Cardiac 3D Model Generation with Uncertainty-Guided Refinement
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Solution Overview
Problem
Existing methods for generating 3D models of patient's organs, particularly the left atrium, pulmonary veins, and left atrial appendage, lack optimization in machine learning and do not account for model certainty, which is crucial for precise atrial fibrillation ablation procedures.
Innovation Solution
A processor-based system that utilizes a trained neural network to generate a 3D model of a patient's organ by determining shape parameters from images, calculating uncertainty levels, and refining the model based on high-uncertainty regions, thereby enhancing precision and safety in ablation procedures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If machine learning models are used to generate heart models, then automation and efficiency are improved, but model certainty and precision are insufficient
Solution Approach 1:
The system implements feedback by calculating uncertainty maps from the neural network output and using this information to iteratively refine the 3D heart model. The uncertainty feedback guides selective refinement of specific regions, improving model reliability while maintaining automation.
Solution Approach 2:
The system performs preliminary uncertainty analysis immediately after initial model generation, identifying regions that require further refinement before final output. This preliminary assessment ensures that critical areas are addressed before clinical use.
2Device complexity
If traditional 3D reconstruction methods are used, then model generation is simpler, but precision and accuracy of cardiac structures are insufficient
Solution Approach 1:
The system segments the heart model into multiple regions based on uncertainty levels, applying different refinement strategies to different segments. This allows precise reconstruction of critical structures while maintaining simplicity in well-defined areas.
Solution Approach 2:
The system applies local quality enhancement by refining only specific regions with high uncertainty rather than uniformly processing the entire model. This concentrates computational resources on areas requiring higher precision while maintaining overall simplicity.
3Manufacturing precision
If comprehensive imaging data is collected to improve model accuracy, then reconstruction precision is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs partial action by collecting and processing only the necessary imaging data required to achieve sufficient precision, rather than comprehensively processing all available data. The uncertainty-guided approach identifies minimum required data processing.
Solution Approach 2:
The system performs preliminary uncertainty analysis to identify which regions require detailed imaging data and which can use standard processing, enabling selective data collection and processing that reduces overall time while maintaining precision where needed.
Data Source
AI summary
An apparatus of generating a three-dimensional (3D) model of a patient's organ, comprising a processor and a memory containing instructions configuring the processor to receive a first set of images of a patient's organ, determine a first set of shape parameters as a function of the first set of images, generate a first 3D model of the patient's organ as a function of the first set of shape parameters, calculate a level of uncertainty at each location on the first 3D model of the patient's organ, receive a second set of images of the patient's organ corresponding to a high uncertainty region of the first 3D model, and determine a second set of shape parameters as a function of the first set of images and the second set of images, and generate a second 3D model of the patient's organ as a function of the second set of shape parameters.