Medical Image Data Workflow Distribution
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Solution Overview
Problem
Current medical image data management systems face bottlenecks due to the large volume of data generated, leading to delays and potential data loss during transmission and archiving, especially when using centralized processing approaches that overload networks and capacity limitations.
Innovation Solution
A method for distributing medical image data across multiple processing nodes by determining a workflow, splitting data into subsets based on required functionalities, and forwarding these subsets to relevant nodes for processing, thereby reducing data transmission volume and optimizing workflow matching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all image data are transmitted to further processing nodes, then complete data availability is achieved, but network load and data transmission volume increase significantly
Solution Approach 1:
The patent segments the complete image data into multiple data subsets (e.g., projection data subsets, reconstructed image subsets) and transmits only the necessary subsets to specific processing nodes based on their functional requirements. This segmentation allows selective data transmission, reducing overall network load while ensuring each node receives the precise data it needs for its processing task.
2Speed
If image data are transmitted during peak network load times, then data transmission occurs promptly, but network bottlenecks and delays increase
Solution Approach 1:
The system performs preliminary actions by pre-transmitting image data subsets to processing nodes during periods of low network load, before they are actually needed for processing. This advance preparation ensures that data are already available at the destination nodes when required, eliminating delays caused by peak-time transmission bottlenecks and ensuring continuous processing capability.
3Quantity of substance
If compressed image data are transmitted at reduced resolution, then data transmission volume is reduced, but image quality and diagnostic accuracy decrease
Solution Approach 1:
The patent applies local quality by transmitting different data subsets with different quality characteristics according to the specific processing needs of each node. For example, projection data may be transmitted at full precision to reconstruction nodes, while preview or monitoring nodes receive lower-resolution subsets. This ensures that each node receives data with the appropriate quality level for its function, maintaining diagnostic accuracy where needed while reducing overall transmission volume.
4Power
If a central image processing server is used, then processing capability is increased, but network capacity requirements and single point of failure risks increase
Solution Approach 1:
The patent segments the centralized processing function into multiple distributed processing nodes, each capable of executing specific processing tasks on received data subsets. This distribution eliminates the single point of failure inherent in centralized servers, as processing can continue on remaining nodes if one fails. It also reduces network capacity requirements by allowing parallel processing and localized data handling, while maintaining high processing capability through the collective power of multiple nodes.
Data Source
AI summary
A method, an arrangement and a product are disclosed, where a multiplicity of nodes are provided which are designed for processing medical image data. Following determination of the specific workflow for processing the image data in at least one embodiment, local, relevant nodes are determined which have the functionality to execute the particular workflow. The image data are then split into image data subsets, on the basis of the workflow, and are forwarded in dedicated fashion to the relevant nodes for the purpose of processing.