Direct Volume Rendering of 4D Deformable Images
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Solution Overview
Problem
Existing methods for volume rendering of 4D deformable volume images are time-consuming, memory-intensive, and difficult to implement, especially in medical applications, often resulting in rough transitions between stages of motion.
Innovation Solution
A system comprising a data acquisition subsystem, segmentation subsystem, deformation matrix generator, mesh patch generator, texture subsystem, evaluator, and compositor that acquires and processes 3D image datasets to generate deformation matrices, deformed mesh patches, and textures, allowing for direct volume rendering of objects in motion without pre-processing, with dynamic sampling and blending for smooth transitions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional volume rendering methods are used for 4D deformable volume images, then the rendering can be performed, but the processing time is very time-consuming and memory requirements are high
Solution Approach 1:
The patent applies preliminary action by pre-computing deformation matrices from the reference volume to all deformed volumes at different time points. These deformation matrices are stored and reused during rendering, eliminating the need to recompute them for each frame. This pre-processing step significantly reduces the processing time during actual rendering operations while maintaining high rendering quality for 4D deformable volume images.
2Productivity
If traditional volume rendering methods are used for 4D deformable volume images, then the rendering can be performed, but extra memory is required
Solution Approach 1:
The patent extracts and separates the deformation computation from the rendering process. By extracting the deformation matrices as independent pre-computed data structures, the system avoids storing and processing entire 4D volume datasets during rendering. This extraction allows the renderer to work with compact deformation representations rather than large volumetric data, significantly reducing memory requirements while maintaining rendering efficiency.
3Shape
If methods like marching cube algorithm are used to convert volume rendering to surface rendering, then surface rendering can be achieved, but smooth transitions between stages are hard to obtain
Solution Approach 1:
The patent applies dynamics by using dynamic mesh deformation based on the pre-computed deformation matrices. Instead of static surface extraction methods like marching cubes that produce discrete surfaces, the system dynamically deforms a reference mesh using the deformation matrices to match the deformed volumes at each time point. This dynamic approach ensures smooth transitions between stages because the mesh continuously adapts to the changing volume shapes through the deformation fields, maintaining geometric consistency across all time points.
4Adaptability or versatility
If multiple volume switching method is used where all intermediate volumes are generated before rendering, then complete 4D data can be visualized, but the method is very time-consuming
Solution Approach 1:
The patent substitutes the mechanical approach of generating and storing all intermediate 4D volume data with a computational field-based approach. Instead of physically creating and manipulating discrete volume datasets for each time point, the system uses deformation matrices that represent the transformation fields. During rendering, these fields are applied on-demand to the reference volume, replacing the need to pre-generate and store multiple complete volume datasets. This substitution dramatically improves rendering speed while maintaining full 4D visualization capability.
Data Source
AI summary
A method and system are presented for direct volume rendering of a deformable volume dataset that represent an object in motion. An original 3D image dataset of the object is acquired at an initial stage of the motion, and segmented. Deformed 3D image datasets of the object are acquired at subsequent stages of the motion. Deformation matrices are computed between the segmented original 3D image dataset and the deformed 3D image datasets. A plurality of deformed mesh patches are generated based on the deformation matrices. 2D textures are generated by dynamically sampling the segmented 3D image dataset, and applied to the deformed mesh patches. The textured deformed mesh patches are evaluated, blended, and composited to generate one or more volume-rendered images of the object in motion.