3D Cardiac Model Reconstruction from Sparse Point Clouds
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Solution Overview
Problem
Current methods for reconstructing 3D models of the heart from sparse data during medical catheterization are prone to errors and require manual intervention, especially when dealing with low-density point clouds, which is time-consuming and inefficient.
Innovation Solution
An automated method for 3D cardiac reconstruction using a point cloud, where filters are applied iteratively to improve resolution, with subsets of filters chosen randomly or based on search strategies, and the filtered volumes are segmented and combined to produce a feature-rich composite volume.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high resolution is used for 3D reconstruction, then feature richness is improved, but errors increase when applied to low density point clouds
Solution Approach 1:
The patent applies dynamic resolution adjustment where the reconstruction resolution is not fixed but adapts based on the density and quality of the point cloud data. The system automatically adjusts resolution parameters during the reconstruction process to optimize between feature richness and accuracy, preventing errors in low-density regions while maintaining detail where data is sufficient.
Solution Approach 2:
The patent changes reconstruction parameters dynamically during processing. By modifying resolution, smoothing factors, and filtering parameters based on the characteristics of the input point cloud, the system achieves high resolution where data supports it while avoiding errors in low-density areas. This parameter adaptation resolves the contradiction between measurement precision and reliability.
2Measurement precision
If manual resolution setting is used for separate regions, then reconstruction quality is improved, but processing time increases
Solution Approach 1:
The patent implements self-service through automatic resolution adjustment. The system analyzes the point cloud data characteristics and autonomously determines optimal resolution parameters for different regions without requiring manual intervention. This automatic quality adjustment maintains high reconstruction quality while eliminating the time-consuming manual process, resolving the contradiction between measurement precision and time loss.
Solution Approach 2:
The patent employs feedback mechanisms where the reconstruction system continuously monitors the quality of generated models and adjusts resolution parameters accordingly. By using quality metrics to feed back into the resolution setting process, the system automatically optimizes reconstruction quality across different regions without manual input, thereby reducing processing time while maintaining precision.
3Productivity
If automated approach is used for resolution adjustment, then processing time is reduced, but resolution control flexibility is worsened
Solution Approach 1:
The patent applies dynamic resolution adjustment where the system automatically adapts resolution parameters based on the specific characteristics of each point cloud dataset. The dynamic nature of the resolution control allows the automated system to flexibly adjust to different anatomical structures and data densities, maintaining both processing efficiency and adaptability to various reconstruction scenarios.
Data Source
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AI summary
3-dimensional cardiac reconstruction is carried out by catheterizing a heart using a probe with a mapping electrode, and acquiring electrical data from respective locations in regions of interest in the heart, representing the locations of the electrical data as a point cloud, reconstructing a model of the heart from the point cloud, applying a set of filters to the model to produce a filtered volume, segmenting the filtered volume to define components of the heart, and reporting the segmented filtered volume.