Radiation Imaging Control Apparatus Foreign Matter Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing radiation imaging systems do not effectively prevent undesired re-imaging due to foreign matters appearing in radiation images, such as clothing buttons or necklaces, especially in scenarios like group medical examinations where attention to such details may be compromised.
Innovation Solution
A radiation imaging control apparatus that includes an image acquisition unit, a foreign matter detection unit using machine learning algorithms, a warning information generation unit, and a display control unit. This apparatus acquires captured images, detects foreign matters likely to appear in radiation images, generates warnings, and displays notifications to prevent undesired radiation re-imaging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If radiation images are captured without foreign matter detection, then imaging speed is maintained and device complexity is reduced, but undesired re-imaging occurs due to foreign matters like clothing buttons and necklaces
Solution Approach 1:
The system captures an optical image of the patient before radiation image acquisition and uses machine learning to detect foreign matters in advance. This preliminary detection allows the system to issue warnings or prevent imaging before foreign matters cause undesired re-imaging, resolving the contradiction by preventing the problem rather than addressing it after occurrence.
Solution Approach 2:
An optical camera serves as an intermediary device to capture images of foreign matters that are not directly detectable by the radiation imaging system. The machine learning algorithm acts as an intermediary processor to analyze these optical images and identify foreign objects, enabling indirect detection that improves reliability without requiring complex modifications to the radiation imaging hardware itself.
2Measurement precision
If manual checking of foreign matters is performed, then detection accuracy is improved, but time consumption increases and productivity decreases
Solution Approach 1:
The system performs self-service by automatically detecting foreign matters using machine learning algorithms without requiring manual intervention. The machine learning model autonomously analyzes optical images, identifies foreign objects, and generates warnings, thereby maintaining high detection accuracy while eliminating the time consumption associated with manual checking and preserving imaging productivity.
3Difficulty of detecting and measuring
If optical camera imaging is added for foreign matter detection, then foreign matter detection capability is improved, but device complexity and cost increase
Solution Approach 1:
Instead of modifying the radiation imaging system to directly detect foreign matters, the system uses a separate optical camera to capture images of the patient and foreign objects. This optical image serves as a copy or representation that can be analyzed by machine learning algorithms to identify foreign matters, improving detection capability while keeping the added complexity manageable by using a simple, well-understood optical imaging approach rather than complex radiation-based detection.
Data Source
AI summary
A radiation imaging control apparatus according to an aspect of the present invention includes an image acquisition unit configured to acquire a captured image obtained by capturing an image of a subject, a foreign matter detection unit configured to detect, in a case where a radiation image of the subject is to be acquired, a foreign matter which is likely to appear in the radiation image from the captured image acquired by the image acquisition unit, a warning information generation unit configured to generate warning information regarding image capturing of a radiation image based on the foreign matter detection unit having detected a foreign matter in the captured image, and a display control unit configured to issue a notification based on the warning information.