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Abstract #0643

A Deep Cascade of Convolutional Neural Networks for MR Image Reconstruction

Jo Schlemper1, Jose Caballero, Joseph V. Hajnal2, Anthony Price2, and Daniel Rueckert3

1Department of Computing, Imperial College London, London, United Kingdom, 2King's College London, 3Imperial College London

The acquisition of Magnetic Resonance Imaging (MRI) is inherently slow. Inspired by recent advances in deep learning, we propose a framework for reconstructing MRI images from undersampled data using a deep cascade of convolutional neural networks. We show, for Cartesian undersampling of 2D cardiac MR images, the proposed deep learning reconstruction method outperforms the state-of-the-art compressed sensing approaches, such as dictionary learning-based MRI (DLMRI) reconstruction, both in terms of reconstruction error, the perceptual quality and the reconstruction speed for 4-fold and 8-fold undersampling.

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