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Abstract #0954

Improvements of Image Quality of 1H and 129Xe MRI by Using an Advanced Acquisition and Reconstruction Method Coupled with Deep Learning

Samuel Perron1, Matthew S. Fox1,2, and Alexei Ouriadov1,2,3
1Physics and Astronomy, The University of Western Ontario, London, ON, Canada, 2Lawson Health Research Institute, London, ON, Canada, 3School of Biomedical Engineering, The University of Western Ontario, London, ON, Canada


Accelerated MRI has the potential to significantly improve image quality without increasing costs, especially for low field strengths. A series of undersampled images are averaged for every unique permutation, and their SNR dependency is fitted to the Stretched-Exponential-Model. The proposed method was implemented in proactively undersampled phantom images at low field (0.074T) and in retroactively undersampled human lung images at high field (3T) using the FGRE pulse sequence; in all cases, SNR was significantly improved within the same scan duration compared to a fully-sampled image. Reconstruction artefacts were minimized or completely removed using a convolutional neural network.

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