MRI Super-Resolution with GAN and 3D Multi-Level DenseNet: Smaller, Faster, and Better

• URL: http://arxiv.org/abs/2003.01217v2
• Date: Fri, 6 Mar 2020 23:46:42 GMT
• Title: MRI Super-Resolution with GAN and 3D Multi-Level DenseNet: Smaller, Faster, and Better
• Authors: Yuhua Chen, Anthony G. Christodoulou, Zhengwei Zhou, Feng Shi, Yibin Xie, Debiao Li
• Abstract summary: High-resolution (HR) magnetic resonance imaging (MRI) provides detailed anatomical information critical for diagnosis in the clinical application. HR MRI typically comes at the cost of long scan time, small spatial coverage, and low signal-to-noise ratio (SNR) Recent studies showed that with a deep convolutional neural network (CNN), HR generic images could be recovered from low-resolution (LR) inputs via single image super-resolution (SISR) approaches.
• Score: 16.65044022241517
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: High-resolution (HR) magnetic resonance imaging (MRI) provides detailed anatomical information that is critical for diagnosis in the clinical application. However, HR MRI typically comes at the cost of long scan time, small spatial coverage, and low signal-to-noise ratio (SNR). Recent studies showed that with a deep convolutional neural network (CNN), HR generic images could be recovered from low-resolution (LR) inputs via single image super-resolution (SISR) approaches. Additionally, previous works have shown that a deep 3D CNN can generate high-quality SR MRIs by using learned image priors. However, 3D CNN with deep structures, have a large number of parameters and are computationally expensive. In this paper, we propose a novel 3D CNN architecture, namely a multi-level densely connected super-resolution network (mDCSRN), which is light-weight, fast and accurate. We also show that with the generative adversarial network (GAN)-guided training, the mDCSRN-GAN provides appealing sharp SR images with rich texture details that are highly comparable with the referenced HR images. Our results from experiments on a large public dataset with 1,113 subjects showed that this new architecture outperformed other popular deep learning methods in recovering 4x resolution-downgraded images in both quality and speed.

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