2 research outputs found
Deep Residual Dense U-Net for Resolution Enhancement in Accelerated MRI Acquisition
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.Comment: SPIE Medical Imaging 201
Deep learning for fast MR imaging: a review for learning reconstruction from incomplete k-space data
Magnetic resonance imaging is a powerful imaging modality that can provide
versatile information but it has a bottleneck problem "slow imaging speed".
Reducing the scanned measurements can accelerate MR imaging with the aid of
powerful reconstruction methods, which have evolved from linear analytic models
to nonlinear iterative ones. The emerging trend in this area is replacing
human-defined signal models with that learned from data. Specifically, from
2016, deep learning has been incorporated into the fast MR imaging task, which
draws valuable prior knowledge from big datasets to facilitate accurate MR
image reconstruction from limited measurements. This survey aims to review deep
learning based MR image reconstruction works from 2016- June 2020 and will
discuss merits, limitations and challenges associated with such methods. Last
but not least, this paper will provide a starting point for researchers
interested in contributing to this field by pointing out good tutorial
resources, state-of-the-art open-source codes and meaningful data sources.Comment: Invited review submitted to Biomedical signal processing and control
in Jan 202