Electroanatomical Mapping Lesion Interconnection Optimization
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Solution Overview
Problem
Current systems for presenting information on lesion formation during ablation procedures struggle to identify optimized groupings and interconnections between lesion segments, which is crucial for effectively treating cardiac arrhythmias.
Innovation Solution
An electroanatomical mapping system that receives lesion markers representing ablation lesion segments, defines disjoint subsets, and computes optimized interconnections and interlesion distances, displaying these on a graphical representation of the tissue.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If lesion markers are displayed on a lesion formation map, then clinicians can visualize lesion segments, but it becomes difficult to interpret interlesion distances and determine optimized groupings between markers
Solution Approach 1:
The system segments lesion markers into multiple disjoint subsets based on spatial proximity and clinical relevance. Each subset represents a distinct lesion group with optimized interconnections, allowing clinicians to interpret distances within each subset without being overwhelmed by the complete set of markers. This segmentation transforms the complex interpretation task into manageable subset analyses.
Solution Approach 2:
The system introduces an intermediary processing layer that automatically computes optimized interconnections and interlesion distances between markers. This intermediary computation layer acts as a mediator between raw marker data and clinician interpretation, providing pre-processed distance information and grouping recommendations that enhance visualization without requiring manual distance calculations.
2Measurement precision
If clinicians manually analyze distances between all lesion markers, then they can identify continuous lesions, but this process is time-consuming and complex
Solution Approach 1:
The system performs preliminary computation of interlesion distances and optimized groupings before clinician review. By pre-calculating which markers form continuous lesions and which represent separate lesions based on distance thresholds and spatial relationships, the system eliminates the need for clinicians to manually analyze all marker pairs, significantly reducing interpretation time while maintaining measurement precision.
Solution Approach 2:
The system enables self-service automated analysis of lesion marker relationships. The electroanatomical mapping system automatically computes interlesion distances, identifies continuous lesions, and presents optimized groupings without requiring manual clinician intervention for distance measurements. This self-service capability maintains precise measurement while dramatically reducing the time clinicians must spend on routine distance analysis.
3Loss of information
If the system displays all lesion markers without grouping, then complete information is provided, but optimized interconnections and distances are not apparent
Solution Approach 1:
The system segments the complete set of lesion markers into multiple disjoint subsets, where each subset contains markers that form continuous or near-continuous lesions. This segmentation organizes the complete information into structured groups with defined interconnections, maintaining information completeness while reducing interpretation complexity by presenting markers in meaningful clusters rather than as an unstructured set.
Solution Approach 2:
The system adds a organizational dimension to the display by creating hierarchical grouping structures. Beyond the spatial coordinates of individual markers, the system introduces a grouping dimension that organizes markers into subsets based on their interconnections and distances. This additional organizational dimension allows complete lesion information to be presented in a structured format that reduces interpretation complexity without losing any marker data.
Data Source
AI summary
An electroanatomical mapping system identifies optimized interconnections between ablation lesion segments. The system receives lesion markers, each representing an ablation lesion segment, and defines one or more disjoint subsets thereof. For each disjoint subset, the system defines optimized interconnections between lesion markers and outputs a graphical representation of the disjoint sets and the optimized interconnections. The disjoint subsets may be defined as minimum cost spanning trees. The optimized interconnections can include the edges of the minimum cost spanning tree as well as additional cycle-closing edges that are not edges of the minimum cost spanning tree, where the cycle-closing edges are leaf edges that satisfy one or more cycle-closing criteria.