario groups. High-performance scenarios can have stricter local goals while poor-performance scenarios have more flexible local goals, allowing the overall plan to maintain adaptability while ensuring reliable performance where achievable.
Data Source
AI summary
A scenario-based treatment plan optimization method for radiotherapy treatment is proposed, in which a first and a second possible scenario are defined. Different optimization functions are defined for the scenarios and the treatment plan is optimized applying the first optimization function under the first scenario and the second optimization function under the second scenario, thereby obtaining a first optimized radiotherapy treatment plan.