  In spite of its extensive adaptation in almost every medical diagnostic and
examinatorial application, Magnetic Resonance Imaging (MRI) is still a slow
imaging modality which limits its use for dynamic imaging. In recent years,
Parallel Imaging (PI) and Compressed Sensing (CS) have been utilised to
accelerate the MRI acquisition. In clinical settings, subsampling the k-space
measurements during scanning time using Cartesian trajectories, such as
rectilinear sampling, is currently the most conventional CS approach applied
which, however, is prone to producing aliased reconstructions. With the advent
of the involvement of Deep Learning (DL) in accelerating the MRI,
reconstructing faithful images from subsampled data became increasingly
promising. Retrospectively applying a subsampling mask onto the k-space data is
a way of simulating the accelerated acquisition of k-space data in real
clinical setting. In this paper we compare and provide a review for the effect
of applying either rectilinear or radial retrospective subsampling on the
quality of the reconstructions outputted by trained deep neural networks. With
the same choice of hyper-parameters, we train and evaluate two distinct
Recurrent Inference Machines (RIMs), one for each type of subsampling. The
qualitative and quantitative results of our experiments indicate that the model
trained on data with radial subsampling attains higher performance and learns
to estimate reconstructions with higher fidelity paving the way for other DL
approaches to involve radial subsampling.
