Cloud-Local Medical Image Data Synchronization
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Solution Overview
Problem
Current cloud-based Picture Archiving and Communications Systems (PACS) face challenges in efficiently managing and exchanging medical images and data across multiple healthcare facilities, particularly when network connections are slow or when a large number of users access medical images simultaneously, leading to increased burden on cloud servers and potential downtime during disconnections.
Innovation Solution
The system offloads burden from the cloud server by determining if raw image data is stored locally or in the cloud repository, instructing facilities to generate medical images locally when possible, and automatically updating and synchronizing data between local and cloud repositories, thereby reducing the need for constant cloud server access and enhancing performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If cloud-based PACS is used for centralized storage and access of medical images, then accessibility and data sharing between healthcare facilities are improved, but server burden increases and system downtime occurs during network disconnections
Solution Approach 1:
The system implements differential storage where frequently accessed medical images are stored locally at each healthcare facility while less frequently accessed images remain in the cloud repository. This allows local access without cloud dependency for common images, eliminating downtime during disconnections, while maintaining cloud connectivity for comprehensive access when available.
Solution Approach 2:
The system divides the medical image repository into multiple segments distributed across cloud and local storage locations. Each healthcare facility maintains a local segment of the repository, enabling independent operation and access to critical images even when disconnected from the central cloud-based PACS system.
2Area of stationary object
If all medical images are stored in the cloud repository, then local storage requirements are reduced, but network bandwidth consumption increases and access speed decreases during simultaneous user access
Solution Approach 1:
The system implements differential storage where frequently accessed medical images are stored locally at each healthcare facility while less frequently accessed images remain in the cloud repository. This allows local access without cloud dependency for common images, eliminating downtime during disconnections, while maintaining cloud connectivity for comprehensive access when available.
Solution Approach 2:
The system pre-loads and caches frequently accessed medical images locally at each healthcare facility before they are needed. This preliminary action ensures that when users need these images, they are immediately available from local storage rather than requiring network retrieval, significantly improving access speed during periods of high simultaneous user access.
3Reliability
If cloud servers process all image generation requests, then consistency across facilities is maintained, but processing time increases and cloud server capacity is exceeded during peak usage
Solution Approach 1:
The system divides image generation processing between cloud and local servers. Complex images requiring intensive processing are generated by the cloud server, while simpler routine images are generated locally by facility servers. This segmentation distributes the processing load, preventing cloud server capacity exhaustion during peak usage while maintaining overall system productivity.
Solution Approach 2:
Each healthcare facility's local server is equipped with image generation capabilities, allowing it to independently generate medical images from stored raw data without always requiring cloud server intervention. This self-service approach reduces processing time for routine images and decreases dependency on cloud server capacity during peak usage periods.
Data Source
AI summary
A method to exchange medical data between a cloud repository on a cloud server and a plurality of local repositories on a plurality of local servers connected to the cloud server. The plurality of local servers comprises a first local server and the plurality of local repositories comprises a first local repository on the first local server. The method includes, causing the first local server to: transmit an image retrieval request to the cloud server, receive the medical image generated by the cloud server or an instruction from the cloud server to generate the medical image locally, and display the generated medical image on a display. The image retrieval request causes the cloud server to determine whether raw image data associated with the image retrieval request is stored in the cloud repository or locally in the first local repository.