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Abstract #0878

Super-resolution convolutional neural networks applied to functional lung MRI at 1.5T

Orso Pusterla1,2, Franesco Santini1,2, Rahel Heule3, Damien Nguyen1,2, Robin Sandk├╝hler2, Simon Andermatt2, Pilippe C. Cattin2, Corin Willers4, Sylvia Nyilas5, Philipp Latzin4, Oliver Bieri1,2, and Grzegorz Bauman1,2

1Division of Radiological Physics, Department of Radiology, University of Basel, Basel, Switzerland, 2Department of Biomedical Engineering, University of Basel, Basel, Switzerland, 3High Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, T├╝bingen, Germany, 4Division of Pediatric Respiratory Medicine, Department of Pediatrics, Inselspital, Bern University Hospital, Bern, Switzerland, 5Department of Radiology, Inselspital, Bern University Hospital, Bern, Switzerland

High-resolution images are needed in many MR applications to enhance the diagnostic information at early stages of the disease. Often, the achievable resolution is limited by acquisition time constraints, in particular in moving organs such as the lung, where rapid imaging is a necessity. The low proton density in the lung parenchyma further constrains the resolution as sufficiently high signal-to-noise ratio (SNR) requires large voxel size. In this work, the concept of super-resolution is investigated to increase the spatial resolution and potentially shorten the acquisition time for functional assessment in the lung without SNR penalty.

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