Decentralized Imaging Control via Networked Expert Workstations
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Solution Overview
Problem
Medical imaging systems require qualified personnel for operation and evaluation, which can be scarce, especially in less developed countries, leading to challenges in providing timely and efficient diagnostic services, especially at night or in remote locations.
Innovation Solution
A decentralized controlled imaging method using a control instance connected via a data transmission network to remotely operate medical imaging systems and an expert workstation for image data evaluation, enabling flexible operation and diagnosis independent of local specialist availability, with the aid of AI and automated systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If centralized control with virtual qualified operator is used, then availability of expert evaluation is improved, but response time and accessibility are worsened due to dependency on central facility availability
Solution Approach 1:
The centralized control system is segmented into distributed control instances that can operate independently at multiple locations. Each control instance can autonomously perform imaging control and evaluation tasks, eliminating the single-point bottleneck of centralized facilities and enabling parallel processing of multiple cases simultaneously.
Solution Approach 2:
The system transitions from a single-dimensional centralized model to a multi-dimensional distributed network architecture. Control instances are deployed across geographic dimensions and operational time dimensions, allowing patients to access expert evaluation capabilities at any location and time, not just during central facility operating hours.
2Measurement precision
If locally available specialists are required for operation and evaluation, then quality of care is improved, but accessibility and coverage are worsened in areas with specialist scarcity
Solution Approach 1:
Automated control systems and AI-based evaluation tools serve as intermediaries between the imaging system and human specialists. These intermediaries can perform preliminary control and evaluation tasks, triage cases by complexity, and prepare data for specialist review, thereby extending specialist capabilities to remote locations without requiring physical presence of specialists.
Solution Approach 2:
The system creates virtual copies of specialist expertise through AI models trained on expert data. These digital twins can replicate specialist evaluation patterns and decision-making processes, enabling consistent quality of care across distributed locations without requiring actual specialists to be physically present at each site.
3Adaptability or versatility
If more qualified personnel are deployed to improve service coverage, then accessibility is improved, but operational costs and system complexity are worsened
Solution Approach 1:
The imaging system is equipped with automated control capabilities and self-evaluation functions that can operate independently without requiring constant human intervention. The system can autonomously perform protocol selection, parameter optimization, initial image quality assessment, and basic diagnostic tasks, reducing the need for additional qualified personnel while maintaining service coverage.
Data Source
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AI summary
A decentralized controlled imaging procedure (100, 300, 400, 500) is described. In this imaging procedure, a control instance (24) that can be connected to a communication device (21k) of a medical imaging system (21) via a data transmission network is identified and selected. A network-based communication link is initiated between the communication device (21k) of the medical imaging system (21) and the control instance (24), and an image acquisition process of an examination area (FoV) of a patient (P) is remotely controlled by the control instance (24) via the network-based communication link. A diagnostic data acquisition procedure is also described. Furthermore, a decentralized image data acquisition system (20, 130) is described. A diagnostic data acquisition system is also described.Furthermore, a method for providing a trained automated control system is described. A training system (120) is also described. In addition, a training data acquisition system (130) is described.