Quantitative susceptibility mapping via deep neural networks with iterative reverse concatenations and recurrent modules.

导出 LinkOut

翻译打印收藏纠错

第一作者： Min,Li

第一单位： School of Computer Science and Engineering, Central South University, Changsha, China.

作者： Min,Li ^[1] ; Chen,Chen ^[1] ; Zhuang,Xiong ^[2] ; Yin,Liu ^[3] ; Pengfei,Rong ^[3] ; Shanshan,Shan ^[4] ; Feng,Liu ^[2] ; Hongfu,Sun ^[5] ; Yang,Gao ^[1]

作者单位： School of Computer Science and Engineering, Central South University, Changsha, China. ^[1] School of Electrical Engineering and Computer Science, University of Queensland, Brisbane, Australia. ^[2] Department of Radiology, The Third Xiangya Hospital, Central South University, Changsha, China. ^[3] State Key Laboratory of Radiation, Medicine and Protection, School for Radiological and Interdisciplinary Sciences (RAD-X), Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou, China. ^[4] School of Engineering, University of Newcastle, Newcastle, Australia. ^[5]

关键词 IR2QSM deep learning dipole inversion quantitative susceptibility mapping recurrent module reverse concatenation

DOI 10.1002/mp.17747

PMID 40089979

发布时间 2025-03-16

翻译满意度评价：

准确一般字义偏差语意偏离译文作用小提交

浏览0

Medical physics

相似文献

中文期刊
外文期刊
学位论文
会议论文

检索历史清除

Quantitative susceptibility mapping via deep neural networks with iterative reverse concatenations and recurrent modules.

Medical physics

相似文献

Medical physics

相关期刊

参考文献：
指定格式：	NoteExpress EndNote RefWorks NoteFirst

检索历史 清除

Quantitative susceptibility mapping via deep neural networks with iterative reverse concatenations and recurrent modules.

Medical physics

相似文献

Medical physics

相关期刊

检索历史清除