  Typical Magnetic Resonance Imaging (MRI) scan may take 20 to 60 minutes.
Reducing MRI scan time is beneficial for both patient experience and cost
considerations. Accelerated MRI scan may be achieved by acquiring less amount
of k-space data (down-sampling in the k-space). However, this leads to lower
resolution and aliasing artifacts for the reconstructed images. There are many
existing approaches for attempting to reconstruct high-quality images from
down-sampled k-space data, with varying complexity and performance. In recent
years, deep-learning approaches have been proposed for this task, and promising
results have been reported. Still, the problem remains challenging especially
because of the high fidelity requirement in most medical applications employing
reconstructed MRI images. In this work, we propose a deep-learning approach,
aiming at reconstructing high-quality images from accelerated MRI acquisition.
Specifically, we use Convolutional Neural Network (CNN) to learn the
differences between the aliased images and the original images, employing a
U-Net-like architecture. Further, a micro-architecture termed Residual Dense
Block (RDB) is introduced for learning a better feature representation than the
plain U-Net. Considering the peculiarity of the down-sampled k-space data, we
introduce a new term to the loss function in learning, which effectively
employs the given k-space data during training to provide additional
regularization on the update of the network weights. To evaluate the proposed
approach, we compare it with other state-of-the-art methods. In both visual
inspection and evaluation using standard metrics, the proposed approach is able
to deliver improved performance, demonstrating its potential for providing an
effective solution.
