Medical Imaging Distributed Cluster Processing

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Solution Overview

Problem

Medical imaging systems face challenges in achieving fast image reconstruction due to the need for substantial computing power, with conventional approaches either requiring local resources or relying on external cloud computing, which can be hindered by bandwidth limitations and network reliability issues.

Innovation Solution

A local control device for medical imaging systems that divides computing tasks into sequential sub-tasks, allowing for both local and remote execution, dynamically utilizing available computing resources to accelerate image reconstruction while ensuring reliability and efficiency.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Speed

If local centralized computing power is equipped to achieve fast image reconstruction, then image reconstruction speed is improved, but device complexity and resource requirements increase

Engineering Contradiction:

Improveimage reconstruction speedVSAvoidcomputing resource requirements

Core Design Contradiction:

SpeedVSDevice complexity

Solution Approach 1:

The computing task is divided into multiple independent sub-tasks that can be executed separately. The measurement control unit segments the overall image reconstruction process into discrete computational units that can be distributed and executed in parallel across different computing resources, reducing the burden on any single local computing unit while maintaining fast reconstruction speed.

Inventive Principle:

Principle #1Segmentation

2Device complexity

If computing tasks are offloaded to external cloud computing resources, then local device complexity is reduced, but reliability decreases due to network dependency

Engineering Contradiction:

Improvelocal computing resource requirementsVSAvoidimaging operation reliability

Core Design Contradiction:

Device complexityVSReliability

Solution Approach 1:

The system merges local computing capabilities with remote computing resources into a hybrid architecture. The local control device retains the ability to execute computing tasks independently while also being capable of utilizing remote resources when available. This combination ensures that the system can operate reliably in both connected and disconnected states, maintaining imaging operation reliability while reducing local device complexity.

Inventive Principle:

Principle #5Merging (Combining)

3Device complexity

If monolithic computing model is used for image reconstruction, then device complexity is reduced, but image reconstruction speed decreases

Engineering Contradiction:

Improvecomputing task structureVSAvoidimage reconstruction speed

Core Design Contradiction:

Device complexityVSSpeed

Solution Approach 1:

The monolithic computing task is segmented into multiple independent sub-tasks that can be executed in parallel. Instead of processing the entire image reconstruction as a single sequential operation, the system divides the computational workload into separate units that can be distributed across multiple processing cores or remote computing resources, significantly accelerating the reconstruction speed while maintaining manageable device complexity.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system ensures continuous processing by executing multiple sub-tasks in parallel without idle waiting periods. While one sub-task is being processed, other sub-tasks can be prepared or executed simultaneously, eliminating gaps in the computational workflow and maximizing the utilization of available computing resources throughout the image reconstruction process.

Inventive Principle:

Principle #20Continuity of useful action

4Speed

If external computing resources are used to accelerate imaging process, then image reconstruction speed is improved, but loss of time occurs due to network communication overhead

Engineering Contradiction:

Improveimage reconstruction speedVSAvoidnetwork communication time

Core Design Contradiction:

SpeedVSLoss of time

Solution Approach 1:

By dividing the computing task into smaller sub-tasks, the system can transmit smaller data units to remote computing resources, reducing the overall network communication overhead. The segmented approach allows for more efficient data transmission and retrieval compared to sending a single large computing task, thereby minimizing the time lost to network communication while still utilizing external resources for acceleration.

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS20230410244A1Medical imaging with distributed cluster-based processing

Publication Date: 2023.12.21 SIEMENS HEALTHINEERS AG

AI summary

A local control device for a medical imaging system includes a measurement control unit for dividing a computing task into a sequence of consecutive sub-tasks. The computing task is related to a generation of image data based on measurement data. The local control device includes an execution unit for executing the computing task. The local control device includes a selection unit for selecting a sub-task of the consecutive sub-tasks for external execution. The local control device includes an outsourcing unit for outsourcing the selected sub-task to a remote computing source for remotely generating a partial computing result and for receiving the generated partial computing result. The execution unit is arranged to execute the computing task based on the received generated partial computing result.