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Abstract #1056

Accelerating MR Elastography using Deep Learning-Reconstruction of Undersampled Data

Robin Antony Birkeland Bugge1, Jon Andre Ottesen2, Elies Fuster3, Atle Bjørnerud2, and Kyrre Eeg Emblem1
1Department of Diagnostic Physics, Oslo University Hospital, Oslo, Norway, 2Department of Computational Radiology and Artificial Intelligence, Oslo University Hospital, Oslo, Norway, 3Biomedical Data Science Laboratory, Instituto Universitario de Tecnologías de la Información y Comunicaciones, Universitat Politècnica de València, Valencia, Spain


Problem summary: Brain MR elastography is associated with extended acquisition times, which is alleviated by reduced coverage or resolution. The aim of this project is to utilize deep learning to accelerate MRE. Methods: We employ an MRE for fully sampled acquisition. Undersampled data is simulated by masking phase-encoding steps. A cascaded reconstruction network is used to reconstruct the phase image from undersampled k-space. Results: There are subtle differences between the reconstructed and fully sampled phase images. We observe a non-significant difference for stiffness values in our preliminary results. Conclusions: The method shows promise for accelerating MR elastography data.

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