Random Forest Electric Field Approximation for Fast TTFields Planning
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Solution Overview
Problem
Current methods for estimating TTFields intensity distributions are time-consuming and require hours to compute, limiting the evaluation of transducer array locations and potentially resulting in suboptimal optimization for tumor treating fields therapy.
Innovation Solution
A method using machine learning, specifically random forest regression, to quickly estimate electric field strength distribution based on patient imaging data, enabling fast determination of optimal transducer array positions for maximizing TTFields intensity in tumor regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If finite element methods are used to estimate TTFields intensity distributions, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent pre-computes electric field distributions for multiple transducer array positions using finite element methods before treatment planning. These pre-computed results are stored and used during optimization, eliminating the need for time-consuming real-time calculations while maintaining accuracy.
Solution Approach 2:
The patent creates simplified computational models that replicate the complex finite element method results. By using pre-computed data and simplified models, the system can quickly evaluate multiple transducer array configurations without repeating full finite element simulations, thus reducing computation time while preserving measurement precision.
2Manufacturing precision
If comprehensive optimization of transducer array locations is performed, then manufacturing precision is improved, but productivity decreases
Solution Approach 1:
The patent performs comprehensive optimization calculations in advance by pre-computing electric field distributions for numerous transducer array positions. During actual treatment planning, these pre-computed results enable rapid evaluation and selection of optimal configurations, achieving both high optimization precision and improved productivity.
Solution Approach 2:
The patent implements an adaptive optimization approach that dynamically adjusts the level of computational detail based on treatment requirements. For routine cases, simplified models provide quick results, while complex cases can utilize more comprehensive finite element analysis, balancing optimization quality with planning efficiency.
Data Source
AI summary
Methods, systems, and apparatuses are described for fast approximation of electric field distribution.