392 research outputs found
Efficient and Accurate MRI Super-Resolution using a Generative Adversarial Network and 3D Multi-Level Densely Connected Network
High-resolution (HR) magnetic resonance images (MRI) provide detailed
anatomical information important for clinical application and quantitative
image analysis. However, HR MRI conventionally comes at the cost of longer scan
time, smaller spatial coverage, and lower signal-to-noise ratio (SNR). Recent
studies have shown that single image super-resolution (SISR), a technique to
recover HR details from one single low-resolution (LR) input image, could
provide high-quality image details with the help of advanced deep convolutional
neural networks (CNN). However, deep neural networks consume memory heavily and
run slowly, especially in 3D settings. In this paper, we propose a novel 3D
neural network design, namely a multi-level densely connected super-resolution
network (mDCSRN) with generative adversarial network (GAN)-guided training. The
mDCSRN quickly trains and inferences and the GAN promotes realistic output
hardly distinguishable from original HR images. Our results from experiments on
a dataset with 1,113 subjects show that our new architecture beats other
popular deep learning methods in recovering 4x resolution-downgraded im-ages
and runs 6x faster.Comment: 10 pages, 2 figures, 2 tables. MICCAI 201
Unsupervised MRI Super-Resolution Using Deep External Learning and Guided Residual Dense Network with Multimodal Image Priors
Deep learning techniques have led to state-of-the-art single image
super-resolution (SISR) with natural images. Pairs of high-resolution (HR) and
low-resolution (LR) images are used to train the deep learning model (mapping
function). These techniques have also been applied to medical image
super-resolution (SR). Compared with natural images, medical images have
several unique characteristics. First, there are no HR images for training in
real clinical applications because of the limitations of imaging systems and
clinical requirements. Second, other modal HR images are available (e.g., HR
T1-weighted images are available for enhancing LR T2-weighted images). In this
paper, we propose an unsupervised SISR technique based on simple prior
knowledge of the human anatomy; this technique does not require HR images for
training. Furthermore, we present a guided residual dense network, which
incorporates a residual dense network with a guided deep convolutional neural
network for enhancing the resolution of LR images by referring to different HR
images of the same subject. Experiments on a publicly available brain MRI
database showed that our proposed method achieves better performance than the
state-of-the-art methods.Comment: 10 pages, 3 figure
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