MRI Raw Data Error Correction Using K-Space Mask Monitoring
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Solution Overview
Problem
Magnetic resonance imaging (MRI) systems face challenges in detecting and correcting errors, such as quantization noise, during the scanning process, which can result in corrupted images that are only identified after the scan is complete, leading to costly re-scans.
Innovation Solution
The MRI system dynamically monitors and corrects potential quantization errors by adjusting signal gain and re-acquiring data in real-time, using a mask-based approach to identify errors within the k-space matrix, ensuring accurate image reconstruction before the scan is finished.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If dynamic monitoring and correction of quantization errors is implemented during MRI scanning, then image quality and reliability are improved, but device complexity and processing requirements increase
Solution Approach 1:
The system performs preliminary actions by establishing mask boundaries before scanning begins. These masks define the acceptable dynamic range for quantization values. During scanning, the system simply checks whether quantization values fall within these pre-established boundaries, rather than performing complex real-time analysis. This preliminary setup enables reliable error detection without requiring complex ongoing processing.
Solution Approach 2:
The system employs self-service mechanisms by automatically monitoring its own quantization values during the scanning process. The data processing system continuously checks whether quantization values exceed mask boundaries and automatically triggers rescanning when errors are detected. This self-monitoring and self-correction capability improves reliability without requiring external intervention or overly complex external control systems.
2Loss of time
If real-time error detection and correction is performed during scanning, then loss of time due to re-scans is reduced, but use of energy and processing resources increases
Solution Approach 1:
The system applies partial action by monitoring only the quantization values against mask boundaries during scanning, rather than performing comprehensive real-time image analysis. This selective monitoring of specific parameters (quantization values within defined masks) enables timely error detection with minimal processing energy, preventing the need for complete re-scans while avoiding excessive computational resource consumption.
Solution Approach 2:
The system utilizes parameter changes by dynamically adjusting the gain of the receive coil during scanning. When quantization values approach mask boundaries, the system modifies the gain parameter to optimize the dynamic range and prevent quantization errors. This parameter adjustment enables real-time error prevention with minimal energy expenditure, as it involves simple gain control rather than complex processing operations.
3Manufacturing precision
If mask-based quantization error detection is implemented, then manufacturing precision of image data is improved, but ease of operation decreases
Solution Approach 1:
The system implements feedback mechanisms by continuously monitoring quantization values during scanning and comparing them against mask boundaries. When quantization errors are detected (values exceeding boundaries), the system provides feedback by automatically triggering a rescanning of the affected data. This closed-loop feedback ensures high data accuracy through continuous verification while maintaining ease of operation, as the feedback process is automated and requires no manual intervention from operators.
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 method reduces the incidence of corrupted images and minimizes the need for re-scans by detecting and correcting errors during the acquisition process, thereby reducing costs and improving image quality.
Implementation Method 1
When subjected to a strong magnetic field, the vector sum of the nuclear magnetic moments of a large number of atoms possessing a nuclear spin angular momentum, such as Hydrogen
Implementation Method 2
the vector sum of the nuclear magnetic moments of a large number of atoms possessing a nuclear spin angular momentum will produce a net magnetic moment in alignment with the externally applied field
Implementation Method 3
The resultant net magnetic moment will furthermore precess with a well-defined frequency that is proportional to the applied magnetic field
Implementation Method 4
After excitation by radio frequency pulses, the net magnetization will generate a signal that can be detected
Data Source
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AI summary
A method of data acquisition at a magnetic resonance imaging (MRI) system is provided. The system receives at least a portion of raw data for an image, and detects anomalies in the portion of raw data received. When anomalies are detected, the system can correct those anomalies dynamically, without waiting for a new scan to be ordered. The system can attempt to scan the offending portion of the raw data, either upon detection of the anomaly or at some point during the scan. The system can also correct anomalies using digital correction methods based on expected values. The anomalies can be detected based on variations from thresholds, masks and expected values all of which can be obtained using one of the ongoing scan, previously performed scans and apriori information relating to the type of scan being performed.