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Abstract #4793

Spatio-Temporal Undersampling Artefact Reduction with Neural Networks for Fast 2D Cine MRI with Limited Data

Andreas Kofler1, Marc Dewey1, Tobias Schaeffter2,3, Christian Wald1, and Christoph Kolbitsch2,3

1Department of Radiology, Charité - Universitätsmedizin Berlin, Berlin, Germany, 2Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany, 3School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom

A well-known bottleneck of neural networks is the requirement of large datasets for successful training. We present a method for reduction of 2D radial cine MRI images which allows to properly train a neural network on limited datasets. The network is trained on spatio-temporal slices of healthy volunteers which are previously extracted from the image sequences and is tested on patients data with known heart dysfunction. The image sequences are reassembled from the processed spatio-temporal slices. Our method is shown to have several advantages compared to other Deep Learning-based methods and achieves comparable results to a state-of-the-art Compressed Sensing-based method.

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