Managed Image Reconstruction Using Distributed Computing
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Solution Overview
Problem
Current medical image reconstruction algorithms are computationally intensive, leading to high demands on computer resources and inefficient use of available capacity, particularly in medical imaging devices, where computers are often underutilized due to varying data volumes and processing requirements.
Innovation Solution
A method that utilizes a computing management computer to efficiently distribute and process medical image data across a group of computers, ensuring a guaranteed minimum performance by checking and managing free capacities, allowing for the efficient use of shared computing resources and optimizing reconstruction times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If new reconstruction algorithms are used to reduce data volume, then image quality is improved and acquisition time is reduced, but computational intensity increases significantly
Solution Approach 1:
The patent segments the computational workload by dividing the computer group into multiple independent computing units (first computer, second computer, third computer). Each computer processes specific aspects of the reconstruction algorithm independently, allowing parallel computation and distributing the computational intensity across multiple machines while maintaining the ability to handle reduced data volumes efficiently.
2Reliability
If computer capacity is dimensioned to handle maximum data volume, then reconstruction performance is ensured, but resource utilization is low when data volume is small
Solution Approach 1:
The system dynamically allocates computational tasks to different computers in the group based on the actual data volume and processing requirements. The computing management computer monitors and adjusts the distribution of reconstruction workloads in real-time, ensuring that computational resources are optimally utilized regardless of whether the data volume is at maximum or minimum levels, thus maintaining both reliability and productivity.
Solution Approach 2:
The computer group is designed with universal functionality where multiple computers can interchangeably handle different aspects of the reconstruction process. Any computer in the group can perform various computational tasks required for image reconstruction, allowing flexible resource allocation and ensuring that the system can adapt to varying data volumes without sacrificing performance or utilization efficiency.
3Productivity
If multiple computers are used to increase computing capacity, then reconstruction speed is improved, but system complexity increases
Solution Approach 1:
The computing management computer serves as an intermediary that coordinates task distribution and result aggregation among the multiple computers in the group. It manages the division of computational work, monitors processing status, and consolidates results from individual computers, thereby enabling parallel processing and improved reconstruction speed while abstracting away the complexity of multi-computer coordination from the overall system.
Data Source
AI summary
The invention concerns a method for reconstructing medical image data which has access to free capacities of at least two computers and manages the use thereof for the purposes of the reconstruction. The method is a particularly reliable alternative to the reconstruction of medical image data based on algorithms that would require a working memory of above-average size.