ArticleUltramicroscopy2020
Reducing bias and variance for CTF estimation in single particle cryo-EM.
Article in Ultramicroscopy, 2020. 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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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- WebCalEM: a browser-based tool for routine and accurate pixel-size calibration in cryo-EM.Acta crystallographica. Section F, Structural biology communications · 2026Article
- WebCalEM: a browser-based tool for routine and accurate pixel size calibration in cryo-EM.bioRxiv : the preprint server for biology · 2026Article
- Novel Artificial Intelligence-Based Approaches for Ab Initio Structure Determination and Atomic Model Building for Cryo-Electron Microscopy.Micromachines · 2023Review
- Toward Single Particle Reconstruction without Particle Picking: Breaking the Detection Limit.SIAM journal on imaging sciences · 2023Article
- Emerging Themes in CryoEM─Single Particle Analysis Image Processing.Chemical reviews · 2022Review
- Ab-initio contrast estimation and denoising of cryo-EM images.Computer methods and programs in biomedicine · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
When using an electron microscope for imaging of particles embedded in vitreous ice, the recorded image, or micrograph, is a significantly degraded version of the tomographic projection of the sample. Apart from noise, the image is affected by the optical configuration of the microscope. This transformation is typically modeled as a convolution with a point spread function. The Fourier transform of this function, known as the contrast transfer function (CTF), is oscillatory, attenuating and amplifying different frequency bands, and sometimes flipping their signs. High-resolution reconstruction requires this CTF to be accounted for, but as its form depends on experimental parameters, it must first be estimated from the micrograph. We present a new method for CTF estimation based on multitaper techniques that reduce bias and variance in the estimate. We also use known properties of the CTF and the background power spectrum to further reduce the variance through background subtraction and steerable basis projection. We show that the resulting power spectrum estimates better capture the zero-crossings of the CTF and yield accurate CTF estimates on several experimental micrographs.
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Registered trials
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