Cited by 1,338Advanced MRI Techniques and ApplicationsSparse and Compressive Sensing TechniquesImage and Signal Denoising Methods

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

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Schlemper, Jo, Caballero, Jose, Hajnal, Joseph V., Price, Anthony N., Rueckert, Daniel·2018·IEEE Transactions on Medical Imaging

DOI 10.1109/tmi.2017.2760978

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Abstract

Inspired by recent advances in deep learning, we propose a framework for reconstructing dynamic sequences of 2-D 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 are acquired using aggressive Cartesian undersampling. First, we show that when each 2-D image frame is reconstructed independently, the proposed method outperforms state-of-the-art 2-D compressed sensing approaches, such as dictionary learning-based MR image reconstruction, in terms of reconstruction error and reconstruction speed. Second, 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 10 s and, for the 2-D case, each image frame can be reconstructed in 23 ms, enabling real-time applications.

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Dual-domain faster Fourier convolution based network for MR image reconstruction
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Ji, S., Jeong, J., Oh, S.-H., Nam, Y., Choi, S. H., Shin, H., Shin, D., Jung, W., & Lee, J. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2021.3093617 · Cited by 9
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Authors: Schlemper, J., Caballero, J., Hajnal, J. V., Price, A. N., & Rueckert, D.
Affiliations: Imperial College London (GB)
King's College London (GB)
ORCID: Schlemper, Jo
Caballero, Jose
Hajnal, Joseph V.
Price, Anthony N.
Rueckert, Daniel
Venue: IEEE Transactions on Medical Imaging
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2018-02
Volume: 37
Issue: 2
Pages: 491-503
ISSN: 0278-0062 (ISSN-L)
1558-254X
DOI: 10.1109/tmi.2017.2760978
OpenAlex: W2594014149
Cited by: 1,338
References: 41
Citations by year: 2026: 66 · 2025: 164 · 2024: 171 · 2023: 147 · 2022: 199 · 2021: 217 · 2020: 182 · 2019: 132 · 2018: 54
FWCI: 78.50
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by
creativecommons.org/licenses/by/3.0/legalcode (vor)
Locations: IEEE Transactions on Medical Imaging (publishedVersion, cc-by)
PubMed (publishedVersion)
arXiv (Cornell University) (submittedVersion)
Research Portal (King's College London) (submittedVersion, cc-by)
Spiral (Imperial College London) (submittedVersion, cc-by)
arXiv (Cornell University) (cc-by)
Funders: EPSRC Programme (EP/P001009/1)
Topics: Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
Physical Sciences › Computer Science › Signal Processing › Sparse and Compressive Sensing Techniques
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Signal Denoising Methods
Keywords: MRI reconstruction, compressed sensing, spatio-temporal correlation, real-time reconstruction
MeSH terms: Algorithms, Heart, Humans, Image Processing, Computer-Assisted, Databases, Factual, Neural Networks, Computer, Magnetic Resonance Imaging, Cine

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

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