Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstruction

TL;DR

This paper introduces a convolutional recurrent neural network that effectively reconstructs high-quality dynamic cardiac MRI images from undersampled data by leveraging temporal correlations and iterative reconstruction structures, outperforming existing methods.

Contribution

The paper presents a novel CRNN architecture that embeds traditional iterative algorithms and models spatiotemporal dependencies for improved MRI reconstruction.

Findings

Outperforms current MR reconstruction methods in accuracy.

Reduces computational complexity and reconstruction time.

Learns temporal dependencies with fewer parameters.

Abstract

Accelerating the data acquisition of dynamic magnetic resonance imaging (MRI) leads to a challenging ill-posed inverse problem, which has received great interest from both the signal processing and machine learning community over the last decades. The key ingredient to the problem is how to exploit the temporal correlation of the MR sequence to resolve the aliasing artefact. Traditionally, such observation led to a formulation of a non-convex optimisation problem, which were solved using iterative algorithms. Recently, however, deep learning based-approaches have gained significant popularity due to its ability to solve general inversion problems. In this work, we propose a unique, novel convolutional recurrent neural network (CRNN) 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 optimisation algorithms. In particular, the proposed architecture embeds the structure of the traditional iterative algorithms, efficiently modelling the recurrence of the iterative reconstruction stages by using recurrent hidden connections over such iterations. In addition, spatiotemporal dependencies are simultaneously learnt by exploiting bidirectional recurrent hidden connections across time sequences. The proposed algorithm is able to learn both the temporal dependency and the iterative reconstruction process effectively with only a very small number of parameters, while outperforming current MR reconstruction methods in terms of computational complexity, reconstruction accuracy and speed.