  The accurate reconstruction of under-sampled magnetic resonance imaging (MRI)
data using modern deep learning technology, requires significant effort to
design the necessary complex neural network architectures. The cascaded network
architecture for MRI reconstruction has been widely used, while it suffers from
the "vanishing gradient" problem when the network becomes deep. In addition,
homogeneous architecture degrades the representation capacity of the network.
In this work, we present an enhanced MRI reconstruction network using a
residual in residual basic block. For each cell in the basic block, we use the
differentiable neural architecture search (NAS) technique to automatically
choose the optimal operation among eight variants of the dense block. This new
heterogeneous network is evaluated on two publicly available datasets and
outperforms all current state-of-the-art methods, which demonstrates the
effectiveness of our proposed method.
