Convolutional recurrent neural networks for dynamic MR image reconstruction

Qin C, Schlemper J, Caballero J, Price AN, Hajnal JV, Rueckert D (2019)

Publication Type: Journal article

Publication year: 2019

Journal

IEEE Transactions on Medical Imaging Institute of Electrical and Electronics Engineers (IEEE)

Book Volume: 38

Pages Range: 280-290

Article Number: 8425639

Journal Issue: 1

DOI: 10.1109/TMI.2018.2863670

Abstract

Accelerating the data acquisition of dynamic magnetic resonance imaging leads to a challenging ill-posed inverse problem, which has received great interest from both the signal processing and machine learning communities over the last decades. The key ingredient to the problem is how to exploit the temporal correlations of the MR sequence to resolve aliasing artifacts. Traditionally, such observation led to a formulation of an optimization problem, which was solved using iterative algorithms. Recently, however, deep learning-based approaches have gained significant popularity due to their ability to solve general inverse problems. In this paper, we propose a unique, novel convolutional recurrent neural network architecture which reconstructs high quality cardiac MR images from highly undersampled k-space data by jointly exploiting the dependencies of the temporal sequences as well as the iterative nature of the traditional optimization algorithms. In particular, the proposed architecture embeds the structure of the traditional iterative algorithms, efficiently modeling the recurrence of the iterative reconstruction stages by using recurrent hidden connections over such iterations. In addition, spatio-temporal dependencies are simultaneously learnt by exploiting bidirectional recurrent hidden connections across time sequences. The proposed method is able to learn both the temporal dependence and the iterative reconstruction process effectively with only a very small number of parameters, while outperforming current MR reconstruction methods in terms of reconstruction accuracy and speed.

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:

Qin, C., Schlemper, J., Caballero, J., Price, A.N., Hajnal, J.V., & Rueckert, D. (2019). Convolutional recurrent neural networks for dynamic MR image reconstruction. IEEE Transactions on Medical Imaging, 38(1), 280-290. https://doi.org/10.1109/TMI.2018.2863670

MLA:

Qin, Chen, et al. "Convolutional recurrent neural networks for dynamic MR image reconstruction." IEEE Transactions on Medical Imaging 38.1 (2019): 280-290.

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