Radiotherapy Plan Optimization via Submanifold Navigation
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Solution Overview
Problem
Current radiotherapy planning methods face challenges in efficiently navigating and optimizing a multitude of Pareto-optimized radiotherapy plans, which are often generated through manual trial-and-error processes due to the complexity of multicriteria optimization problems.
Innovation Solution
The proposed solution involves using specialized computing hardware configurations and techniques to explore the Pareto surface of solutions by establishing a submanifold in weight space, allowing for the estimation of additional solutions based on derivatives of first-order optimality conditions, and navigating this submanifold to identify optimal treatment plan parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual trial-and-error process is used to find acceptable weights for Pareto optimal radiotherapy plan, then the optimization problem can be solved, but the process becomes tedious and time-consuming
Solution Approach 1:
The system pre-generates a comprehensive set of Pareto optimal plans with different weight combinations before the user needs to select a treatment plan. This preliminary optimization phase creates a library of optimal solutions that can be quickly browsed and selected, eliminating the need for time-consuming manual trial-and-error weight adjustment during the actual treatment planning process.
Solution Approach 2:
The system creates multiple copies of the optimization problem with different weight configurations, generating a family of Pareto optimal plans. Instead of solving one optimization problem repeatedly with manual weight adjustments, the system solves multiple optimization problems in parallel or pre-computed, allowing the user to copy and compare different optimal plans to select the most suitable one.
2Manufacturing precision
If full optimization problem is solved for each parameter combination, then accurate Pareto optimal plans are obtained, but the calculation time increases from seconds to an hour
Solution Approach 1:
The optimization problem is segmented into multiple independent sub-problems, each with a specific weight configuration. Instead of solving one large optimization problem iteratively with manual weight adjustments, the system divides the work into separate optimization runs that can be executed in parallel or pre-computed, significantly improving the speed of generating Pareto optimal plans while maintaining accuracy.
Solution Approach 2:
The system performs optimization calculations in advance, generating a comprehensive set of Pareto optimal plans before the user needs to make a selection. This preliminary computation phase creates a ready-to-review library of optimal plans, eliminating the need for time-consuming real-time optimization during the treatment planning consultation process.
3Adaptability or versatility
If multiple Pareto optimal plans are generated, then better plan selection is possible, but significant time and effort are needed to identify and select the most suitable plan
Solution Approach 1:
The system generates multiple copies of optimal treatment plans with different weight configurations and presents them to the user in a comparative format. This allows the user to review and compare different optimal plans side-by-side, making the selection process more efficient while maintaining the flexibility to choose the most suitable plan based on clinical judgment and patient-specific factors.
Data Source
AI summary
Systems and methods are disclosed for exploration and adaptation of radiotherapy treatment plans. Example operations for radiotherapy treatment planning include: obtaining a plurality of solutions (e.g., Pareto-optimal solutions) of a radiotherapy problem, exploring the plurality of solutions to identify an additional solution in a submanifold space (e.g., exploration of a Pareto surface), and generating treatment plan parameters based on the additional solution for use in a radiation therapy treatment. In an example, exploring the plurality of solutions includes: establishing a submanifold space from a manifold space representing the plurality of solutions in fewer dimensions than the weights; producing additional sets of weights in the submanifold space based on derivatives of first-order optimality conditions of the radiotherapy problem, the derivatives determined with respect to the weights; and navigating in the submanifold space to arrive at the additional solution, corresponding to one of the additional sets of weights.