MRI RF Coil Abnormality Detection via Signal Path Segmentation
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Solution Overview
Problem
Current magnetic resonance imaging (MRI) systems face challenges in determining the source of signal-to-noise ratio (SNR) abnormalities, as they cannot differentiate between issues in the MRI apparatus and RF coils, leading to time-consuming troubleshooting and potential unnecessary repairs. Additionally, SNR measurement processes are complex and require manual operation, making remote or automated detection difficult.
Innovation Solution
The implementation of an MRI apparatus with an RF coil apparatus, receive circuitry, and processing circuitry that compares signals from different paths to detect abnormalities, allowing for automated detection of issues within the RF coil system without the need for a phantom, enabling remote operation and reducing repair costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a phantom is used to measure SNR in the RF coil, then the SNR value can be obtained, but it cannot be determined whether the abnormality is in the MRI apparatus or the RF coil
Solution Approach 1:
The patent segments the signal path into multiple sections: RF coil, receive circuitry, and other system components. By measuring noise levels at different segments and comparing them, the system can identify which specific segment contains the abnormality. This segmentation approach transforms a single undifferentiated SNR measurement into multiple targeted measurements that locate the problem source.
Solution Approach 2:
The patent introduces a noise measurement signal as an intermediary to diagnose system abnormalities. Instead of directly measuring image quality SNR, the system measures noise levels from the RF coil and compares them against expected values. This intermediary measurement provides diagnostic information about the health of individual components without requiring phantom imaging.
2Reliability
If multiple RF coils and phantoms are used for SNR measurement, then comprehensive coverage is achieved, but the operator cannot remember combinations and procedures
Solution Approach 1:
The system performs self-diagnosis by automatically measuring noise levels from each RF coil and comparing them against stored reference values. The processing circuitry autonomously identifies abnormalities without requiring operator intervention for phantom placement or measurement execution. This self-service capability eliminates the need for operators to remember complex procedures while maintaining comprehensive diagnostic coverage.
Solution Approach 2:
The patent stores reference noise level values for each RF coil in advance during normal operation. When diagnostic mode is activated, the system retrieves these pre-stored references and compares them against current measurements. This preliminary action of storing baseline data enables rapid automated diagnosis without requiring operators to manually set up phantoms or recall proper measurement procedures.
3Measurement precision
If manual SNR measurement with phantoms is performed, then measurement can be carried out, but remote operation or automated detection is not possible
Solution Approach 1:
The patent replaces the mechanical phantom-based measurement system with an electronic noise measurement system. Instead of physically placing phantoms in the RF coil, the system electronically generates and measures noise signals directly from the coil elements. This substitution eliminates the need for physical phantoms while maintaining measurement accuracy and enabling full automation and remote operation capability.
4Reliability
If troubleshooting is performed without knowing the abnormality location, then SNR issues can be identified, but it takes time for inexperienced servicemen to isolate the problem
Solution Approach 1:
The system segments the diagnostic process into targeted measurements for each RF coil and system component. By measuring and comparing noise levels from individual coils against reference values, the system quickly identifies which specific component is abnormal. This segmentation approach reduces troubleshooting time from hours of manual isolation to automated instant identification of the problematic component.
Solution Approach 2:
The system implements feedback by continuously monitoring noise levels from RF coils and comparing them against stored reference values. When an abnormality is detected, the system provides immediate feedback about which component is problematic. This feedback mechanism enables rapid troubleshooting by directing attention precisely to the faulty component rather than requiring systematic elimination of all possibilities.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This solution enables efficient detection of abnormalities in the RF coil system, simplifies the SNR measurement process, and allows for remote operation, reducing repair costs by accurately identifying malfunction spots within the MRI apparatus.
Implementation Method 1
a coil element 201 that generates a thermal noise
Data Source
AI summary
According to one embodiment, the MRI apparatus includes an RF coil apparatus having a coil element, a coil port to which the RF coil apparatus is connectible, receive circuitry receiving a signal detected by the RF coil apparatus via the coil port when neither an RF pulse nor a gradient magnetic field is being applied, and performing A/D conversion with an A/D converter, and processing circuitry detecting an abnormality based on the signal. With the RF coil apparatus being connected to the coil port, the receive circuitry switches at least one switch provided in a section between the coil element and the A/D converter between on and off, and receives the signal. The processing circuitry compares a signal of a path where the coil element and A/D converter are connected with a signal of a path where the coil element and A/D converter are not connected, and detects the abnormality.