Brain MRI Super Resolution Using 3D Deep Densely Connected Neural Networks

• Published in: arXiv.org
• Main Authors: Chen, Yuhua, Xie, Yibin, Zhou, Zhengwei, Shi, Feng, Christodoulou, Anthony G, Li, Debiao
• Format: Paper Journal Article
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 08.01.2018
• Subjects: Brain; Computer Science - Computer Vision and Pattern Recognition; Image resolution; Image restoration; Interpolation; Machine learning; Magnetic resonance imaging; Medical imaging; Neural networks; Quantitative analysis; Signal to noise ratio; Spatial resolution
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.1801.02728

Magnetic resonance image (MRI) in high spatial resolution provides detailed anatomical information and is often necessary for accurate quantitative analysis. However, high spatial resolution typically comes at the expense of longer scan time, less spatial coverage, and lower signal to noise ratio (SNR). Single Image Super-Resolution (SISR), a technique aimed to restore high-resolution (HR) details from one single low-resolution (LR) input image, has been improved dramatically by recent breakthroughs in deep learning. In this paper, we introduce a new neural network architecture, 3D Densely Connected Super-Resolution Networks (DCSRN) to restore HR features of structural brain MR images. Through experiments on a dataset with 1,113 subjects, we demonstrate that our network outperforms bicubic interpolation as well as other deep learning methods in restoring 4x resolution-reduced images.