GPU Triangular Mesh Rendering for 3D Medical Image Speed
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Solution Overview
Problem
The existing methods for processing and rendering 3D medical image data, such as DICOM files, are time-consuming due to the large amount of data involved, especially when manipulating images to view them from different angles, even with modern computers.
Innovation Solution
The use of a Graphics Processing Unit (GPU) to generate and manipulate triangular meshes from voxels, where parallel rays are coded based on their origin, and the image is rendered by intersecting these rays with the mesh, significantly reducing processing time by up to 400% through techniques like ray marching, marching cubes, ball pivoting, and Delaunay triangulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional CPU-based voxel processing is used for 3D medical image rendering, then comprehensive data processing is achieved, but processing time becomes excessively long
Solution Approach 1:
The patent replaces traditional CPU-based sequential processing with GPU-based parallel processing architecture. The transfer function evaluation is implemented as a parallel computation across multiple shader cores, where each processing element simultaneously evaluates one or more voxels. This architectural substitution enables thousands of voxels to be processed in parallel, reducing rendering time from minutes to seconds while maintaining comprehensive data processing capability.
Solution Approach 2:
The patent divides the 3D volume data into multiple voxel groups that can be processed independently and simultaneously. The transfer function evaluation is segmented into discrete computational units that map to individual GPU processing elements. This segmentation allows the system to process large volumes of medical image data by distributing computation across many parallel threads, achieving both complete data processing and high rendering speed.
2Manufacturing precision
If detailed voxel data is processed for accurate 3D representation, then image accuracy is maintained, but processing complexity increases
Solution Approach 1:
The patent changes the computational parameters by evaluating the transfer function directly in GPU shader code rather than through traditional CPU-based algorithms. By implementing the transfer function as a programmable shader that operates on voxel data in parallel, the system maintains accurate 3D representation while simplifying the processing architecture. The shader-based approach allows complex transfer functions to be evaluated efficiently through hardware-optimized parallel computation.
Data Source
AI summary
Image processing is carried out by accepting an array of voxels that include data representing a physical property of a 3-dimensional object, segmenting the array of voxels into a plurality of regional subarrays of voxels that respectively satisfy predetermined criteria, transforming the subarrays into respective triangular meshes, the meshes having triangles that surround the subarrays and intercept the outer voxels of the subarrays, and rendering the triangular meshes on a display