Conic Optimization for Radiation Therapy Fluence Maps
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Solution Overview
Problem
Current fluence map optimization methods in radiation therapy do not guarantee globally optimal solutions and are often restricted to specific problem forms, lacking efficiency and accuracy in achieving the desired trade-off between treatment plan evaluation criteria.
Innovation Solution
The use of conic optimization formulations to model fluence map optimization problems, enabling the application of advanced numerical solvers and ensuring globally optimal solutions by formulating dose-based and biologically-based criteria as convex optimization problems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional optimization methods (sequential quadratic programming, conjugate gradient, L-BFGS, barrier methods) are used to solve fluence map optimization problems, then treatment planning can be performed, but the solutions are not guaranteed to be globally optimal and the methods are restricted to specific problem forms
Solution Approach 1:
The patent transforms the optimization problem by changing the mathematical formulation from conventional methods to conic optimization. This parameter change in the problem representation enables guaranteed global optimality while maintaining versatility across different problem forms through standardized conic programming techniques.
Solution Approach 2:
The conic optimization framework provides a universal approach that can handle various fluence map optimization problems with different evaluation criteria (dose-based, biologically-based) within a single unified mathematical structure, eliminating the need for problem-specific specialized methods.
2Manufacturing precision
If advanced numerical solvers are used to solve large-scale optimization problems, then fluence map optimization can be achieved, but the computational time and complexity increase
Solution Approach 1:
By reformulating the optimization problem as a conic programming problem, the patent enables the use of efficient conic solvers that can handle large-scale problems faster. The conic formulation transforms complex non-convex problems into convex problems that can be solved more efficiently with modern numerical methods.
3Ease of operation
If conventional optimization methods are used, then treatment planning can be performed, but the methods do not guarantee optimal trade-offs between various treatment plan evaluation criteria
Solution Approach 1:
The conic optimization framework provides a unified approach that can handle multiple evaluation criteria (dose-based, biologically-based) simultaneously within a single optimization problem, ensuring optimal trade-offs are achieved through the convex optimization properties of conic programming.
Data Source
AI summary
Systems and methods are provided for determining a treatment plan for a radiation therapy system. The method includes dividing a three-dimensional volume of a patient into a grid of dose voxels, wherein at least a portion of said dose voxels are designated to belong to at least one target or to at least one critical structure. The method also includes modeling an ionizing radiation dose as delivered by a plurality of beamlets each having a beamlet fluence, to create a modeled radiation dose fluence map. The method also includes determining a voxel-based fluence map optimization (FMO) model for the modeled radiation dose a fluence map. The method also includes determining a conic optimization formulation for the FMO model to create a determined conic optimization solution and generating an optimized fluence map by updating the modeled radiation dose fluence map with the determined conic optimal fluence map.