Multi-criteria Radiation Therapy Optimization with Time-Based Constraints
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Solution Overview
Problem
Conventional radiation therapy treatment planning primarily focuses on dose-based constraints, neglecting other critical factors such as treatment modality, delivery time, fractionation schedule, toxicity probabilities, and economic tradeoffs, which are not adequately addressed, leading to suboptimal treatment plans.
Innovation Solution
A multi-criteria optimization method is implemented to determine radiation therapy plans that consider competing objectives including delivery time, fractionation, and economic parameters, using computing devices to generate and iteratively refine plans that balance dose coverage, treatment time, and cost, while exploring Pareto optimal tradeoffs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional radiation therapy treatment planning focuses only on dose-based constraints, then the treatment planning process is simple, but the treatment quality and efficiency are suboptimal
Solution Approach 1:
The treatment planning process is segmented into multiple independent optimization runs, each focusing on a specific criterion (dose distribution, delivery time, fractionation schedule, economic parameters). This allows complex multi-criteria optimization to be broken down into manageable segments that can be executed separately and then integrated to produce comprehensive treatment plans.
Solution Approach 2:
The patent extends the traditional single-objective optimization space by adding multiple new dimensions representing different criteria (delivery time, fractionation, cost). This transforms the problem from optimizing along one dimension (dose) to navigating a multi-dimensional objective space, enabling comprehensive trade-off analysis across all critical treatment parameters.
2Productivity
If multiple competing objectives are considered in optimization, then treatment efficiency and quality improve, but computational complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing optimization results for different criterion combinations. When generating treatment plans, it retrieves and integrates these pre-computed results rather than performing exhaustive real-time optimization, significantly reducing computational complexity while maintaining comprehensive multi-criteria analysis.
Solution Approach 2:
The optimization system serves itself by automatically generating multiple candidate plans across different objective spaces and then self-evaluating them against all criteria. The system autonomously performs the integration and selection processes without requiring external intervention, streamlining the workflow despite the underlying computational complexity.
3Adaptability or versatility
If delivery time and fractionation criteria are included in optimization, then treatment planning comprehensiveness improves, but the number of criteria to optimize increases
Solution Approach 1:
The optimization framework is designed with universal functionality to handle any number and type of criteria through a standardized interface. The same core optimization engine processes dose, time, fractionation, and economic criteria uniformly, allowing the system to adapt to different treatment scenarios without requiring separate specialized procedures for each criterion type.
Data Source
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AI summary
Multi-criteria optimization of radiation therapy tools for receiving 210 a plurality of criteria including one or more sets of competing objectives. The competing objective can include delivery time criteria, fractionation criteria or similar time-based criteria. One or more radiation therapy plans can be determined 220 based on a multi-criteria optimization of the plurality of criteria for the radiation therapy treatment. The one or more optimized radiation therapy plans can be output 250 for consideration by one or more members of a radiation oncology team.