  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.
