Electroanatomical Map Update via Densified Sample Points
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Solution Overview
Problem
Existing methods for updating electroanatomical maps after an ablation procedure are either computationally intensive or lack accuracy, as they struggle to efficiently project new measurements onto a three-dimensional mesh representing the heart's surface, especially when dealing with large tiles and sparse vertex spacing.
Innovation Solution
A system that samples the mesh uniformly to create a denser set of sample points, using a space-partitioning data structure like a k-d tree to quickly find the closest sample point, followed by a refined projection to accurately update the mesh by identifying the closest point within a limited portion of the mesh, thereby reducing computational load and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing methods project new measurements directly onto mesh vertices, then the updating process is computationally intensive, but the accuracy is improved
Solution Approach 1:
The patent applies preliminary action by first performing a rough projection of measurement locations onto the mesh to identify candidate vertices, then refining this projection only in the vicinity of these candidates. This preliminary step narrows down the search space before the accurate but computationally intensive projection is performed, thus resolving the contradiction between accuracy and efficiency.
Solution Approach 2:
The patent segments the projection process into two distinct stages: a rough projection phase that quickly identifies candidate regions, and a refined projection phase that achieves high accuracy only in those localized regions. This segmentation allows the system to maintain overall computational efficiency while achieving local accuracy where needed.
2Device complexity
If the mesh uses large tiles with sparse vertex spacing to reduce data complexity, then computational load decreases, but projection accuracy deteriorates
Solution Approach 1:
The rough projection step serves as a preliminary action that quickly identifies the relevant candidate vertices even in sparse meshes. This preliminary identification ensures that the subsequent refined projection focuses computational resources on the correct locations, maintaining accuracy despite the sparse vertex spacing.
Solution Approach 2:
The patent introduces an intermediary rough projection step that acts as a mediator between the sparse mesh structure and the required high-accuracy projection. This intermediary step bridges the gap by providing initial location estimates that guide the final accurate projection, allowing sparse meshes to maintain their computational advantages while achieving necessary accuracy.
Data Source
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AI summary
Described embodiments include a system for updating a mesh, which includes a plurality of vertices, representing a surface of a heart. The system includes an electrical interface and a processor. The processor is configured to define a plurality of sample points on the mesh, such that a density of the sample points is greater than a density of the vertices, to receive, via the electrical interface, a plurality of signals from a catheter, the signals indicating an electrical property of each of a plurality of locations on the surface, and to update the mesh in accordance with the electrical property, by, for each of the locations, identifying a closest sample point to the location, subsequently, identifying, in a portion of the mesh in which the closest sample point is located, a closest point to the location, and, subsequently, associating the closest point with the electrical property of the location.