Healthy-Model TTFields Transducer Placement Without Segmentation
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Solution Overview
Problem
Segmenting medical images to determine transducer layouts for tumor treating fields (TTFields) is computationally demanding and time-consuming, particularly for regions like the torso, requiring significant resources and healthcare provider time.
Innovation Solution
Generate transducer layouts for TTFields by selecting a healthy model representative of the patient without segmenting medical images, using measurements and characteristics to place transducers, and calculating dosages based on tissue conductivity without modifying the model to include abnormal tissue.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If medical images are segmented to determine transducer layouts, then treatment accuracy is improved, but computational processing time and resources increase significantly
Solution Approach 1:
The patent applies segmentation by dividing the complex medical image processing task into distinct stages: initial image acquisition, automated segmentation algorithms that separate tumor regions from healthy tissue, and subsequent transducer layout determination. This structured segmentation enables efficient processing while maintaining treatment accuracy by ensuring precise tumor localization before transducer placement calculation.
Solution Approach 2:
The patent implements preliminary action through pre-processing steps including image normalization, contrast enhancement, and preliminary tumor region identification performed before the main transducer layout calculation. These preliminary actions prepare the data in advance, reducing the computational burden during the actual treatment planning phase and accelerating overall processing time.
2Measurement precision
If medical images are segmented to determine transducer layouts, then treatment accuracy is improved, but healthcare provider time increases
Solution Approach 1:
The patent applies self-service through automated segmentation algorithms and AI-driven transducer layout determination systems that perform complex image analysis and treatment planning tasks without requiring extensive healthcare provider intervention. The system automatically processes medical images, identifies tumor regions, calculates optimal transducer placements, and generates treatment plans, freeing healthcare providers from time-consuming manual analysis while maintaining high treatment accuracy through algorithmic precision.
3Manufacturing precision
If computational resources are increased for image segmentation, then transducer layout accuracy is improved, but processing time increases
Solution Approach 1:
The patent implements periodic action through iterative refinement algorithms that progressively improve transducer layout accuracy through multiple processing passes. The system performs initial coarse segmentation followed by refined segmentation iterations, each pass enhancing precision while managing computational load. This periodic processing approach achieves high transducer layout accuracy without requiring all computational resources to be allocated simultaneously, thus reducing overall processing time.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach reduces computational processing and healthcare provider time while providing effective TTFields treatment, saving resources and improving treatment efficiency.
Implementation Method 1
TTFields are induced non-invasively into a region of interest by transducers placed on the patient's body and applying alternating current (AC) voltages between the transducers
Implementation Method 2
calculating, by the computer, for each of the locations, a dosage of tumor treating fields treatment in the region of interest of the healthy model without modifying the healthy model to include abnormal tissue
Data Source
AI summary
A method for determining transducer locations for delivery of tumor treating fields based on models of healthy subjects, including: receiving a medical image of a subject having an abnormality; receiving a selection of a healthy model from a plurality of healthy models, the healthy model being representative of the subject, the selection based on the medical image of the subject; receiving a selection of locations on the healthy model to place transducers to treat the abnormality of the subject without identifying a location of the abnormality in the healthy model; receiving an indication of a region of interest in the healthy model; and calculating for each of the locations, a dosage of tumor treating fields treatment in the region of interest of the healthy model without modifying the healthy model to include abnormal tissue.