Predictive Electric Field Models for TTFields Array Placement
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Solution Overview
Problem
Current methods for optimizing the placement of transducer arrays for Tumor Treating Fields (TTFields) therapy are time-consuming and suboptimal, relying on finite element methods that require hours to compute electric field distributions, limiting the evaluation of array locations and potentially compromising treatment efficacy.
Innovation Solution
A predictive model is trained using image data from patients to estimate electric field strength distributions, allowing for fast estimation of optimal transducer array positions through machine learning techniques such as random forest regression, thereby optimizing TTFields therapy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If finite element methods are used to estimate electric field distribution, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent pre-computes electric field distributions for multiple transducer array configurations using finite element methods before treatment planning. These pre-computed results are stored and used to rapidly estimate fields 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. These surrogate models capture the essential physics of electric field distribution but compute much faster, enabling practical optimization while preserving measurement precision
2Productivity
If more transducer array locations are evaluated, then productivity is improved, but loss of time increases
Solution Approach 1:
The patent performs preliminary evaluation and ranking of multiple transducer array locations using pre-computed electric field data. This allows clinicians to assess many more configurations than would be feasible with real-time computation, improving productivity without proportionally increasing optimization time
Solution Approach 2:
The patent implements an adaptive optimization approach that dynamically adjusts the evaluation process based on preliminary results. High-priority configurations are evaluated in detail while lower-priority ones are assessed more quickly, optimizing the balance between productivity and time investment
Data Source
AI summary
Methods, systems, and apparatuses are described for fast approximation of electric field distribution.