Multi-GPU CT Image Reconstruction with Dynamic Priority Assignment
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Solution Overview
Problem
Computed tomography (CT) image processing apparatuses face difficulties in reconstructing cross-sectional images in real-time when at least one graphics processing unit (GPU) malfunctions, leading to inefficiencies and potential diagnostic delays in emergency situations.
Innovation Solution
An apparatus and method that dynamically assign priority operations to a plurality of GPUs, allowing the system to maintain normal operation even with a malfunctioning GPU by distributing tasks such that the primary operation is performed by a greater number of functioning GPUs, while secondary operations are handled by fewer GPUs, ensuring continuous image reconstruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If a Multi-GPU architecture is used to reconstruct cross-sectional images, then image reconstruction speed is boosted, but system reliability deteriorates because the GPUs perform tasks interdependently and any malfunction prevents intended reconstruction
Solution Approach 1:
The patent segments the image reconstruction task into independent first operations and second operations with different priority levels. The first operation is divided among multiple GPUs while the second operation can be performed by a single GPU or fewer GPUs, allowing the system to continue functioning even if some GPUs malfunction during execution.
Solution Approach 2:
The patent implements beforehand cushioning by establishing a priority-based operation assignment mechanism before any malfunction occurs. The controller is configured to monitor GPU status and dynamically reassign operations based on predetermined priorities, ensuring that critical first operations can still be performed by remaining functional GPUs even when some GPUs fail.
2Power
If multiple GPUs perform image processing interdependently, then processing capacity is enhanced, but ease of operation deteriorates because the system becomes complex to manage and monitor
Solution Approach 1:
The patent implements feedback by having the controller continuously monitor the operational status of each GPU and use this information to dynamically adjust task assignments. When a GPU malfunctions or shows signs of failure, the controller receives feedback about the system state and automatically reassigns operations to maintain functionality without requiring manual intervention.
Solution Approach 2:
The patent applies dynamics by making the GPU task assignment flexible and adaptive rather than fixed. The controller dynamically adjusts which GPUs perform first operations versus second operations based on real-time system conditions, operational priorities, and individual GPU performance characteristics, simplifying management while maintaining high processing capacity.
Data Source
AI summary
An apparatus for processing a medical image includes an image processor including a plurality of processors, the plurality of processors configured to reconstruct a cross-sectional image of an object by performing a first operation having a first priority and a second operation having a second priority that is lower than the first priority, and a controller configured to monitor whether a malfunction occurs among the plurality of processors, and configured to assign, to at least one of the plurality of processors, at least one of the first operation and the second operation to be performed, based on a result of monitoring of the plurality of processors.