A deep cascade of convolutional neural networks for dynamic MR image reconstruction
File(s)
Published version
Author(s)
Schlemper, J
Caballero, J
Hajnal, J
Price, A
Rueckert, D
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.
Date Issued
2017-10-13
Date Acceptance
2017-10-03
Citation
IEEE Transactions on Medical Imaging, 2017, 37 (2), pp.491-503
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/.
License URL
Sponsor
Engineering & Physical Science Research Council (EPSRC)
Grant Number
EP/P001009/1
Subjects
Science & Technology
Technology
Life Sciences & Biomedicine
Computer Science, Interdisciplinary Applications
Engineering, Biomedical
Engineering, Electrical & Electronic
Imaging Science & Photographic Technology
Radiology, Nuclear Medicine & Medical Imaging
Computer Science
Engineering
Deep learning
convolutional neural network
dynamic magnetic resonance imaging
compressed sensing
image reconstruction
SENSE
SPARSITY
Algorithms
Databases, Factual
Heart
Humans
Image Processing, Computer-Assisted
Magnetic Resonance Imaging, Cine
Neural Networks, Computer
Heart
Humans
Magnetic Resonance Imaging, Cine
Algorithms
Neural Networks (Computer)
Image Processing, Computer-Assisted
Databases, Factual
cs.CV
cs.CV
Nuclear Medicine & Medical Imaging
08 Information and Computing Sciences
09 Engineering
Publication Status
Published
Date Publish Online
2017-10-13