Multi-Criteria Optimization for Radiation Therapy Planning
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Solution Overview
Problem
Current radiation therapy treatment planning methods face challenges in effectively generating high-quality treatment plans using multi-criteria optimization (MCO) processes, particularly in balancing radiation dose distribution for tumors while minimizing damage to healthy tissues and organs at risk.
Innovation Solution
The method involves using a computer-implemented process that receives dose-distribution-derived functions, probability distributions, and a loss function to define a multi-criteria optimization problem. This process generates multiple output treatment plans by optimizing two or more objective functions based on these inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If multi-criteria optimization is used to generate treatment plans, then treatment plan quality and diversity are improved, but computational complexity and processing time increase
Solution Approach 1:
The optimization process is segmented into multiple independent objective functions, each evaluating a specific aspect of treatment plan quality (e.g., tumor dose coverage, organ-at-risk sparing, treatment time). This allows the complex MCO problem to be broken down into manageable components that can be optimized separately and combined to generate diverse treatment plan options
Solution Approach 2:
The patent introduces a new dimension to the optimization process by incorporating probability distributions that represent uncertainty and variability in treatment outcomes. This adds a probabilistic dimension to the traditional deterministic optimization, enabling the system to generate treatment plans that account for real-world variability while maintaining computational tractability
2Adaptability or versatility
If multiple objective functions are used in MCO, then treatment plan options and clinical relevance are improved, but computation time and processing resources increase
Solution Approach 1:
The patent performs preliminary actions by pre-defining and pre-calculating the objective functions and their weights based on clinical guidelines and patient-specific factors. This preparation allows the actual optimization process to proceed more efficiently, as the framework is already in place and only requires inputting specific patient parameters and solving the optimization problem
3Reliability
If probability distributions are used to represent knowledge from historic treatment plans, then treatment plan reliability is improved, but data processing complexity increases
Solution Approach 1:
The patent uses probability distributions to create a probabilistic copy or representation of historic treatment plan data rather than directly processing the raw historical data. This transformation into probability distributions simplifies the data processing by capturing essential statistical characteristics (mean, variance, skewness) while discarding unnecessary details, thereby reducing processing complexity while maintaining reliability
Data Source
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AI summary
A computer-implemented method for generating a radiation therapy treatment plan for a volume of a patient, the method comprising: receiving an image of the volume; receiving at least one dose-distribution-derived function configured to provide a value as an output based on, as input, at least part of a dose distribution defined relative to said image; receiving a first probability distribution and at least a second, different, probability distribution, the first and at least second probability distributions; defining a multi-criteria optimization problem comprising at least a first objective function based on the at least one dose-distribution-derived function, the first probability distribution and a loss function; and a second objective function based on the at least one dose-distribution-derived function, the second probability distribution and the loss function; and performing a multi-criteria optimization process based on said at least two objective functions to generate at least two output treatment plans.