Medical Imaging Auto-Correction Using Machine Logs for Image Quality
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Solution Overview
Problem
Medical imaging devices experience degradation in image quality over time due to hardware issues, which are not effectively addressed by current maintenance practices, leading to suboptimal clinical assessments.
Innovation Solution
A system that utilizes machine logs to identify out-of-range parameters and automatically tunes hardware settings or applies image filters to improve image quality, while also providing recommendations for servicing or replacing components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If preventive maintenance is performed on a fixed schedule, then component replacement is ensured, but image quality degradation is not effectively addressed and maintenance time is wasted
Solution Approach 1:
The system performs preliminary analysis of machine logs to identify degradation trends before they affect image quality. By detecting out-of-range parameters early and automatically tuning hardware settings, the system prevents quality degradation without requiring scheduled maintenance interventions.
Solution Approach 2:
The system continuously monitors machine logs and image quality metrics, using feedback loops to automatically adjust hardware settings when degradation is detected. This closed-loop control enables real-time compensation for component aging, eliminating the need for fixed-schedule preventive maintenance.
2Reliability
If hardware components are replaced on schedule, then component failure is prevented, but clinically significant image quality degradation is not addressed
Solution Approach 1:
The system enables self-diagnosis and self-correction by automatically analyzing machine logs, identifying out-of-range parameters, and tuning hardware settings without human intervention. This autonomous maintenance capability addresses image quality issues before they become clinically significant.
Solution Approach 2:
The system replaces manual mechanical maintenance with automated digital analysis and control. By using software-based parameter monitoring and automatic hardware tuning, the system achieves more precise and timely intervention than traditional scheduled maintenance.
3Loss of information
If manual monitoring of machine logs is performed, then device status is tracked, but image quality degradation is not detected in time
Solution Approach 1:
The system performs continuous automated analysis of machine logs alongside continuous image quality monitoring. This uninterrupted monitoring and analysis enables real-time detection of degradation trends that would be missed by periodic manual checks.
Solution Approach 2:
The system introduces an automated intermediary analysis layer between raw machine log data and maintenance decisions. This intermediary layer continuously processes log information, identifies degradation patterns, and triggers appropriate responses without requiring manual interpretation.
4Reliability
If scheduled maintenance is performed, then component replacement is ensured, but unnecessary maintenance increases costs and service life is reduced
Solution Approach 1:
The system performs preliminary assessment of component health through continuous machine log analysis, enabling maintenance to be scheduled only when actually needed based on real-time degradation data rather than fixed time intervals.
Solution Approach 2:
The system transitions from static scheduled maintenance to dynamic condition-based maintenance. By continuously monitoring component parameters and adjusting maintenance timing based on actual degradation rates, the system extends component service life while maintaining reliability.
Data Source
AI summary
A device for optimizing an image acquisition device (12) includes at least one electronic processor (20) operatively connected to read a machine log (30) of the image acquisition device. A non-transitory computer readable medium (26) stores instructions readable and executable by the at least one electronic processor to perform an image acquisition method (100). The method includes: extracting logged parameters of the image acquisition device from the machine log of the image acquisition device; identifying one or more out-of-range parameters of the image acquisition device from the logged parameters extracted from the machine log of the image acquisition device; automatically tuning one or more electrical or mechanical settings of the image acquisition device on the basis of the one or more out-of-range parameters to transform the image acquisition device into a tuned image acquisition device; and controlling the tuned image acquisition device to acquire one or more images of a patient.