AI Classification for MRI Spike False Value Detection
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Solution Overview
Problem
Magnetic resonance imaging (MRI) systems face challenges in efficiently identifying and correcting spike false values, which can lead to artifacts and reduced signal-to-noise ratios due to various interference sources, requiring costly and time-consuming manual troubleshooting.
Innovation Solution
A computer-implemented method using a trained artificial intelligence classification function, specifically a neural network, to analyze radiofrequency signal data and determine the cause of spike false values, providing a probability vector for potential causes and enabling targeted correction measures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual troubleshooting is used to identify and correct spike false values, then the problem can be resolved, but it requires costly and time-consuming technician intervention
Solution Approach 1:
The system automatically detects and classifies spike false values using AI algorithms, enabling self-diagnosis and self-correction without requiring manual technician intervention. The control facility independently identifies interference sources and applies appropriate correction measures, transforming the troubleshooting process from a manual service model to an autonomous self-service model.
Solution Approach 2:
The patent replaces manual mechanical troubleshooting with an automated computational system. The AI-based classification function substitutes human technicians' analytical work, using machine learning models to detect patterns and identify causes of spike false values that previously required human expertise and time-consuming investigation.
2Reliability
If manual troubleshooting is used to identify and correct spike false values, then the problem can be resolved, but it involves costly technician intervention
Solution Approach 1:
The system performs automatic detection and classification of spike false values, eliminating the need for expensive manual technician services. The control facility independently executes the troubleshooting process, reducing operational costs by replacing human labor with automated AI-based analysis and correction mechanisms.
Solution Approach 2:
The patent substitutes manual troubleshooting operations with an automated computational system. The AI classification function replaces human technicians' expertise and labor, transforming the cost structure from labor-intensive manual services to automated computational processing, thereby reducing operational expenses.
3Productivity
If AI classification function is used to identify spike false values, then manual intervention is reduced, but the system complexity increases
Solution Approach 1:
The control facility is designed to perform multiple functions: it not only controls the magnetic resonance imaging process but also executes AI-based detection, classification, and correction of spike false values. By integrating these diverse functions into a single multi-functional system, the patent avoids increasing overall system complexity while enhancing productivity.
Solution Approach 2:
The patent combines the AI classification function with the existing control facility, merging detection, analysis, and correction capabilities into a unified system. This integration approach consolidates multiple functions into one system rather than adding separate independent systems, thereby managing complexity while improving problem resolution speed.
Data Source
AI summary
A computer-implemented method for operating a magnetic resonance facility to determine at least one potential cause for a false value in the image data of at least one imaging procedure, compiling an input dataset that is to be analyzed and comprises radiofrequency signal data acquired during the imaging procedure, applying a trained artificial intelligence classification function to the input dataset to determine an output dataset that describes the potential causes of the false value, and outputting at least a portion of the output data of the output dataset.