Surface Map Encoding for Anatomical Image Analysis
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Solution Overview
Problem
Current radiological diagnosis relies heavily on qualitative and subjective methods, with limited quantitative techniques for analyzing anatomical images, particularly due to challenges in representing homogeneous tissue structures and noisy voxel-based segmentation, which hinders the detection and characterization of abnormalities and disease progression.
Innovation Solution
A non-invasive imaging system that generates a surface map to encode properties of tissue regions using template data, reducing complexity by transforming homogeneous substructures into 2D surface maps and applying Large Deformation Diffeomorphic Mapping (LDDMM) for accurate shape representation and disease diagnosis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If voxel-based analysis is used to analyze anatomical images, then automated image analysis can be performed, but the representation of homogeneous substructures becomes complex and unreliable
Solution Approach 1:
The patent segments the brain into distinct anatomical regions (gray matter, white matter, CSF) and represents each region using a single representative voxel. This segmentation approach reduces the complexity from representing 10-100 million individual voxels to representing each anatomical region with one voxel, while maintaining automated analysis capabilities
Solution Approach 2:
The patent introduces an intermediary step of region-based representation that mediates between the detailed voxel data and the anatomical structures. By using region centroids and representative voxels as intermediaries, the system simplifies the representation of homogeneous substructures while preserving essential anatomical information
2Device complexity
If 1 mm scale anatomical representation is used, then complexity is reduced, but relatively homogeneous substructures lose their detailed characteristics
Solution Approach 1:
The patent applies local quality by assigning different properties to different anatomical regions. Each region (gray matter, white matter, CSF) is represented with its own characteristic intensity value and spatial location, preserving the unique characteristics of each homogeneous substructure while using a coarse 1 mm scale representation
3Productivity
If conventional voxel-based analysis is used, then automated processing is enabled, but registration reliability deteriorates in areas with homogeneous intensity
Solution Approach 1:
The patent extracts the essential registration information from homogeneous areas by using region centroids and representative voxel positions rather than relying on individual voxel intensities. This extraction approach removes the problematic homogeneous intensity information that causes registration failures while preserving the spatial location data needed for reliable alignment
Data Source
AI summary
A non-invasive imaging system, including: a non-invasive imaging scanner; a signal processing unit in communication with the imaging scanner to receive an imaging signal from a subject under observation; and a data storage unit in communication with the signal processing unit, wherein the data storage unit stores template data corresponding to a tissue region of the subject, and wherein the signal processing unit is adapted to generate a surface map to encode a property of a subvolume of the tissue region using the template data.