Hybrid CPU-GPU Rendering for Medical 3D Volumes
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Solution Overview
Problem
Rendering three-dimensional medical images from voluminous medical volume point cloud data sets is computationally intensive, leading to long processing times, especially when using low-parallelism microprocessors.
Innovation Solution
A hybrid architecture system combining CPU and GPU components, leveraging the parallel processing capabilities of GPUs to enhance performance, by executing volume rendering processes on both central processing units and graphical processing units, allowing for interactive real-time 3D renderings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If microprocessors with low parallelism are used for rendering three-dimensional medical images, then device complexity is reduced, but processing speed and productivity deteriorate significantly
Solution Approach 1:
The patent combines CPU and GPU into a hybrid processing system. The CPU handles control logic and data preparation while the GPU performs parallel rendering computations. This merging allows the system to leverage the strengths of both processor types, achieving high rendering speeds without requiring an entirely complex GPU-based architecture.
Solution Approach 2:
The system makes the rendering pipeline multi-functional by enabling both CPU and GPU to participate in different stages of the rendering process. The CPU manages data transfer, parameter setting, and control flow, while the GPU executes parallel ray-tracing and voxel processing. This universal approach allows flexible task distribution based on computational requirements.
2Measurement precision
If high computational horsepower is used to render three-dimensional medical images, then rendering quality is improved, but processing time increases prohibitively
Solution Approach 1:
The patent segments the rendering computation into multiple independent parallel threads that can be executed simultaneously on the GPU. Each thread handles specific rays or voxels, allowing the system to process large medical volume datasets with high quality while maintaining fast rendering times through parallel execution.
Solution Approach 2:
The system implements continuous rendering by maintaining active GPU processing throughout the rendering pipeline. Data is transferred and processed in continuous streams, with the GPU constantly performing computations while the CPU prepares subsequent data batches, eliminating idle time and maintaining high utilization of computational resources.
3Manufacturing precision
If voluminous medical volume point cloud data sets are processed in detail, then image accuracy is improved, but computational requirements and processing time increase
Solution Approach 1:
The patent implements progressive rendering where the system first renders a lower-resolution preview of the medical volume data, then progressively refines specific regions of interest with higher detail. This allows the system to process voluminous datasets with high accuracy where needed while using fewer computational resources for less critical areas.
Data Source
AI summary
A method, computer program product, and computing system for receiving a three-dimensional image data set for processing on a medical imaging system; processing the three-dimensional image data set on a central processing unit to generate a CPU output; processing the three-dimensional image data set on a graphical processing unit, if the graphical processing unit is available, to calculate a segmentation volume surface normal and generate a GPU output; and combining the CPU output and the GPU output to generate a rendered three-dimensional medical volume that depicts various tissue densities; wherein the GPU output includes a plurality of rendered frames.