ArticleScientific reports2018
Exploring an optimal wavelet-based filter for cryo-ET imaging.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Stratified prediction of HER2 status in breast cancer by integrating intratumoral and peritumoral radiomics from DCE-MRI.BMC cancer · 2026Article
- DUAL: deep unsupervised simultaneous simulation and denoising for cryo-electron tomography.bioRxiv : the preprint server for biology · 2024Article
- Cryo-electron tomography of intact cardiac muscle reveals myosin binding protein-C linking myosin and actin filaments.Journal of muscle research and cell motility · 2023Article
- Spaghetti Tracer: A Framework for Tracing Semiregular Filamentous Densities in 3D Tomograms.Biomolecules · 2022Article
- Entropy-regularized deconvolution of cellular cryotransmission electron tomograms.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- [Progress in filters for denoising cryo-electron microscopy images].Beijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences · 2021Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cryo-electron tomography (cryo-ET) is one of the most advanced technologies for the in situ visualization of molecular machines by producing three-dimensional (3D) biological structures. However, cryo-ET imaging has two serious disadvantages-low dose and low image contrast-which result in high-resolution information being obscured by noise and image quality being degraded, and this causes errors in biological interpretation. The purpose of this research is to explore an optimal wavelet denoising technique to reduce noise in cryo-ET images. We perform tests using simulation data and design a filter using the optimum selected wavelet parameters (three-level decomposition, level-1 zeroed out, subband-dependent threshold, a soft-thresholding and spline-based discrete dyadic wavelet transform (DDWT)), which we call a modified wavelet shrinkage filter; this filter is suitable for noisy cryo-ET data. When testing using real cryo-ET experiment data, higher quality images and more accurate measures of a biological structure can be obtained with the modified wavelet shrinkage filter processing compared with conventional processing. Because the proposed method provides an inherent advantage when dealing with cryo-ET images, it can therefore extend the current state-of-the-art technology in assisting all aspects of cryo-ET studies: visualization, reconstruction, structural analysis, and interpretation.
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Registered trials
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