MRI Receiving System Noise Distribution for Patient-Specific QA
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Solution Overview
Problem
Existing magnetic resonance imaging systems lack effective methods for synchronous and patient-specific quality assurance, failing to account for patient-dependent noise patterns and errors, such as movement, which can affect the accuracy of noise detection and system operation.
Innovation Solution
A method involving acquiring magnetic resonance images, determining noise distribution using a computing unit, and assessing system operation based on this distribution, allowing for continuous quality assurance that includes noise reduction and patient-specific effects without the need for phantoms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If quality assurance is performed using a phantom and specific service imaging sequence, then system errors can be detected, but patient-dependent error patterns such as movement are not taken into account
Solution Approach 1:
The system performs self-diagnosis by automatically evaluating noise distributions in acquired magnetic resonance images against expected noise distributions, enabling the system to monitor its own quality without external phantom-based quality assurance
Solution Approach 2:
The quality assurance system transitions from static phantom-based evaluation to dynamic patient-specific monitoring by continuously evaluating noise distributions in real-time acquired images, adapting to each patient's specific conditions and movement patterns
2Measurement precision
If noise distribution is determined from acquired magnetic resonance images, then patient-specific noise patterns can be detected, but additional processing time and computational resources are required
Solution Approach 1:
The patent replaces mechanical/physical quality assurance methods (phantoms, manual evaluations) with computational analysis by substituting physical measurement systems with algorithmic noise distribution evaluation in k-space and image space
Solution Approach 2:
The system creates a virtual reference by generating expected noise distributions through simulation or reference measurements, then compares actual noise distributions against these virtual copies to detect deviations without requiring physical phantom-based quality assurance
3Reliability
If synchronous monitoring of each magnetic resonance image is implemented, then patient-dependent error patterns can be detected, but system complexity increases
Solution Approach 1:
The noise evaluation system serves multiple functions simultaneously: it monitors quality assurance, detects patient movement, identifies system errors, and evaluates image quality, making the system versatile without requiring separate dedicated systems for each function
Data Source
AI summary
A computer-implemented method for determining correct operation of a receiving system of a magnetic resonance device using of a magnetic resonance measurement, by: acquiring a magnetic resonance image using the receiving system during the magnetic resonance measurement, determining a noise distribution of the acquired magnetic resonance image by means of a computing unit, and determining correct operation of the receiving system by means of the computing unit on the basis of the noise distribution. Also, a magnetic resonance device, having a computing unit which is designed to coordinate the computer-implemented method and execute the by means of the magnetic resonance device.