Automated MR Image Quality Assessment via Multi-Module Metric Analysis
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Solution Overview
Problem
Current manual image quality assessment in magnetic resonance tomography (MRT) is time-consuming, subjective, and prone to errors, especially in stressful situations or with inexperienced operators, and lacks robustness in accounting for variability in MR image impressions.
Innovation Solution
A method involving a quality unit with multiple independent quality modules, each determining specific quality metrics such as NRMS, PSNR, DSS, GMSD, and others, which provide assessment indicators to an assessment unit for objective and automated image quality evaluation, utilizing machine learning and deep learning techniques for robustness and user-specific customization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual image quality assessment is performed by medical operators, then subjective evaluation can be conducted, but the process becomes time-consuming and prone to errors
Solution Approach 1:
The patent replaces the manual mechanical assessment process with an automated computing-based system. Multiple quality modules automatically calculate objective metrics (NRMS, PSNR, DSS, GMSD, etc.) without human intervention, eliminating the time-consuming nature of manual assessment while providing consistent, error-free measurements.
Solution Approach 2:
The assessment system performs self-evaluation by automatically processing images through multiple quality modules that compute metrics independently. The system serves itself by generating comprehensive quality assessments without requiring external human operators, thus reducing assessment time while maintaining precision.
2Reliability
If manual image quality assessment is performed by medical operators, then evaluation can be conducted, but subjective criteria and personal preferences lead to inconsistent results
Solution Approach 1:
The patent replaces subjective human judgment with objective computational algorithms. The computing unit automatically calculates standardized quality metrics without being influenced by operator preferences, stress levels, or experience, ensuring consistent and reliable assessment results across different users and situations.
Solution Approach 2:
The patent transforms subjective quality assessment into objective parameter-based evaluation. By changing from human perceptual criteria to quantifiable metrics (NRMS, PSNR, DSS, GMSD), the system eliminates variability introduced by different operators while maintaining ease of operation through automated computation.
3Measurement precision
If multiple quality metrics are determined by independent quality modules, then comprehensive image quality assessment is achieved, but system complexity increases
Solution Approach 1:
The patent divides the quality assessment system into multiple independent quality modules, each responsible for calculating specific metrics (NRMS, PSNR, DSS, GMSD). This segmentation allows comprehensive evaluation through specialized sub-components while keeping each module simple and manageable, reducing overall system complexity through modular design.
Solution Approach 2:
The computing unit serves multiple functions by housing and coordinating multiple quality modules within a single system. This multi-functionality allows comprehensive quality assessment without proportionally increasing system complexity, as the central computing unit manages all modules efficiently.
4Productivity
If automated image quality assessment is implemented, then objectivity and speed are improved, but the system requires complex algorithms and processing
Solution Approach 1:
The patent segments the complex assessment task into multiple independent quality modules, each handling specific metrics. This division allows parallel processing of different metrics, significantly improving assessment speed while keeping individual algorithm implementations relatively simple and manageable.
Solution Approach 2:
The patent transforms complex quality assessment into calculations based on standardized parameters and metrics. By changing from subjective evaluation to objective parameter measurement, the system achieves high productivity through automated computation of well-defined mathematical formulas, reducing the need for complex proprietary algorithms.
Data Source
AI summary
Techniques are described for providing at least one assessment indicator of an image quality of at least one magnetic resonance image, which comprises: providing at least one magnetic resonance image for assessing the image quality, wherein the providing is made to a quality unit; determining at least two different quality metrics of the at least one magnetic resonance image, wherein the quality unit comprises two or more quality modules, and each of the two or more quality modules is designed to determine one of the two or more different quality metrics, and providing the two or more quality metrics to an assessment unit; ascertaining the at least one assessment indicator by means of the assessment unit, wherein the at least one assessment indicator is ascertained from the two or more different quality metrics; and providing the at least one assessment indicator.