Parallel Image Reconstruction via Data Segmentation
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Solution Overview
Problem
Current diagnostic medical systems face challenges in improving image reconstruction performance, scalability, and flexibility due to complex and computationally intensive algorithms, requiring additional processing units that increase system complexity and reduce flexibility.
Innovation Solution
The method involves decomposing raw image data into subsets and processing them in parallel using multiple image generation processors, allowing for scalable and efficient image reconstruction without the need for extensive system reconfiguration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If specialized processing units are added to improve processing capability, then processing performance is improved, but system complexity increases
Solution Approach 1:
The raw image data is divided into multiple subsets, with each subset assigned to a separate processing unit for parallel processing. This segmentation allows the system to process multiple portions of data simultaneously, improving overall processing performance while maintaining a standardized architecture that does not significantly increase system complexity
Solution Approach 2:
Multiple processing units are designed with identical or similar functional capabilities, where each unit can independently process different subsets of raw image data. This universal design allows the system to scale by adding more of the same processing units rather than integrating increasingly complex specialized components
2Productivity
If new processing units are integrated to increase processing capability, then productivity improves, but adaptability decreases
Solution Approach 1:
By segmenting the data processing task into independent subsets that can be handled by identical processing units, the system gains flexibility in configuring and scaling processing capacity without requiring complex reintegration of different specialized components
Solution Approach 2:
The system allows dynamic allocation of data subsets to processing units based on available resources and processing requirements. Processing units can be added or removed from the system without requiring reconfiguration of the overall system architecture, maintaining adaptability while improving processing capability
3Speed
If processing units operate in parallel to reduce reconstruction time, then speed improves, but device complexity increases
Solution Approach 1:
The reconstruction process is segmented into independent parallel operations where each processing unit works on a separate data subset simultaneously. This segmentation enables speed improvement through parallel processing while keeping each individual processing unit relatively simple and the overall system configuration manageable
Solution Approach 2:
Multiple processing units are merged into a coordinated system where each unit performs the same processing operations on different data subsets. The results from all processing units are then combined to form the complete reconstructed image, achieving speed improvement through parallel execution without requiring each unit to be overly complex
Data Source
AI summary
A method and apparatus for processing raw image data to create processed images. Raw image data is acquired. The raw image data is decomposed by a data decomposer into N subsets of raw image data. The number N is based on a number of available image generation processors. The N subsets of raw image data are processed by at least one image generation processor to create processed image data. If more than one image generation processor is available, the image generation processors perform image processing on the raw image data in parallel with respect to each other.