Radiation Therapy Beam Selection Optimization
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Solution Overview
Problem
Current methods for selecting beam orientations in radiation therapy are computationally intensive and often result in approximate or low-quality treatment plans, making manual selection the prevailing clinical practice, which is inefficient and inaccurate.
Innovation Solution
A system and method that optimize beam orientations by iteratively eliminating beams from a candidate set, using a processor to execute steps of obtaining inputs, optimizing treatment plans, and checking termination criteria, with continuous and differentiable complexity criteria to reduce computational intensity and improve planning efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If greedy methods are used to iteratively add beams to optimize treatment plan, then beam selection quality improves, but computational time increases significantly
Solution Approach 1:
The patent pre-calculates and stores beam contribution metrics and objective function gradients for all candidate beams before the iterative selection process. This preliminary computation enables the greedy algorithm to quickly evaluate beam additions without performing full optimization calculations at each iteration, significantly reducing computational time while maintaining selection quality
Solution Approach 2:
The patent performs a limited number of optimization iterations (e.g., only 1-3 iterations) rather than continuing until full convergence. This partial optimization approach provides sufficient beam selection quality while avoiding the excessive computational time that would result from complete optimization at each iteration
2Measurement precision
If mixed-integer programming methods are used to solve beam orientation optimization, then global optimality is achieved, but computational cost becomes prohibitive
Solution Approach 1:
The patent extracts the beam selection problem from the full treatment plan optimization problem. By pre-calculating beam contribution metrics and using these to guide iterative beam addition, the method separates beam selection from detailed optimization, achieving near-optimal results without the prohibitive computational complexity of mixed-integer programming
Solution Approach 2:
The patent changes the problem parameters by using continuous beam contribution metrics and gradient information instead of discrete binary variables. This transformation enables the use of efficient gradient-based optimization methods rather than computationally intensive mixed-integer programming, while maintaining solution quality
3Adaptability or versatility
If stochastic search methods are used to optimize beam orientations, then search space coverage improves, but processing time increases
Solution Approach 1:
The patent pre-generates a comprehensive set of candidate beams covering the entire search space of possible beam orientations. This preliminary action ensures thorough search space coverage without requiring time-consuming stochastic exploration during the optimization process, as the candidate set already represents diverse beam options
4Measurement precision
If local search methods are used to fine-tune beam angles, then optimization precision improves, but the method fails to escape local minima
Solution Approach 1:
The patent performs preliminary beam selection using gradient information from the objective function to identify promising beam orientations. This preliminary action with broad search capability precedes local refinement, ensuring the process starts from a good global position before applying local search methods for precision tuning
Solution Approach 2:
The patent maintains continuous gradient information throughout the beam selection process, allowing the algorithm to follow the objective function landscape continuously rather than making discrete jumps. This continuous guidance helps escape local minima by providing directional information that points toward better regions of the search space
Data Source
AI summary
A system plans a radiation therapy treatment of a target volume based on inputs in the form of: a set of candidate beams (B), where each beam defines an arrangement of a therapeutic beam relative to the target volume; a treatment plan (x) for the radiation therapy treatment that uses a subset of the candidate beams (B); an objective function (F) describing a quality of the treatment plan (x); and a feasible region (X) describing requirements on the treatment plan (x) that must be fulfilled. The objective function (F) and/or the feasible region (X) also reflect a first complexity criterion (τ(x)≤{circumflex over (τ)}) limiting a first complexity measure (τ(x)) to be less than or equal to a maximum first complexity ({circumflex over (τ)}). An optimization step is executed repeatedly; whereby, in each iteration, an updated treatment plan (x′) is calculated by optimizing the treatment plan (x) with respect to the objective function (F) and the feasible region (X). Here, if a termination criterion is fulfilled, a set of selected beams (B*) is calculated based on the updated treatment plan (x′). The set of selected beams (B*) is a subset of the set of candidate beams (B). Otherwise, the updated treatment plan (x′) is set to the treatment plan (x); an updated first complexity criterion (τ′(x)≤{circumflex over (τ)}′) is calculated; the updated first complexity criterion (τ′(x)≤{circumflex over (τ)}′) is set to the first complexity criterion (τ(x)≤{circumflex over (τ)}), and another iteration of the optimization step is executed.