One-dimensional Deep Low-rank and Sparse Network for Accelerated MRI

TL;DR

This paper introduces ODLS, a 1D deep learning network for accelerated MRI that is easier to train, more robust, and outperforms existing methods in limited data scenarios.

Contribution

The paper proposes a novel 1D convolution-based deep network, ODLS, unrolling a low-rank and sparse reconstruction model for improved MRI image reconstruction.

Findings

ODLS outperforms state-of-the-art methods visually and quantitatively.

ODLS is robust to different undersampling scenarios.

ODLS is memory-efficient and suitable for limited training data.

Abstract

Deep learning has shown astonishing performance in accelerated magnetic resonance imaging (MRI). Most state-of-the-art deep learning reconstructions adopt the powerful convolutional neural network and perform 2D convolution since many magnetic resonance images or their corresponding k-space are in 2D. In this work, we present a new approach that explores the 1D convolution, making the deep network much easier to be trained and generalized. We further integrate the 1D convolution into the proposed deep network, named as One-dimensional Deep Low-rank and Sparse network (ODLS), which unrolls the iteration procedure of a low-rank and sparse reconstruction model. Extensive results on in vivo knee and brain datasets demonstrate that, the proposed ODLS is very suitable for the case of limited training subjects and provides improved reconstruction performance than state-of-the-art methods both visually and quantitatively. Additionally, ODLS also shows nice robustness to different undersampling scenarios and some mismatches between the training and test data. In summary, our work demonstrates that the 1D deep learning scheme is memory-efficient and robust in fast MRI.