Beam Geometry Optimization Without Isocenter in Radiation Planning
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Solution Overview
Problem
Current radiation therapy optimization algorithms fail to optimize beam geometry parameters effectively, leading to suboptimal treatment plans due to large search spaces and dependency on fixed or manually entered beam geometry settings, which are not patient-centric and often result in clinically unacceptable outcomes.
Innovation Solution
A two-step process involving a machine learning model to predict initial beam geometry parameters and limit the search space, followed by iterative optimization of all treatment parameters, including beam geometry, to achieve optimal treatment plans without requiring explicit determination of the system isocenter.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional optimization algorithms are used without full beam geometry optimization, then the treatment planning process is simpler and faster, but the treatment plan quality is suboptimal
Solution Approach 1:
The optimization process is divided into two distinct steps: first, machine learning models predict initial beam geometry parameters and define search spaces; second, traditional optimization algorithms optimize within those limited search spaces. This segmentation allows full beam geometry optimization to be achieved while maintaining computational efficiency by avoiding the need to search the entire parameter space.
2Ease of operation
If explicit isocenter determination is required, then the beam geometry optimization is more constrained and easier to control, but the treatment planning process becomes more complex and time-consuming
Solution Approach 1:
The isocenter determination step is extracted and eliminated from the optimization process. Instead of requiring explicit isocenter input, the machine learning models directly predict beam geometry parameters based on patient anatomy and treatment objectives, allowing the optimization to proceed without this intermediate constraint.
Solution Approach 2:
Machine learning models serve as intermediaries between the input patient data and the beam geometry parameters. These models encode the complex relationships between anatomy, treatment goals, and optimal beam geometries, eliminating the need for explicit isocenter determination while maintaining geometric accuracy.
3Manufacturing precision
If beam geometry parameters are not fully optimized, then the treatment planning time is reduced, but the dose distribution quality deteriorates
Solution Approach 1:
Machine learning models perform preliminary predictions of beam geometry parameters and define constrained search spaces before the traditional optimization algorithm runs. This preliminary action guides the optimization process toward promising regions of the parameter space, achieving high-quality dose distributions faster than unconstrained optimization would require.
Data Source
AI summary
Systems and methods are disclosed for optimizing a treatment plan using all degrees of freedom including those related to beam geometry parameters, the optimization including a step for limiting the search space for the beam geometry parameters using a trained machine learning model, and systems and methods are disclosed for obtaining beam geometry parameters for treatment planning that do not require knowledge of the beam delivery device isocenter.