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Motion-Informed locally low-rank 5D Flow MRI
In Flow MRI, image artifacts mainly result from cardiac and respiratory motion, causing blurring or ghosting. CINE imaging addresses cardiac motion by acquiring data throughout the cardiac cycle. To tackle respiratory motion, traditional methods involved measuring respiratory signals and accepting data within a limited respiratory motion range, at the cost of reduced scan efficiency and increased acquisition time. Newer approaches record data in a free breathing manner and use self-navigation to organize it into bins, improving efficiency and reducing acquisition time.
Low rank priors are a cutting-edge technique in dynamic MR image reconstruction, and recent research by Hoh et al. has shown that incorporating motion information into locally low rank (LLR) reconstruction (MI-LLR) between bins can improve reconstructions for free breathing 3D cardiac perfusion MRI.
The aim of this project is to investigate the benefit of using MI-LLR reconstructions on Flow data.
In 4D Flow MRI respiratory gating or triggering is required to suppress respiratory motion. Recent work has aimed at resolving rather than suppressing respiratory motion and hence has been referred to as 5D Flow MRI. To achieve image reconstruction from undersampled data, data correlations along space, cardiac and respiratory phases have been exploited by favouring low-rank representations. However, it has been shown that temporal smoothing of high fidelity image features can occur leading to temporal low-pass filtering of velocity data. To address the issue, low-rank properties have been refined by determining and incorporating transformations between motion states.
In 4D Flow MRI respiratory gating or triggering is required to suppress respiratory motion. Recent work has aimed at resolving rather than suppressing respiratory motion and hence has been referred to as 5D Flow MRI. To achieve image reconstruction from undersampled data, data correlations along space, cardiac and respiratory phases have been exploited by favouring low-rank representations. However, it has been shown that temporal smoothing of high fidelity image features can occur leading to temporal low-pass filtering of velocity data. To address the issue, low-rank properties have been refined by determining and incorporating transformations between motion states.
• Investigate and report on the accuracy of our current self-gating method
o Coil clustering
o Automatic coil detection
o Limited field-of-view
• Investigate and report on the motion-informed LLR algorithm compare to LLR baseline
o Motion artifacts reduction
o Underestimation of peak velocities
o Potential turbulence estimation benefits
• Investigate and report on the accuracy of our current self-gating method
o Coil clustering
o Automatic coil detection
o Limited field-of-view
• Investigate and report on the motion-informed LLR algorithm compare to LLR baseline
o Motion artifacts reduction
o Underestimation of peak velocities
o Potential turbulence estimation benefits
Supervisors: Sébastien Emery (emery@biomed.ee.ethz.ch); Prof. Dr. Sebastian (kozerke@biomed.ee.ethz.ch).
Supervisors: Sébastien Emery (emery@biomed.ee.ethz.ch); Prof. Dr. Sebastian (kozerke@biomed.ee.ethz.ch).