Image Shift Detection Using Reference Elements in MR-Linac Systems
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Solution Overview
Problem
Image shift and wraparound issues in image capture devices, such as MR-Linac systems, can be difficult to identify, especially for small shifts, leading to inaccuracies and safety concerns in radiotherapy treatments.
Innovation Solution
A method involving the use of reference elements, such as faulty pixels or markers, which are identified during calibration and compared to their expected positions to detect image shifts, allowing for image correction or discard, thereby reducing user burden and enhancing safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If image capture devices are used without reference elements, then the device operation is simpler, but image shift cannot be detected leading to safety issues
Solution Approach 1:
Reference elements are incorporated into the imaging panel during manufacture before actual image capture operations. This preliminary incorporation ensures that detection capability is built-in from the start, eliminating the need for additional external detection equipment while maintaining reliability.
Solution Approach 2:
Reference elements act as intermediary markers that facilitate the detection of image shifts. These elements serve as a mediator between the imaging system and the detection algorithm, enabling accurate shift detection without requiring complex hardware modifications to the entire system.
2Reliability
If manual image verification is required, then device complexity is reduced, but user burden increases and safety is compromised
Solution Approach 1:
The imaging system performs self-verification by automatically detecting image shifts through analysis of reference element positions. This self-service capability eliminates the need for manual verification by users, reducing operational burden while maintaining high safety standards through automated quality control.
Solution Approach 2:
The system implements automated feedback mechanisms where the detection algorithm continuously monitors reference element positions and provides real-time feedback on image quality. This feedback loop enables automatic correction or rejection of shifted images without requiring user intervention, thereby improving safety while ease of operation.
3Productivity
If small image shifts occur, then image capture continues without interruption, but detection difficulty increases leading to potential errors
Solution Approach 1:
Instead of analyzing the entire image for shifts, the system focuses detection efforts on specific local regions where reference elements are positioned. This localized approach maintains imaging continuity by allowing most of the image to be processed normally while concentrating computational resources on detecting even subtle shifts at reference element locations.
Solution Approach 2:
The detection algorithm employs sensitive parameter changes in the analysis of reference element positions, such as sub-pixel precision measurements and statistical analysis of position variations. These parameter changes enable the detection of small shifts that would be imperceptible in standard image analysis, maintaining productivity while overcoming detection difficulties.
Data Source
AI summary
A method of identifying image shift comprises identifying a position of a reference element in a captured image, comparing the identified position with a predetermined expected position, and identifying image shift if the compared positions do not match.