Cardiac Chamber Mesh Refinement via Dual-Region Segmentation
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Solution Overview
Problem
Existing 3D cardiac chamber imaging techniques struggle to accurately map cardiac chamber anatomy due to variations in pulmonary vein configurations between patients, leading to errors in fluoroscopy and incomplete anatomical representation.
Innovation Solution
A method that refines a mapped surface mesh of a cardiac chamber by deforming its central and outer regions using specific segmentation algorithms configured according to shape-constraints and image data, respectively, to generate a more accurate and adaptable anatomical representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a pre-defined mean model is used for segmentation, then the segmentation process is simplified and faster, but it cannot capture the wide variety of PV configurations and anatomical variations between patients
Solution Approach 1:
The method segments the mapped surface mesh into two distinct regions: a central region representing the cardiac chamber and an outer region representing peripheral cardiac structures. This segmentation allows different processing strategies to be applied to each region, enabling both efficiency and adaptability.
Solution Approach 2:
Different segmentation algorithms are applied to different regions of the mesh. The central region uses a first segmentation algorithm with shape constraints, while the outer region uses a second segmentation algorithm that adapts to image data. This local differentiation allows each region to be processed with the most appropriate method for its characteristics.
2Manufacturing precision
If the mapped surface mesh is used directly for MBS adaptation, then all anatomical details are covered, but the arbitrary mesh topology cannot be processed by algorithms requiring known model topology
Solution Approach 1:
A mean mesh model serves as an intermediary between the arbitrary mapped surface mesh and the MBS algorithm. The mean mesh model has a known topology that is compatible with MBS algorithms, while still representing the essential anatomical structure. This intermediary enables the processing of arbitrary meshes by transforming them into a compatible format.
Solution Approach 2:
The method transforms the problem from directly adapting an arbitrary mesh to applying MBS, by introducing a intermediate representation layer. The mapped surface mesh is first processed to create a mean mesh model with standardized topology, which then serves as the basis for MBS adaptation.
3Adaptability or versatility
If multiple mean models are created for common PV patterns, then better coverage of anatomical variations is achieved, but the number of models required becomes very large and the selection process complex
Solution Approach 1:
Instead of using a static set of predefined mean models, the method employs a dynamic approach where the mean mesh model is adaptively deformed based on image data. This allows a single mean model to accommodate multiple PV patterns through deformation, eliminating the need for multiple static models.
Solution Approach 2:
The method changes the parameters of the mean mesh model through deformation to match the specific anatomical variations in the patient's image data. By adjusting the mesh parameters dynamically, the system can adapt to different PV configurations without requiring separate models for each pattern.
Data Source
AI summary
The invention provides a method for refining a mapped surface mesh of a cardiac chamber. The method includes obtaining a mapped surface mesh of the cardiac chamber anatomy, wherein the mapped surface mesh comprises a central region representing a cardiac chamber and an outer region representing a peripheral cardiac structure connected to the cardiac chamber, and wherein the mapped surface mesh comprises a first view of an anatomical landmark within the cardiac chamber, and obtaining image data of a cardiac chamber anatomy of a subject. The central region of the mapped surface mesh is deformed based on a first segmentation algorithm configured according to one or more predetermined shape-constraints and the outer region of the mapped surface mesh is deformed based on a second segmentation algorithm configured according to the image data, thereby generating a deformed outer region. The deformed central region and the deformed outer region are then combined, thereby generating a refined mapped surface mesh.