Cardiac Surface Modeling From Sparse Point Clouds Using SDF
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Solution Overview
Problem
Existing methods for generating surface models of cardiac structures from point clouds are time-consuming and prone to inaccuracies due to sparse or incomplete data sampling, leading to gaps and incomplete representations of cardiac anatomy.
Innovation Solution
A computer-implemented method using a machine learning model, such as a neural network, to generate a final signed distance field (SDF) from sparse point cloud data, which is then used to construct an accurate faceted surface model of the cardiac structure, incorporating techniques like gradient descent and affine transformation to compensate for orientation and data completeness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional surface model generation methods (convex hulls, alpha shapes, voxel-based methods) are used, then the process is systematic and structured, but the generation time is excessively long (upwards of 20 minutes) and accuracy deteriorates when data is sparse or incomplete
Solution Approach 1:
The patent replaces traditional geometric algorithms (convex hulls, alpha shapes, voxel-based methods) with a machine learning model that directly predicts surface models from point cloud data. This substitution of the computational mechanism dramatically reduces processing time from over 20 minutes to seconds while maintaining or improving accuracy, especially for sparse data conditions.
Solution Approach 2:
The patent transforms the input point cloud data into a standardized format with specific parameters (normalization, feature scaling) that optimize the machine learning model's performance. By changing how the data is represented and processed through learned parameters in the neural network, the system achieves faster convergence and higher accuracy compared to traditional methods.
2Manufacturing precision
If dense sampling is performed to ensure complete cardiac chamber coverage, then the surface model accuracy is improved, but the data collection time and procedural complexity increase significantly
Solution Approach 1:
The machine learning model is pre-trained on extensive datasets of complete cardiac geometries, enabling it to predict missing regions and infer complete surface models from sparse input data. This preliminary preparation of the model allows it to compensate for incomplete sampling without requiring the operator to perform time-consuming dense sampling procedures.
Solution Approach 2:
The system creates a complete surface model by learning from training data that represents complete cardiac geometries. The model effectively copies and reconstructs missing anatomical features based on patterns learned from comprehensive training examples, allowing accurate representation without requiring complete input data.
3Productivity
If sparse data sampling is used to reduce procedural time, then the data collection process is faster, but the resulting surface model contains gaps and inaccuracies corresponding to voids in the data
Solution Approach 1:
The machine learning model acts as an intermediary between the sparse input point cloud and the complete surface model output. It processes the incomplete data through learned representations and generates a complete, accurate surface model by inferring missing regions, effectively mediating the transition from sparse to complete geometry.
Solution Approach 2:
The patent replaces traditional geometric reconstruction methods with a machine learning-based approach that can infer complete surfaces from sparse data. This substitution enables the system to handle incomplete input data effectively, generating accurate surface models without requiring dense sampling, thus maintaining both speed and accuracy.
Data Source
AI summary
A system for generating a surface model of a cardiac structure includes a display device, a medical device, and a model reconstruction system. The medical device includes one or more sensors configured to generate sensor output used to generate location data for points disposed on a surface of the cardiac structure. The model reconstruction system configured to: (a) process the sensor output to generate a point cloud that represents measured locations on the surface of the cardiac structure and/or within the cardiac structure, (b) extract a surface point cloud from the point cloud, (d) generate a final signed distance field (SDF) representing a shape of a surface of the cardiac structure via a machine learning model, (e) construct a surface model representing the shape of the surface of the cardiac structure based on the final SDF, and output or display the surface model to a user via the display device.