ArticleJournal of structural biology: X2020
A Monte Carlo framework for missing wedge restoration and noise removal in cryo-electron tomography.
Article in Journal of structural biology: X, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Deep-learning methods for contrast enhancement and artifact reduction in cryo-electron tomography: a systematic analysis of the state of the art and proposed improvements.Acta crystallographica. Section D, Structural biology · 2026Article
- Template Learning: Deep learning with domain randomization for particle picking in cryo-electron tomography.Nature communications · 2025Article
- Investigating the missing-wedge problem in small-angle X-ray scattering tensor tomography across real and reciprocal space.Journal of synchrotron radiation · 2024Article
- Missing Wedge Completion via Unsupervised Learning with Coordinate Networks.International journal of molecular sciences · 2024Article
- Missing Wedge Completion via Unsupervised Learning with Coordinate Networks.bioRxiv : the preprint server for biology · 2024Article
- DUAL: deep unsupervised simultaneous simulation and denoising for cryo-electron tomography.bioRxiv : the preprint server for biology · 2024Article
- A method for restoring signals and revealing individual macromolecule states in cryo-ET, REST.Nature communications · 2023Article
- Automatic Differentiation for Inverse Problems in X-ray Imaging and Microscopy.Life (Basel, Switzerland) · 2023Article
- Computational methods forFrontiers in cellular and infection microbiology · 2023Review
- Structure Detection in Three-Dimensional Cellular Cryoelectron Tomograms by Reconstructing Two-Dimensional Annotated Tilt Series.Journal of computational biology : a journal of computational molecular cell biology · 2022Article
- On bias, variance, overfitting, gold standard and consensus in single-particle analysis by cryo-electron microscopy.Acta crystallographica. Section D, Structural biology · 2022Article
- Cryo-electron tomography related radiation-damage parameters for individual-molecule 3D structure determination.Frontiers in chemistry · 2022Review
- Deep learning improves macromolecule identification in 3D cellular cryo-electron tomograms.Nature methods · 2021Article
- HEMNMA-3D: Cryo Electron Tomography Method Based on Normal Mode Analysis to Study Continuous Conformational Variability of Macromolecular Complexes.Frontiers in molecular biosciences · 2021Article
- Novel nonlinear reconstruction method with grey-level quantisation units for electron tomography.Scientific reports · 2020Article
- Three-dimensional deconvolution processing for STEM cryotomography.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
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2 authors.
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Abstract
We propose a statistical method to address an important issue in cryo-electron tomography image analysis: reduction of a high amount of noise and artifacts due to the presence of a missing wedge (MW) in the spectral domain. The method takes as an input a 3D tomogram derived from limited-angle tomography, and gives as an output a 3D denoised and artifact compensated volume. The artifact compensation is achieved by filling up the MW with meaningful information. To address this inverse problem, we compute a Minimum Mean Square Error (MMSE) estimator of the uncorrupted image. The underlying high-dimensional integral is computed by applying a dedicated Markov Chain Monte-Carlo (MCMC) sampling procedure based on the Metropolis-Hasting (MH) algorithm. The proposed MWR (Missing Wedge Restoration) algorithm can be used to enhance visualization or as a pre-processing step for image analysis, including segmentation and classification of macromolecules. Results are presented for both synthetic data and real 3D cryo-electron images.
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