GPU Server Virtualization for Cath Lab Imaging
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Solution Overview
Problem
The high cost and resource-intensive nature of traditional cath lab imaging systems, which require expensive high-powered computers to process large 3D data sets, limits the availability of catheterization laboratories, especially in smaller clinics and under-resourced hospitals, making it difficult to perform procedures like intravascular imaging and angiographic interventions.
Innovation Solution
A computer server with a graphics processor that utilizes a hypervisor to create dedicated virtual machines for each cath lab, allowing for efficient sharing of graphical processing resources, including GPUs capable of massive parallel data processing, to handle the workload of multiple cath labs, even when they are geographically separated, thereby reducing the need for multiple dedicated machines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If each cath lab uses a dedicated high-powered computer for processing 3D medical imaging data, then the processing capability and image quality are sufficient, but the cost and resource requirements become prohibitively expensive
Solution Approach 1:
The patent combines multiple cath lab workloads onto a single shared computer system with a GPU cluster, allowing multiple virtual machines to access processing resources simultaneously. This merging approach maintains the processing capability needed for high-quality 3D medical imaging while eliminating the need for each cath lab to have its own dedicated high-powered computer.
Solution Approach 2:
The shared computer system is designed to serve multiple cath labs simultaneously, making a single system universal for processing imaging data from different cath labs. The system can dynamically allocate GPU resources to different virtual machines based on demand, providing multi-functional capability that replaces multiple dedicated systems.
2Productivity
If multiple dedicated high-powered computers are deployed to serve multiple cath labs, then each lab has sufficient processing power, but the overall system cost and resource consumption increase significantly
Solution Approach 1:
The patent merges the computing resources of multiple dedicated computers into a single shared system with GPU clustering. This allows the system to serve multiple cath labs with the same or fewer physical resources, as the GPU cluster can handle multiple workloads concurrently through virtualization and resource allocation.
Solution Approach 2:
The system implements self-service resource allocation where the shared computer automatically manages and distributes GPU resources to different cath lab virtual machines based on real-time demand. This eliminates the need for manual provisioning of dedicated computers for each lab while ensuring each lab receives adequate processing power when needed.
3Quantity of substance
If a shared computer system is used to process data from multiple cath labs, then resource efficiency improves and costs decrease, but the complexity of resource management and system architecture increases
Solution Approach 1:
The patent introduces a hypervisor as an intermediary layer between the physical GPU hardware and the multiple cath lab applications. This hypervisor manages resource allocation, scheduling, and isolation automatically, simplifying the complexity of running multiple virtual machines on shared hardware. The intermediary abstracts the complexity from individual cath lab operations while enabling efficient resource sharing.
Data Source
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AI summary
The invention provides a computer server with a graphical processer that can process data from multiple medical imaging systems simultaneously. Data sets can be provided by any suitable imaging system (x-ray, angiography, PET scans, MRI, IVUS, OCT, cath labs, etc.) and a processing system of the invention allocates resources in the form of a virtual machine, processing power, operating system, applications, etc., as-needed. Embodiments of the invention may find particular application with cath labs due to the particular processing requirements of typical cath lab systems.