MRI Anomaly Correction via Dynamic Partial Rescanning
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Solution Overview
Problem
Magnetic resonance imaging (MRI) systems face challenges in detecting and correcting anomalies in data acquisition, leading to costly re-scans due to corrupted images not identified until the completion of the scan.
Innovation Solution
A method for correcting anomalies in MRI data acquisition involves detecting anomalies in received raw data by comparing it to a mask with lower and upper boundary values, and repeating partial scans with varying TR or TE parameters to obtain replacement data, allowing for dynamic correction during the scan process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a complete scan is performed to ensure image quality, then image reliability is improved, but scan time increases and productivity decreases
Solution Approach 1:
The system performs preliminary anomaly detection during the data acquisition process by comparing received raw data against expected signal ranges defined by masks. This allows early identification of corrupted data before the complete scan finishes, enabling selective re-scanning of only affected sections rather than requiring complete re-scans.
Solution Approach 2:
The scan process is segmented into detectable portions where anomalies can be identified and corrected independently. Instead of treating the entire scan as a single unit, the system divides the data acquisition into segments that can be individually assessed and re-scanned if needed, improving overall scan efficiency.
2Loss of time
If anomaly detection is performed during scan, then scan time is reduced, but system complexity increases
Solution Approach 1:
The system implements feedback mechanisms where detected signal values are continuously compared against predefined masks during data acquisition. This feedback loop provides real-time information about data quality, enabling dynamic adjustment of the scanning process and early termination of scans with detectable anomalies.
Solution Approach 2:
The anomaly detection system uses built-in reference masks and automatic comparison capabilities that are integrated into the MRI system itself. The system performs self-diagnosis of data quality without requiring external intervention or complex additional equipment.
3Reliability
If repeated partial scans are performed to correct anomalies, then image reliability is improved, but loss of time increases
Solution Approach 1:
The system performs partial re-scans of only the specific sections containing anomalies rather than repeating entire scans. This partial action approach corrects only the necessary portions of data while leaving already-verified sections untouched, minimizing time loss during correction cycles.
Solution Approach 2:
The system modifies scanning parameters such as TR (repetition time) or TE (echo time) when performing repeated partial scans to correct persistent anomalies. By changing acquisition parameters, the system attempts to obtain different signal characteristics that may resolve the anomaly without requiring unlimited re-scanning.
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.