Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstruction

By Chen Qin, Jo Schlemper, Jose Caballero, Anthony N. Price, Joseph V. Hajnal, Daniel Rueckert. 2017

569 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

An overview of deep learning in medical imaging focusing on MRI (2018, background): “convolutional recurrent neural networks for dynamic MR image reconstruction [134], 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 rec”
Small Sample Learning in Big Data Era (2018, methodology): “Along this line of research, various unfolded technique have been successfully applied into dynamic MR image reconstruction (Schlemper et al., 2018; Qin et al., 2017), sparse view/data Computed Tomography(CT) reconstruction (Gupta et al.”
Self‐supervised learning of physics‐guided reconstruction neural networks without fully sampled reference data (2019, methodology): “ visually outperformed CG-SENSE at R = 2 according to the reader study. Most DL-MRI approaches use supervised learning for network training in order to provide improved accelerated MRI reconstruction (28,29,32,33,59). However, acquiring fully-sampled data is challenging in many practical scenarios o”
Deep-Learning Methods for Parallel Magnetic Resonance Imaging Reconstruction: A Survey of the Current Approaches, Trends, and Issues (2019, background): “veloped speciﬁcally in the context of parallel imaging. Some architectures for image domain machine learning have been designed speciﬁ- cally towards a target application, for example dynamic imaging [62, 63]. In their current form, these were not yet demonstrated in the context of multi-coil data. ”
Deep Magnetic Resonance Image Reconstruction: Inverse Problems Meet Neural Networks (2020, unknown): “Deep-learning-based MRI reconstruction methods can be approximately categorized as unrolling-based approaches [5]–[12] and those not based on unrolling [13]–[28].”
Variable-Density Single-Shot Fast Spin-Echo MRI with Deep Learning Reconstruction by Using Variational Networks. (2018, background): “These approaches have been demonstrated to achieve faster reconstruction than conventional PICS while maintaining comparable image quality (11,15).”
Solving inverse problems using data-driven models (2019, background): “Quinto and O. Öktem (2008), ‘Local tomography in electron microscopy’, SIAM J.”