Self-Supervised High-Dimentional Magnetic Resonance Image Denoising Using Super-Resolved Single Noisy Image

doi:10.1109/ISBI53787.2023.10230690

	Self-Supervised High-Dimentional Magnetic Resonance Image Denoising Using Super-Resolved Single Noisy Image
	Jiang, Changhao1 ; Tian, Xuanyu1 ; Li, Yanbin 3; Wu, Jiangjie1 ; Mu, Xin2 ; Zhang, Lei2 ; Zhang, Yuyao1
	2023-04-18
会议录名称	2023 IEEE 20TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI)
ISSN	1945-7928
卷号	2023-April
发表状态	已发表
DOI	10.1109/ISBI53787.2023.10230690
摘要	Denoising of magnetic resonance image (MRI) is a critical step in MRI image processing and analysis. With the advantage of not requiring paired noisy-clean images for training, self-supervised denoising methods are emerging as competitive alternatives to supervised denoising methods in MRI denoising. However, current self-supervised image denoising methods are not effective enough for MRI. In this work, we propose Noise2SR-M (N2SR-M), a self supervised denoising method for MR images, which is more efficient for high-dimensional MR images. N2SR-M is designed for training with paired noisy data of different sizes divided from a single high-dimensional noisy input image. Our N2SR-M model is able to utilize the redundant information from the additional image dimension to generate noisy image pairs for the denoising task. With the combination of additional dimension constraint and the effectiveness of SR method based training image pair generation, our model is more efficient for denoising high-dimensional MR images. The quantitative and qualitative improvements in blood oxygenation level dependent (BOLD) imaging denoising task demonstrate that N2SR-M successfully restores detailed image contents and removes tiny structural noise and artifacts from noise-corrupted high-dimensional MRI. Moreover, the denoised BOLD image also induces more efficient R2∗ image computation. © 2023 IEEE.
会议录编者/会议主办者	Flywheel ; Kitware ; Siemens Healthineers ; UCLouvain
关键词	Self-supervised MRI Denoising BOLD
会议名称	20th IEEE International Symposium on Biomedical Imaging, ISBI 2023
出版地	345 E 47TH ST, NEW YORK, NY 10017 USA
会议地点	Cartagena, Colombia
会议日期	18-21 April 2023
URL	查看原文
收录类别	EI ; CPCI-S
语种	英语
资助项目	National Natural Science Foundation of China["62071299","61901256","91949120"]
WOS研究方向	Computer Science ; Engineering ; Radiology, Nuclear Medicine & Medical Imaging
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Biomedical ; Radiology, Nuclear Medicine & Medical Imaging
WOS记录号	WOS:001062050500367
出版者	IEEE Computer Society
EI入藏号	20233914806138
EI主题词	Image denoising
EISSN	1945-8452
EI分类号	701.2 Magnetism: Basic Concepts and Phenomena ; 716.1 Information Theory and Signal Processing ; 723.2 Data Processing and Image Processing ; 746 Imaging Techniques
原始文献类型	Conference article (CA)
来源库	IEEE
引用统计	正在获取...
文献类型	会议论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/333439
专题	信息科学与技术学院信息科学与技术学院_硕士生信息科学与技术学院_博士生信息科学与技术学院_PI研究组_张玉瑶组生物医学工程学院生物医学工程学院_PI研究组_张雷组（生医工）
通讯作者	Jiang, Changhao
作者单位	1.ShanghaiTech Univ, Sch Informat Sci & Technol, Shanghai, Peoples R China 2.ShanghaiTech Univ, Sch Biomed Engn, Shanghai, Peoples R China 3.Shanghai United Imaging Intelligence Co Ltd, Shanghai, Peoples R China
第一作者单位	信息科学与技术学院
通讯作者单位	信息科学与技术学院
第一作者的第一单位	信息科学与技术学院
推荐引用方式 GB/T 7714	Jiang, Changhao,Tian, Xuanyu,Li, Yanbin,et al. Self-Supervised High-Dimentional Magnetic Resonance Image Denoising Using Super-Resolved Single Noisy Image[C]//Flywheel, Kitware, Siemens Healthineers, UCLouvain. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE Computer Society,2023.