A deep cascade of convolutional neural networks for dynamic MR image reconstruction
Open accessAbstract
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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Convolutional recurrent neural networks for dynamic MR image reconstruction reconstruct good quality cardiac MR images from highly undersampled complex-valued k-space data by learning spatio-temporal dependencies, outperforming 3D CNN approaches and compressed sensing-based dynamic MRI reconstruction algorithms in computational complexity, reconstruction accuracy and speed for different undersam-
“convolutional recurrent neural networks for dynamic MR image reconstruction [135], reconstructing good quality cardiac MR images from highly undersampled complex-valued k -space data by learning spatio-temporal dependencies, outperforming 3D CNN approaches and compressed sensing-based dynamic MRI reconstruction algorithms in computational complexity, reconstruction accuracy and speed for different”
From An overview of deep learning in medical imaging focusing on MRI (2019) · cites this paper as [135]
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Self-citation
- Authors
- Schlemper, J., Caballero, J., Hajnal, J. V., Price, A. N., & Rueckert, D.
- Affiliations
- Imperial College London (GB)
- King's College London (GB)
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2018-02
- Volume
- 37
- Issue
- 2
- Pages
- 491-503
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- 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 · 2017: 5 · 2016: 1
- FWCI
- 78.50
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- 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 › Engineering › Computational Mechanics › 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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