Left Atrium Shape Reconstruction from Sparse Catheter Data
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Solution Overview
Problem
Current electro-anatomical mapping (EAM) systems require extensive catheter maneuvering and prolonged procedure times to produce accurate anatomical maps of the left atrium, especially for pulmonary vein isolation in atrial fibrillation treatment.
Innovation Solution
An encoder-decoder network with a regularization term is used to reconstruct the shape of the left atrium from partial catheter data, allowing for early visualization and reducing mapping time by utilizing a dataset of 3D atria shapes and corresponding catheter trajectories.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dense sampling of the left atrial surface is performed using catheter-based EAM, then anatomical mapping accuracy is improved, but procedure time increases to more than 10 minutes
Solution Approach 1:
The system performs preliminary action by using pre-trained neural networks that have learned from extensive training data to reconstruct the left atrial anatomy. The network can generate accurate anatomical maps from sparse catheter measurements without requiring the catheter to densely sample the entire surface, thus reducing procedure time while maintaining accuracy.
Solution Approach 2:
The system creates a computational copy of the left atrial anatomy through neural network reconstruction. Instead of physically mapping every point on the atrial surface with the catheter, the system uses a digital model trained on training data to replicate and complete the anatomical structure from limited measurements, effectively copying the complex geometry from sparse inputs.
2Measurement precision
If extensive catheter maneuvering is performed to touch a large portion of the boundary, then anatomical accuracy is improved, but device complexity and operation difficulty increase
Solution Approach 1:
The system replaces the mechanical catheter-based surface touching approach with a computational neural network model. Instead of requiring the catheter to physically contact and measure every point on the atrial boundary, the system uses a trained neural network to infer and reconstruct the complete anatomy from sparse measurements, substituting mechanical sampling with computational reconstruction.
Solution Approach 2:
The neural network acts as an intermediary between the sparse catheter measurements and the complete anatomical representation. The network receives limited input data from the catheter and produces a comprehensive 3D anatomical model, mediating the transformation from sparse measurements to complete anatomy without requiring direct extensive catheter contact with the surface.
3Loss of time
If sparse catheter measurements are used, then procedure time is reduced to less than 3 minutes, but anatomical reconstruction accuracy deteriorates
Solution Approach 1:
The system incorporates feedback mechanisms through the neural network training process, where the network continuously refines its reconstruction based on the input measurements and compares against ground truth data from training cases. This feedback loop enables the network to learn optimal reconstruction strategies that maintain high accuracy even from sparse inputs.
Solution Approach 2:
The system changes the approach from direct measurement parameters to computational reconstruction parameters. Instead of relying on the number of physical measurements, the system transforms sparse measurements into a computational representation that the neural network processes to generate accurate anatomy, changing the fundamental parameter of how anatomical information is derived.
Data Source
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AI summary
A method includes, in a processor, receiving example representations of geometrical shapes of a given type of organ. In a training phase, a neural network model is trained using the example representations. In a modeling phase, the trained neural network model is applied to a set of location measurements acquired in an organ of the given type, to produce a three-dimensional model of the organ.