Deep robust residual network for super-resolution of 2D fetal brain MRI | |
Song, Liyao1; Wang, Quan2; Liu, Ting3; Li, Haiwei2; Fan, Jiancun1; Yang, Jian3; Hu, Bingliang2 | |
作者部门 | 光谱成像技术研究室 |
2022-01-10 | |
发表期刊 | SCIENTIFIC REPORTS |
ISSN | 2045-2322 |
卷号 | 12期号:1 |
产权排序 | 2 |
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. |
DOI | 10.1038/s41598-021-03979-1 |
收录类别 | SCI |
语种 | 英语 |
WOS记录号 | WOS:000741645800091 |
出版者 | HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.opt.ac.cn/handle/181661/95692 |
专题 | 光谱成像技术研究室 |
通讯作者 | Fan, Jiancun; Yang, Jian; Hu, Bingliang |
作者单位 | 1.Xi An Jiao Tong Univ, Sch Informat & Commun Engn, Xian 710049, Peoples R China 2.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Xian 710049, Peoples R China 3.Xi An Jiao Tong Univ, Affiliated Hosp 1, Xian 710061, 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). |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Deep robust residual(1339KB) | 期刊论文 | 出版稿 | 限制开放 | CC BY-NC-SA | 请求全文 |
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