Abstract #0878

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

Orso Pusterla^1,2, Franesco Santini^1,2, Rahel Heule³, Damien Nguyen^1,2, Robin Sandkühler², Simon Andermatt², Pilippe C. Cattin², Corin Willers⁴, Sylvia Nyilas⁵, Philipp Latzin⁴, Oliver Bieri^1,2, and Grzegorz Bauman^1,2

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, Tübingen, 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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