MRI Data Acquisition With Dynamic Gain for Quantization Error Control
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Solution Overview
Problem
Magnetic resonance imaging (MRI) systems face challenges in detecting errors in acquired signals, particularly quantization noise, which can corrupt entire images and are typically identified only after the scan is complete, leading to costly re-scans.
Innovation Solution
The implementation of a method within the MRI system to detect and correct quantization errors in real-time by using an analog to digital converter (ADC) to digitize signals with adjustable gain, allowing for dynamic response during the scan to mitigate signal anomalies, such as applying varying gains to adjust signal amplitudes and phases to prevent clipping and loss of information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time detection and correction of quantization errors is implemented, then image quality and reliability are improved, but device complexity increases
Solution Approach 1:
The system performs preliminary detection of quantization errors during the scanning process by monitoring digitized signals against expected boundaries. When potential errors are detected, the system triggers a rescan of the affected k-space lines before completing the full image reconstruction, preventing corrupted images from being generated in the first place.
Solution Approach 2:
The system implements a feedback mechanism where detected quantization errors trigger automatic correction actions. The error detection module continuously monitors the scanning process, and when anomalies are detected, the system feeds this information back to the scanning control to initiate rescans of specific k-space lines, creating a closed-loop quality assurance system.
2Loss of time
If real-time error detection is performed during scanning, then loss of time is reduced by avoiding complete re-scans, but device complexity increases
Solution Approach 1:
The system divides the k-space data acquisition into separable, independently rescannable lines. When a quantization error is detected in a specific k-space line, only that particular line needs to be rescanned rather than repeating the entire scan sequence. This segmentation allows partial rescans that significantly reduce the time penalty compared to complete re-scans.
Solution Approach 2:
The system performs partial rescans of only the affected k-space lines rather than complete rescans of the entire dataset. This partial action approach applies the corrective scanning effort only where needed, reducing the overall time investment while still achieving the goal of eliminating corrupted data from the final image.
3Manufacturing precision
If adjustable gain is used to prevent quantization errors, then manufacturing precision is improved, but device complexity increases
Solution Approach 1:
The system employs dynamic gain adjustment where the amplification factor is varied during the scanning process based on the detected signal characteristics. By adapting the gain level in real-time to match the signal amplitude, the system optimizes the utilization of the ADC's dynamic range and minimizes quantization errors without requiring overly complex hardware design.
Solution Approach 2:
The system changes the gain parameter during signal acquisition to optimize digitization accuracy. By adjusting the amplification factor to match the expected signal amplitude range, the system ensures that the ADC operates in its optimal range, thereby reducing quantization errors and improving overall signal fidelity.
Data Source
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.