Deep robust residual network for super-resolution of 2D fetal brain MRI | |
Song, Liyao3; Wang, Quan2; Liu, Ting1; Li, Haiwei2; Fan, Jiancun3; Yang, Jian1; Hu, Bingliang2 | |
刊名 | SCIENTIFIC REPORTS |
2022-01-10 | |
卷号 | 12期号:1 |
ISSN号 | 2045-2322 |
DOI | 10.1038/s41598-021-03979-1 |
产权排序 | 2 |
英文摘要 | Spatial resolution is a key factor of quantitatively evaluating the quality of magnetic resonance imagery (MRI). Super-resolution (SR) approaches can improve its spatial resolution by reconstructing high-resolution (HR) images from low-resolution (LR) ones to meet clinical and scientific requirements. To increase the quality of brain MRI, we study a robust residual-learning SR network (RRLSRN) to generate a sharp HR brain image from an LR input. Due to the Charbonnier loss can handle outliers well, and Gradient Difference Loss (GDL) can sharpen an image, we combined the Charbonnier loss and GDL to improve the robustness of the model and enhance the texture information of SR results. Two MRI datasets of adult brain, Kirby 21 and NAMIC, were used to train and verify the effectiveness of our model. To further verify the generalizability and robustness of the proposed model, we collected eight clinical fetal brain MRI 2D data for evaluation. The experimental results have shown that the proposed deep residual-learning network achieved superior performance and high efficiency over other compared methods. |
语种 | 英语 |
出版者 | HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY |
WOS记录号 | WOS:000741645800091 |
内容类型 | 期刊论文 |
源URL | [http://ir.opt.ac.cn/handle/181661/95692] |
专题 | 西安光学精密机械研究所_光学影像学习与分析中心 |
通讯作者 | Fan, Jiancun; Yang, Jian; Hu, Bingliang |
作者单位 | 1.Xi An Jiao Tong Univ, Affiliated Hosp 1, Xian 710061, Peoples R China 2.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Xian 710049, Peoples R China 3.Xi An Jiao Tong Univ, Sch Informat & Commun Engn, Xian 710049, Peoples R China |
推荐引用方式 GB/T 7714 | Song, Liyao,Wang, Quan,Liu, Ting,et al. Deep robust residual network for super-resolution of 2D fetal brain MRI[J]. SCIENTIFIC REPORTS,2022,12(1). |
APA | Song, Liyao.,Wang, Quan.,Liu, Ting.,Li, Haiwei.,Fan, Jiancun.,...&Hu, Bingliang.(2022).Deep robust residual network for super-resolution of 2D fetal brain MRI.SCIENTIFIC REPORTS,12(1). |
MLA | Song, Liyao,et al."Deep robust residual network for super-resolution of 2D fetal brain MRI".SCIENTIFIC REPORTS 12.1(2022). |
个性服务 |
查看访问统计 |
相关权益政策 |
暂无数据 |
收藏/分享 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论