Radiotherapy Plan Optimization via Adapted Parameter Representation
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Solution Overview
Problem
Current radiotherapy treatment planning is time-consuming and complex, relying on manual trial-and-error methods that are heavily dependent on the planner's experience, and is hindered by the variability in anatomical structures between patients, leading to inefficiencies and inconsistencies in generating optimal treatment plans that balance dose delivery to tumors while minimizing exposure to healthy tissues.
Innovation Solution
A computer-implemented method and system that processes radiotherapy optimization problems by instantiating candidate parameters, converting them into an adapted representation, and using machine learning models, such as deep neural networks, to estimate and generate deliverable treatment plans, reducing planning time and improving consistency through iterative and parallel processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual trial-and-error optimization methods are used to create treatment plans, then the quality of treatment plans can be improved through planner expertise, but the planning time and complexity increase significantly
Solution Approach 1:
The system performs preliminary action by pre-processing patient anatomical data and treatment parameters to create an optimized representation before the actual treatment plan generation. This preprocessing step includes segmenting anatomical structures, defining target volumes, and organizing patient-specific parameters in a standardized format that enables rapid automated optimization, thereby reducing the time required for manual trial-and-error methods while maintaining plan quality
Solution Approach 2:
The patent replaces the mechanical manual optimization process with an automated computational system. Instead of relying on planners to manually adjust parameters through trial and error, the system uses computer-based optimization algorithms that automatically process patient data, evaluate treatment parameters, and generate optimized treatment plans. This substitution dramatically reduces planning time while maintaining or improving treatment plan quality through consistent application of optimization criteria
2Manufacturing precision
If the number of OARs (organs at risk) is increased to provide more comprehensive protection, then the quality of treatment planning is improved, but the complexity of the optimization process increases
Solution Approach 1:
The system applies segmentation by dividing the complex optimization problem into manageable components. Patient anatomical structures are segmented into distinct segments (target volumes, OARs, normal tissues), and the optimization process is segmented into separate processing steps. Each OAR is independently identified and protected according to its specific constraints, allowing the system to handle multiple organs at risk without overwhelming complexity. The optimization parameters are also segmented into different categories that can be processed independently and then integrated
Solution Approach 2:
The patent implements universality through a standardized parameter representation system that can handle any number and type of OARs using the same framework. The system uses a universal data structure and optimization algorithm that adapts to different patient anatomies and treatment scenarios without requiring separate complex processes for each case. This multi-functional approach allows the same system to efficiently manage protection of multiple OARs simultaneously by applying general optimization principles that work across diverse clinical situations
3Manufacturing precision
If anatomical variations between patients are accommodated to improve individualized treatment, then treatment precision is improved, but the consistency and efficiency of planning process deteriorates
Solution Approach 1:
The system handles anatomical variations by dynamically adjusting parameters based on patient-specific anatomy. The parameter representation is designed to capture patient-specific anatomical features while maintaining a consistent processing framework. Key parameters such as target volume definitions, OAR boundaries, and dose constraints are automatically adapted to each patient's unique anatomy through image registration and segmentation processes. This allows the system to maintain high treatment precision for individualized anatomies while preserving planning efficiency through automated parameter adaptation rather than manual adjustment
Data Source
AI summary
Techniques for generating a radiotherapy treatment plan are provided. The techniques include receiving a radiotherapy optimization problem, the radiotherapy problem comprising a plurality of parameters; processing the radiotherapy optimization problem to instantiate a first set of candidate parameters; converting the first set of candidate parameters into an adapted representation; defining an adapted radiotherapy optimization problem as a function of the adapted representation such that a given solution to the adapted optimization problem estimates a solution to the radiotherapy optimization problem; processing the adapted radiotherapy optimization problem to estimate a function of the solution to the adapted radiotherapy optimization problem; and processing the estimated function of the solution to the adapted optimization problem to generate a deliverable radiotherapy treatment plan.