Heart Tissue Conductivity Analysis via Ray-Based Spatial Sampling
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Solution Overview
Problem
Current methods lack an automated way to accurately identify the structure of fibrosis in heart muscle tissue using 3D medical images, which is crucial for diagnosing arrhythmias and planning treatments such as implantable defibrillator placement or catheter ablation procedures.
Innovation Solution
A computer-implemented method calculates the local spatial structure of conducting properties in heart muscle tissue by analyzing spatial distribution data from medical images or catheter measurements, using ray-based sampling and classification techniques to identify re-entrant conducting channels associated with arrhythmias.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated methods are used to identify fibrosis structure, then productivity is improved, but measurement precision deteriorates
Solution Approach 1:
The method segments the fibrosis identification process into distinct computational steps: ray casting from grid points, sampling along ray paths, classification of tissue types encountered, and calculation of channelicity values. This segmentation allows automated processing while maintaining precision through systematic analysis of tissue architecture.
Solution Approach 2:
The invention transforms the identification problem from direct image analysis to a parameter-based calculation approach. By computing channelicity values (a quantitative parameter representing conducting channel characteristics) from spatial distribution data, the method enables automated identification while preserving measurement precision through mathematical transformation of the tissue structure data.
2Measurement precision
If detailed spatial distribution analysis is performed, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The method analyzes spatial distribution by introducing a directional dimension through ray casting. Instead of analyzing only the 3D spatial coordinates, rays are cast in multiple directions from each grid point, sampling tissue types along each ray path. This dimensional approach to spatial analysis enables detailed characterization of fibrosis structure while using systematic computational geometry to manage processing complexity.
Solution Approach 2:
The computational method is self-sufficient in that it processes the spatial distribution data through autonomous algorithms without requiring external intervention. The ray casting, sampling, and channelicity calculation procedures are fully automated computational processes that analyze the input data and generate results independently, reducing the need for complex external processing systems.
Data Source
AI summary
A method for calculating values indicative for the local spatial structure of conducting properties of heart muscle tissue. The method uses a continuous ROI where there may exist a CC, each point of the ROI being associated with a value of a given spatial distribution. The method for each one of the points: spans rays within the ROI in multiple 3-D directions using coordinates of a reference coordinate system of each point, providing a structure with a plurality of rays with a given geometry; defines a sequence of sampling points on each ray of said structure; maps the value of the spatial distribution on each defined sampling point of each ray of the structure; classifies each ray of the structure by assigning a single value, providing an associated ray value; and reduces each structure to a single point value in view of said geometry and their associated ray value.