Improved Denoising of Cryo-Electron Microscopy Micrographs with Simulation-Aware Pretraining

doi:10.1089/cmb.2024.0513

	Improved Denoising of Cryo-Electron Microscopy Micrographs with Simulation-Aware Pretraining
	Yang, Zhidong 1,3; Li, Hongjia 5; Zang, Dawei 1; Han, Renmin 4; Zhang, Fa 2
	2024-05-28
发表期刊	JOURNAL OF COMPUTATIONAL BIOLOGY
ISSN	1066-5277
页码	12
摘要	Cryo-electron microscopy (cryo-EM) has emerged as a potent technique for determining the structure and functionality of biological macromolecules. However, limited by the physical imaging conditions, such as low electron beam dose, micrographs in cryo-EM typically contend with an extremely low signal-to-noise ratio (SNR), impeding the efficiency and efficacy of subsequent analyses. Therefore, there is a growing demand for an efficient denoising algorithm designed for cryo-EM micrographs, aiming to enhance the quality of macromolecular analysis. However, owing to the absence of a comprehensive and well-defined dataset with ground truth images, supervised image denoising methods exhibit limited generalization when applied to experimental micrographs. To tackle this challenge, we introduce a simulation-aware image denoising (SaID) pretrained model designed to enhance the SNR of cryo-EM micrographs where the training is solely based on an accurately simulated dataset. First, we propose a parameter calibration algorithm for simulated dataset generation, aiming to align simulation parameters with those of experimental micrographs. Second, leveraging the accurately simulated dataset, we propose to train a deep general denoising model that can well generalize to real experimental cryo-EM micrographs. Comprehensive experimental results demonstrate that our pretrained denoising model achieves excellent denoising performance on experimental cryo-EM micrographs, significantly streamlining downstream analysis.
关键词	cryo-EM image denoising noise simulation and deep learning
DOI	10.1089/cmb.2024.0513
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61932018] ; National Natural Science Foundation of China[32241027] ; National Natural Science Foundation of China[62072280] ; National Natural Science Foundation of China[62072441] ; National Natural Science Foundation of China[61902373] ; National Key Research and Development Program of China[2021YFF0704300] ; Natural Science Foundation of Shandong Province[ZR2023YQ057] ; Youth Innovation Promotion Association CAS, the Foundation of the Chinese Academy of Sciences, China[JCPYJJ 22013] ; Strategic Priority Research Program of the Chinese Academy of Sciences, China[XDB24050300] ; Strategic Priority Research Program of the Chinese Academy of Sciences, China[XDB44030300]
WOS研究方向	Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Computer Science ; Mathematical & Computational Biology ; Mathematics
WOS类目	Biochemical Research Methods ; Biotechnology & Applied Microbiology ; Computer Science, Interdisciplinary Applications ; Mathematical & Computational Biology ; Statistics & Probability
WOS记录号	WOS:001234379300001
出版者	MARY ANN LIEBERT, INC
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/40027
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Han, Renmin; Zhang, Fa
作者单位	1.Chinese Acad Sci, Inst Comp Technol, High Performance Comp Res Ctr, Beijing, Peoples R China 2.Beijing Inst Technol, Sch Med Technol, Beijing 100081, Peoples R China 3.Univ Chinese Acad Sci, Beijing, Peoples R China 4.Shandong Univ, Res Ctr Math & Interdisciplinary Sci, Frontiers Sci Ctr Nonlinear Expectat, Minist Educ, Qingdao 266237, Peoples R China 5.Purdue Univ, Weldon Sch Biomed Engn, W Lafayette, IN USA
推荐引用方式 GB/T 7714	Yang, Zhidong,Li, Hongjia,Zang, Dawei,et al. Improved Denoising of Cryo-Electron Microscopy Micrographs with Simulation-Aware Pretraining[J]. JOURNAL OF COMPUTATIONAL BIOLOGY,2024:12.
APA	Yang, Zhidong,Li, Hongjia,Zang, Dawei,Han, Renmin,&Zhang, Fa.(2024).Improved Denoising of Cryo-Electron Microscopy Micrographs with Simulation-Aware Pretraining.JOURNAL OF COMPUTATIONAL BIOLOGY,12.
MLA	Yang, Zhidong,et al."Improved Denoising of Cryo-Electron Microscopy Micrographs with Simulation-Aware Pretraining".JOURNAL OF COMPUTATIONAL BIOLOGY (2024):12.