Cloud-Based Medical Imaging Reconstruction via Gadgetron Framework
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Solution Overview
Problem
Current medical imaging systems face challenges with inadequate computational resources for fast image reconstruction, leading to lengthy processing times and limitations in deploying advanced algorithms clinically, due to hardware obsolescence and insufficient processing power at the time of deployment.
Innovation Solution
The Gadgetron framework is extended to support distributed computing across multiple nodes, leveraging cloud computing resources to scale computational power dynamically and deploy non-linear reconstruction algorithms efficiently, allowing for clinically acceptable latency and improved image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If computational resources are increased to achieve fast image reconstruction, then image reconstruction time is reduced, but hardware complexity and cost increase
Solution Approach 1:
The patent extracts the computational processing function from the local medical imaging device and relocates it to a remote cloud computing system. The imaging device only performs data acquisition and transmission, while the computationally intensive image reconstruction is performed remotely, thereby reducing local hardware complexity while maintaining fast reconstruction capabilities.
Solution Approach 2:
The patent introduces a communication network as an intermediary between the medical imaging device and the cloud computing system. This intermediary enables the transfer of raw imaging data and reconstructed images, allowing the system to leverage remote computational resources without requiring direct integration of complex hardware.
2Manufacturing precision
If advanced reconstruction algorithms are deployed, then image quality is improved, but computational requirements increase
Solution Approach 1:
The patent creates a universal cloud-based computational platform that can execute multiple different reconstruction algorithms (e.g., iterative reconstruction, compressed sensing, deep learning-based methods) on demand. This multi-functional approach allows the system to deploy advanced algorithms for improved image quality without requiring each local device to have dedicated hardware for each algorithm.
Solution Approach 2:
The patent implements dynamic allocation of computational resources in the cloud system, where processing power can be scaled up or down based on the specific requirements of different reconstruction algorithms and imaging modalities. This dynamic approach allows advanced algorithms to be deployed with appropriate computational power without over-provisioning.
3Productivity
If processing power is increased locally, then reconstruction speed is improved, but hardware obsolescence occurs faster
Solution Approach 1:
The patent shifts the computational processing from the spatial dimension (local hardware) to the network dimension (cloud infrastructure). By moving processing to the cloud, the system can access continuously updated computational resources without the local device hardware becoming obsolete, as the processing power exists in a separate, upgradable dimension.
Data Source
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AI summary
The present invention relates to a system and apparatus for managing and processing raw medical imaging data.