Instant Scout Scan Checker for Medical Imaging Setup Errors
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Solution Overview
Problem
In medical imaging, there is a shortage of well-trained staff leading to preventable quality issues such as incorrect patient positioning, foreign objects in the field of view (FOV), and incorrect FOV, which can result in the need to re-acquire images or repeat examinations, increasing patient inconvenience and dose load.
Innovation Solution
An apparatus that extracts a preview image from a medical imaging device's GUI, performs image analysis to detect potential problems, and outputs alerts for issues like foreign objects, incorrect FOV, and patient misplacement, using machine learning and image segmentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If automated image analysis is implemented to detect potential problems in preview images, then image quality and patient safety are improved, but device complexity and implementation difficulty increase
Solution Approach 1:
The patent uses the existing graphical user interface (GUI) as an intermediary to capture preview images. Instead of adding a separate dedicated imaging system, the solution leverages the controller's existing display and capture capabilities, thereby reducing system complexity while maintaining reliability improvements through automated analysis.
Solution Approach 2:
The system performs self-analysis by automatically processing captured preview images through machine learning algorithms to detect potential problems. This self-service approach eliminates the need for manual review by technologists, improving reliability without requiring additional human resources or complex operational workflows.
2Reliability
If manual review of preview images by technologists is performed, then image quality issues can be detected, but staff workload increases and productivity decreases
Solution Approach 1:
The system performs automatic quality assessment through machine learning algorithms that analyze preview images without requiring technologist intervention. This self-service capability maintains high image quality standards while preserving examination throughput, as the automated system operates in real-time without adding manual review steps to the workflow.
Solution Approach 2:
The system provides immediate feedback by analyzing preview images and alerting technologists to potential problems before the actual imaging examination begins. This real-time feedback mechanism ensures high image quality while maintaining productivity, as corrections can be made during setup rather than requiring re-acquisition during the examination.
3Reliability
If preview images are analyzed in real-time before imaging examinations, then preventable quality issues are reduced, but processing time and computational resources increase
Solution Approach 1:
The system performs quality assessment during the setup phase by analyzing preview images before the actual imaging examination begins. This preliminary action ensures that potential problems are detected and corrected in advance, maintaining high image quality while minimizing impact on examination throughput since the analysis occurs during routine positioning and setup activities.
Data Source
AI summary
An apparatus (1) for use in conjunction with a medical imaging device (2) having an imaging device controller (4) that displays a graphical user interface (GUI) (8) including a preview image viewport (9). The apparatus includes at least one electronic processor (20) programmed to: receive a video feed (17) of the GUI displayed on the imaging device controller; extract a preview image (12) displayed in the preview image viewport from the live video feed of the GUI; perform an image analysis (38) on the extracted preview image to detect one or more image features (42) indicative of one or more potential problems associated with a medical imaging examination performed with the medical imaging device; and output an alert (30) when one or more potential problems associated with the medical imaging examination is detected from the one or more image features.