  Magnetic resonance imaging (MRI) reconstruction is an active inverse problem
which can be addressed by conventional compressed sensing (CS) MRI algorithms
that exploit the sparse nature of MRI in an iterative optimization-based
manner. However, two main drawbacks of iterative optimization-based CSMRI
methods are time-consuming and are limited in model capacity. Meanwhile, one
main challenge for recent deep learning-based CSMRI is the trade-off between
model performance and network size. To address the above issues, we develop a
new multi-scale dilated network for MRI reconstruction with high speed and
outstanding performance. Comparing to convolutional kernels with same receptive
fields, dilated convolutions reduce network parameters with smaller kernels and
expand receptive fields of kernels to obtain almost same information. To
maintain the abundance of features, we present global and local residual
learnings to extract more image edges and details. Then we utilize
concatenation layers to fuse multi-scale features and residual learnings for
better reconstruction. Compared with several non-deep and deep learning CSMRI
algorithms, the proposed method yields better reconstruction accuracy and
noticeable visual improvements. In addition, we perform the noisy setting to
verify the model stability, and then extend the proposed model on a MRI
super-resolution task.
