A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction

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Title: A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction
Authors: Schlemper, J
Caballero, J
Hajnal, J
Price, A
Rueckert, D
Item Type: Journal Article
Abstract: Inspired by recent advances in deep learning, we propose a framework for reconstructing dynamic sequences of 2D cardiac magnetic resonance (MR) images from undersampled data using a deep cascade of convolutional neural networks (CNNs) to accelerate the data acquisition process. In particular, we address the case where data is acquired using aggressive Cartesian undersampling. Firstly, we show that when each 2D image frame is reconstructed independently, the proposed method outperforms state-of-the-art 2D compressed sensing approaches such as dictionary learning-based MR image reconstruction, in terms of reconstruction error and reconstruction speed. Secondly, when reconstructing the frames of the sequences jointly, we demonstrate that CNNs can learn spatio-temporal correlations efficiently by combining convolution and data sharing approaches. We show that the proposed method consistently outperforms state-of-the-art methods and is capable of preserving anatomical structure more faithfully up to 11-fold undersampling. Moreover, reconstruction is very fast: each complete dynamic sequence can be reconstructed in less than 10s and, for the 2D case, each image frame can be reconstructed in 23ms, enabling real-time applications.
Issue Date: 13-Oct-2017
Date of Acceptance: 3-Oct-2017
URI: http://hdl.handle.net/10044/1/51669
DOI: https://dx.doi.org/10.1109/TMI.2017.2760978
ISSN: 0278-0062
Publisher: Institute of Electrical and Electronics Engineers
Start Page: 491
End Page: 503
Journal / Book Title: IEEE Transactions on Medical Imaging
Volume: 37
Issue: 2
Copyright Statement: This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/.
Sponsor/Funder: Engineering & Physical Science Research Council (EPSRC)
Funder's Grant Number: EP/P001009/1
Keywords: cs.CV
08 Information And Computing Sciences
09 Engineering
Nuclear Medicine & Medical Imaging
Publication Status: Published
Appears in Collections:Faculty of Engineering

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