Distributed Medical Image Processing via Auction-Based Task Segmentation
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Solution Overview
Problem
Current medical imaging systems face challenges in efficiently distributing and processing large computing tasks for image reconstruction, particularly in scalable and cost-effective ways, especially with the rise in imaging resolution and complex applications, and require decentralized coordination of heterogeneous processing resources in cloud-based environments.
Innovation Solution
A method that divides medical imaging tasks into smaller sub-tasks for distributed execution across multiple remote computing units, using an auction-like negotiation process to select the most suitable resources based on available computing power and compensation, facilitating flexible and scalable processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If cloud-based processing is used to distribute computing tasks, then scalability and cost-effectiveness are improved, but task coordination complexity and bandwidth requirements increase
Solution Approach 1:
The patent divides a medical imaging computing task into multiple sub-tasks that can be independently distributed to different remote computing units. This segmentation allows the system to scale by adding more computing units without proportionally increasing coordination complexity, as each sub-task can be managed separately through the auction mechanism.
2Productivity
If computing tasks are divided into smaller sub-tasks for distributed execution, then processing time is reduced, but task distribution complexity increases
Solution Approach 1:
The patent introduces an auction mechanism as an intermediary layer between the task requester and remote computing units. This mediator automatically handles the complex distribution of sub-tasks by receiving bids from computing units and allocating work based on predefined criteria, thereby reducing the actual coordination complexity despite task fragmentation.
3Adaptability or versatility
If heterogeneous computing resources are utilized in a decentralized environment, then resource utilization efficiency is improved, but systematic coordination requirements increase
Solution Approach 1:
The patent implements a dynamic auction-based coordination system where computing units can bid for sub-tasks based on their current availability, capabilities, and pricing. This dynamic mechanism allows heterogeneous resources to be efficiently utilized while the systematic coordination requirements are managed through automated bidding and allocation processes rather than static scheduling.
Data Source
AI summary
A method of performing a medical imaging process, including: dividing a computing task of a medical imaging system unit into a set of sub-tasks, wherein the computing task is related to a generation of medical image data based on measurement data; selecting at least one sub-task of the set of sub-tasks for external execution; exchanging a sequence of signals including task delegation signals with a plurality of remote computing units, whereby the sequence of signals comprises at least one request signal sent by the medical imaging system unit; outsourcing the at least one selected sub-task to the selected remote computing unit for remotely generating a computing result of the at least one selected sub-task; receiving the generated computing result from the selected remote computing unit; and completing the computing task with the computing result received from the selected remote computing unit.