MRI Coil Placement Verification for Off-Label Use Detection
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Solution Overview
Problem
Magnetic resonance imaging (MRI) systems struggle to detect the correct placement of RF coils relative to the subject's body, leading to potential image quality issues and safety risks due to off-label use, which can result in excessive specific absorption rate (SAR) when coils are used for anatomical regions not specified in their certification.
Innovation Solution
A posture recognition system using image sensors and machine learning algorithms to determine the subject's posture and RF coil location, comparing these to predefined anatomical features to identify mismatches and provide warning signals before scans, ensuring correct coil placement and safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If RF coils are used for anatomical regions not specified in their certification (off-label use), then the versatility and adaptability of the MRI system increases, but the safety and reliability deteriorate due to excessive specific absorption rate (SAR) and image quality issues
Solution Approach 1:
The system implements automatic feedback by detecting the subject's anatomical posture using image sensors and machine learning, comparing the detected anatomy with the coil's certified anatomical regions, and providing real-time warnings or preventing operation when mismatches are detected. This closed-loop feedback mechanism ensures safety while maintaining adaptability.
Solution Approach 2:
The system performs preliminary detection and verification of coil-anatomy matching before the MRI scan begins. By using posture recognition and machine learning to identify anatomical regions in advance, the system prevents unsafe off-label usage before it can cause harm, while still allowing legitimate adaptive uses.
2Reliability
If automatic coil identification and posture recognition systems are implemented, then the safety and reliability of MRI scans improve, but the device complexity and cost increase
Solution Approach 1:
The MRI system integrates multiple functions into a unified platform: automatic coil identification, image capture, posture recognition using machine learning, anatomical region detection, and safety verification. This multi-functional approach improves reliability without proportionally increasing complexity, as shared hardware and software resources serve multiple purposes.
Solution Approach 2:
The system performs self-verification by automatically detecting the subject's anatomy, comparing it with coil certifications, and determining whether the coil placement is appropriate. This self-service capability reduces the need for manual verification by operators, improving reliability while the automated nature keeps complexity manageable.
3Ease of operation
If manual verification of coil placement is performed by operators, then the ease of operation is maintained, but the measurement precision and detection accuracy of coil position deteriorate
Solution Approach 1:
The system introduces an intermediary automated verification layer between the operator and the coil placement process. Image sensors and machine learning algorithms act as intermediaries to objectively detect and verify anatomical regions, providing precise measurement data to support operator decisions. This maintains operator control while significantly improving detection precision.
Solution Approach 2:
The system replaces manual visual inspection and physical measurement with automated image-based detection and machine learning analysis. This substitution of mechanical/operator-based verification with optical and computational methods dramatically improves measurement precision while requiring minimal additional operator intervention.
Data Source
AI summary
Disclosed herein is a medical system comprising: —a memory storing machine executable instructions; —a computational system, wherein execution of the machine executable instructions causes the computational system to perform a mismatch check comprising: —receive posture recognition system data, wherein the posture recognition system data comprises a set of subject coordinates and a set of coil coordinates described using a current coordinate system, wherein the set of subject coordinates are descriptive of anatomical features of a subject, wherein the set of coil coordinates are descriptive of a coil location of a magnetic resonance imaging coil, wherein coil data comprising a predefined range of coil positioning coordinates referenced to the anatomical features is associated with the magnetic resonance imaging coil; —determine an allowed range of coil coordinates by mapping the predefined range of coil positioning coordinates to the current coordinate system using the set of subject coordinates and the anatomical features; and —provide a warning signal in case of a mismatch between the set of coil coordinates and the allowed range of coil coordinates.