A deep cascade of convolutional neural networks for MR image reconstruction

Schlemper J, Caballero J, Hajnal JV, Price A, Rueckert D (2017)

Publication Type: Conference contribution

Publication year: 2017

Journal

Lecture Notes in Computer Science Springer Verlag

Publisher: Springer Verlag

Book Volume: 10265 LNCS

Pages Range: 647-658

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Boone, NC, USA

ISBN: 9783319590493

DOI: 10.1007/978-3-319-59050-9_51

Abstract

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.

Involved external institutions

Imperial College London / The Imperial College of Science, Technology and Medicine GB United Kingdom (GB) King’s College London GB United Kingdom (GB)

How to cite

APA:

Schlemper, J., Caballero, J., Hajnal, J.V., Price, A., & Rueckert, D. (2017). A deep cascade of convolutional neural networks for MR image reconstruction. In Hongtu Zhu, Marc Niethammer, Martin Styner, Hongtu Zhu, Dinggang Shen, Pew-Thian Yap, Stephen Aylward, Ipek Oguz (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 647-658). Boone, NC, USA: Springer Verlag.

MLA:

Schlemper, Jo, et al. "A deep cascade of convolutional neural networks for MR image reconstruction." Proceedings of the 25th International Conference on Information Processing in Medical Imaging, IPMI 2017, Boone, NC, USA Ed. Hongtu Zhu, Marc Niethammer, Martin Styner, Hongtu Zhu, Dinggang Shen, Pew-Thian Yap, Stephen Aylward, Ipek Oguz, Springer Verlag, 2017. 647-658.

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