Voxel Data Visualization for Medical Imaging
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Solution Overview
Problem
Current medical imaging techniques, such as surface and volume rendering, struggle to efficiently visualize the anatomy of solid organs and correlate quantitative analysis data, leading to inefficient analysis and reduced reproducibility, especially in visualizing continuous perfusion parameters of the left ventricle's wall.
Innovation Solution
A method that involves segmenting voxel data based on a segment model, reformating it to fit a reference shape with inner and outer reference surfaces, and mapping it to a target shape to preserve volumetric distribution, allowing for direct visualization of property values and transmurality information, enabling a more efficient and comprehensive visualization of anatomical and quantitative data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If surface rendering technique is used, then visualization of surfaces and boundaries is optimized, but visualization of tissue within solid organs is inadequate
Solution Approach 1:
The method segments the 3D medical image data into multiple 2D display planes that represent different anatomical layers or structures. By dividing the volumetric data into discrete planar segments, the system can render each plane with appropriate visualization properties while preserving information about internal tissue structures that would otherwise be hidden in surface rendering.
2Productivity
If bull's-eye plot is used for quantitative analysis, then analysis efficiency is improved, but direct reflection of anatomy is lost
Solution Approach 1:
The method transforms the traditional 2D bull's-eye plot into a 3D visualization by introducing display planes with depth information. The 3D coordinate system (x, y, z) is mapped to visual dimensions where the radial distance from center represents anatomical position, the angular position represents segment location, and the vertical dimension or color encoding represents the quantitative parameter values. This dimensional transformation allows simultaneous preservation of anatomical context and quantitative analysis capability.
3Loss of information
If multiple cross-sections and 2D visualizations are inspected, then comprehensive information is obtained, but analysis efficiency decreases
Solution Approach 1:
The method merges multiple 2D cross-sectional views and quantitative parameter maps into a single integrated 3D visualization. By combining anatomical structure display with parameter encoding in one unified display plane or set of planes, the system eliminates the need for users to mentally integrate information from separate images, thereby maintaining comprehensive information while significantly improving analysis efficiency.
4Quantity of substance
If data size increases, then detail information is improved, but visualization complexity increases
Solution Approach 1:
The method applies different visualization properties to different regions or layers within the display planes. By assigning local quality characteristics such as different color mappings, transparency levels, or resolution priorities to specific anatomical regions or parameter ranges, the system can effectively handle large data sets by emphasizing clinically relevant information while reducing the visual complexity of less critical data, thus managing visualization complexity without losing essential detail information.
Data Source
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AI summary
The invention relates to visualization of medical images, and in embodiments to the visualization of the left ventricle of the human heart or other organs. A method of visualizing one or more sets of voxel data is disclosed. The method comprising: providing one or more sets of voxel data, providing and segmenting the voxel data in accordance with a segment model. The segmented voxel data is reformatted to fit a reference shape (20) being defined by at least an inner (22) reference surface and an outer (23) reference surface. The reformatted voxel data is mapped to a target shape being defined by at least a first (29) target surface and a second (200) target surface. The target shape is moreover visualized. The mapping of the reformatted voxel data to a target shape is a mapping of one or more property values from the inner reference surface to the first target surface, and from the outer reference surface to the second target surface, and where a direction (26, 27) extending along the inter- surface distance of the reference shape is maintained in the target shape.