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A deep cascade of convolutional neural networks for MR image reconstruction

Research output: Chapter in Book/Report/Conference proceedingOther chapter contribution

Jo Schlemper, Jose Caballero, Joseph V. Hajnal, Anthony Price, Daniel Rueckert

Original languageEnglish
Title of host publicationInformation Processing in Medical Imaging - 25th International Conference, IPMI 2017, Proceedings
PublisherSpringer Verlag
Number of pages12
Volume10265 LNCS
ISBN (Print)9783319590493
Publication statusPublished - 2017
Event25th International Conference on Information Processing in Medical Imaging, IPMI 2017 - Boone, United States
Duration: 25 Jun 201730 Jun 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10265 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349


Conference25th International Conference on Information Processing in Medical Imaging, IPMI 2017
CountryUnited States

King's Authors


The acquisition of Magnetic Resonance Imaging (MRI) is inherently slow. Inspired by recent advances in deep learning, we propose a framework for reconstructing MR images from undersampled data using a deep cascade of convolutional neural networks to accelerate the data acquisition process. We show that for Cartesian undersampling of 2D cardiac MR images, the proposed method outperforms the state-of-the-art compressed sensing approaches, such as dictionary learning-based MRI (DLMRI) reconstruction, in terms of reconstruction error, perceptual quality and reconstruction speed for both 3-fold and 6-fold undersampling. Compared to DLMRI, the error produced by the method proposed is approximately twice as small, allowing to preserve anatomical structures more faithfully. Using our method, each image can be reconstructed in 23 ms, which is fast enough to enable real-time applications.

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