Motion Correction in Accelerated T1-Weighted MRI
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Solution Overview
Problem
Current magnetic resonance imaging (MRI) techniques face challenges in obtaining clinically useful images when patients are moving, as they often result in blurring and artifacts due to inadequate motion correction, especially in accelerated imaging scenarios.
Innovation Solution
The method involves acquiring undersampled T1-weighted k-space data using a PROPELLER technique, with a calibration blade for fully or oversampled k-space data to generate reconstruction weights via the APPEAR algorithm, and synthesizing missing data to produce motion-corrected T1-weighted images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If accelerated imaging techniques are used to reduce scan time, then productivity is improved, but image quality deteriorates due to motion artifacts and blurring
Solution Approach 1:
The system performs preliminary motion correction by comparing k-space data from different blades to estimate patient motion during the scan. This motion estimation is done before final image reconstruction, allowing the system to pre-correct the data by adjusting phase and magnitude of k-space samples based on the estimated motion, thereby preventing motion artifacts in the final image
Solution Approach 2:
The system implements a feedback loop where motion is continuously estimated from acquired k-space data, and correction parameters are continuously updated based on this motion estimation. The corrected k-space data is then used for reconstruction, creating a closed-loop system that adaptively compensates for patient motion throughout the accelerated scan
2Loss of time
If less k-space data is acquired to accelerate imaging, then scan time is reduced, but measurement precision deteriorates due to insufficient data for accurate reconstruction
Solution Approach 1:
The system introduces an intermediary calibration blade that is fully or oversampled, which serves as a reference for estimating motion and generating reconstruction weights. This calibration blade acts as a mediator that enables accurate reconstruction of the undersampled imaging blades by providing the necessary information about system response and motion without requiring full sampling of all imaging data
Solution Approach 2:
The system changes the sampling parameters dynamically by using different sampling strategies for different parts of k-space. The calibration blade uses full or oversampling while the imaging blades use undersampling, and the system adjusts reconstruction parameters (weights, phase corrections) based on the relationship between these differently sampled datasets to maintain reconstruction accuracy
Data Source
AI summary
A method includes the acts of acquiring a blade of k-space calibration data; acquiring a set of T1-weighted k-space imaging data, the set of T1-weighted k-space imaging data having blades of undersampled k-space data rotated about a section of k-space. Each blade of undersampled k-space data includes first data points having acquired data and second data points that are missing data. The method also includes generating a set of reconstruction weights for the blades of undersampled k-space data using the blade of k-space calibration data; synthesizing k-space data for at least a portion of the second data points using the set of reconstruction weights; and generating a T1-weighted image using the T1-weighted k-space imaging data and the synthesized k-space data.