BRAIN MRI SUPER RESOLUTION USING 3D DEEP DENSELY CONNECTED NEURAL NETWORKS

被引：0

作者：

Chen, Yuhua ^{[1
,2
]}

Xie, Yibin ^{[2
]}

Zhou, Zhengwei ^{[1
,2
]}

Shi, Feng ^{[2
]}

Christodoulou, Anthony G. ^{[2
]}

Li, Debiao ^{[1
,2
]}

机构：

[1] Univ Calif Los Angeles, Dept Bioengn, Los Angeles, CA 90095 USA

[2] Cedars Sinai Med Ctr, Biomed Image Res Inst, Los Angeles, CA 90048 USA

来源：

2018 IEEE 15TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI 2018) | 2018年

关键词：

Super-resolution; MRI; deep learning; 3D Neural Network; image enhancement; IMAGE SUPERRESOLUTION;

D O I：

暂无

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

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.

引用

页码：739 / 742

页数：4

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