  Magnetic resonance imaging (MRI) is one of the noninvasive imaging modalities
that can produce high-quality images. However, the scan procedure is relatively
slow, which causes patient discomfort and motion artifacts in images.
Accelerating MRI hardware is constrained by physical and physiological
limitations. A popular alternative approach to accelerated MRI is to
undersample the k-space data. While undersampling speeds up the scan procedure,
it generates artifacts in the images, and advanced reconstruction algorithms
are needed to produce artifact-free images. Recently deep learning has emerged
as a promising MRI reconstruction method to address this problem. However,
straightforward adoption of the existing deep learning neural network
architectures in MRI reconstructions is not usually optimal in terms of
efficiency and reconstruction quality. In this work, MRI reconstruction from
undersampled data was carried out using an optimized neural network using a
novel evolutionary neural architecture search algorithm. Brain and knee MRI
datasets show that the proposed algorithm outperforms manually designed neural
network-based MR reconstruction models.
