TTFields Transducer Layout Using MRI-CT Segmentation
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Solution Overview
Problem
Existing tumor treating field (TTFields) treatment planning lacks customization and accuracy in transducer layout generation, relying heavily on conventional methods that do not fully utilize medical imaging data for optimized placement.
Innovation Solution
A computer-implemented method and apparatus that utilizes MRI and CT medical images to generate transducer layouts for TTFields, employing tools like auto matching, overlap segmenting, split segmenting, clean-up segmenting, and avoidance area identification to improve placement accuracy and customization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional transducer layout methods are used, then the treatment planning process is simple, but the placement accuracy and customization are insufficient
Solution Approach 1:
The system performs preliminary segmentation of medical images into anatomical structures and tumor regions before transducer placement. This pre-processing creates a detailed anatomical map that guides subsequent transducer positioning, improving placement accuracy without increasing operational complexity during treatment delivery
Solution Approach 2:
The system utilizes multiple imaging parameters (MRI and CT scans with different contrast and resolution) to characterize tissue properties. By analyzing these varying parameters, the system optimizes transducer placement for maximum field strength in tumor regions while minimizing exposure to healthy tissues
2Adaptability or versatility
If medical imaging data is fully utilized for transducer layout generation, then treatment personalization is improved, but processing time and computational resources increase
Solution Approach 1:
The medical images are segmented into distinct anatomical structures and tumor regions using automated image processing algorithms. This segmentation divides the complex imaging data into manageable components that can be quickly analyzed and used for transducer placement optimization
Solution Approach 2:
The system creates simplified 3D models and representations of the patient's anatomy based on detailed medical images. These computational models serve as efficient copies that retain essential geometric and tissue property information while requiring minimal computational resources for transducer layout optimization
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
Enhances the precision and personalization of TTFields delivery by leveraging medical imaging data, resulting in improved treatment planning and transducer array placement.
Implementation Method 1
Tumor treating fields (TTFields) are low intensity alternating electric fields within the intermediate frequency range (for example, 50 kHz to 1 MHz)
Implementation Method 2
TTFields are induced non-invasively into the region of interest by transducers placed on the patient's body and applying AC voltages between the transducers
Data Source
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AI summary
A method for generating a transducer layout for delivering tumor treating fields to a subject includes presenting a user-selectable icon to display a slice through medical images, which can be MRI medical images and CT medical images registered together. The slice includes corresponding slices through the MRI and CT medical images overlaid on each other. The medical images include voxels. The method further includes presenting a user- selectable icon to manually segment the slice through the medical images to obtain a manually segmented slice through the medical images. The method further includes presenting a user-selectable icon to automatically clean up the manually segmented slice to obtain a cleaned up manually segmented slice through the medical images. The method further includes presenting a user-selectable icon to generate transducer layouts for application of tumor treating fields to the subject based on the cleaned up manually segmented slice through the medical images.