3D Heart Mapping Voxel Density Marking
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Solution Overview
Problem
Current 3-dimensional mapping systems for heart electrical activity are limited by coarse-grained anatomical meshes, leading to unreliable interpolation in regions with few measured points, making it difficult to assess interpolation quality due to visual overload from other map information.
Innovation Solution
A method that constructs a 3-dimensional model of the heart using voxels, interpolates physiologic parameter values, determines regional densities by counting measured points within predefined distances, and modifies graphical characteristics to distinguish sparse and dense zones, thereby assessing interpolation quality without displaying measured points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If 3-dimensional mapping systems use coarse-grained anatomical meshes, then the system complexity is reduced and manufacturing is easier, but the interpolation reliability deteriorates in regions with few measured points
Solution Approach 1:
The patent divides the 3-dimensional map into multiple regions based on measured point density (sparse regions vs. dense regions). This segmentation allows different interpolation quality assessments for different areas, resolving the contradiction by maintaining coarse-grained mesh simplicity while identifying specific regions where interpolation reliability may be compromised.
Solution Approach 2:
The patent applies different visual characteristics to different regions of the map based on their measured point density. Sparse regions receive distinct visual marking to indicate lower interpolation reliability, while dense regions maintain normal visualization. This local differentiation resolves the contradiction by preserving overall system simplicity while enhancing reliability assessment in critical areas.
2Reliability
If measured points are displayed on the map to assess interpolation quality, then the reliability assessment is improved, but visual overload occurs due to other map information
Solution Approach 1:
The patent uses color or visual characteristic changes to mark sparse regions on the map. Instead of displaying individual measured points which would cause visual overload, the system changes the visual appearance of regions (e.g., shading, color intensity) to indicate areas with low measured point density. This resolves the contradiction by providing reliable interpolation quality assessment through intuitive visual cues without adding visual clutter.
Solution Approach 2:
The patent extracts the essential information needed for reliability assessment (measured point density) and represents it separately from the detailed map information. By removing individual point displays and replacing them with regional density indicators, the system maintains reliability assessment capability while eliminating visual overload from catheter icons and other map elements.
3Manufacturing precision
If the number of measured points is increased to improve anatomical mesh resolution, then the manufacturing precision is improved, but the device complexity and data processing requirements increase
Solution Approach 1:
The patent applies partial action by focusing computational and visual resources only on regions where it is necessary. Instead of uniformly increasing mesh resolution across the entire heart map, the system identifies sparse regions and applies enhanced visual marking only there. This resolves the contradiction by achieving effective precision improvement in critical areas without the excessive complexity of uniformly high-resolution mesh generation and processing.
Data Source
AI summary
Values of a physiologic parameter at respective measured points in a heart are obtained. A 3-dimensional model of the heart is constructed, which includes first spatial elements that include the measured points and second spatial elements that do not include the measured points. The values of the parameter in the second spatial elements are interpolated and regional densities of the measured points in the model determined. The values of the parameter at the first spatial elements and the second spatial elements are displayed on a functional map of the heart, and a graphical characteristic of the map is modified responsively to the regional densities.