Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

ACE: automated CTF estimation.

Satya P Mallick1, Bridget Carragher, Clinton S Potter

  • 1Department of Electrical and Computer Engineering, University of California, San Diego, USA. spmallick@graphics.ucsd.edu

Ultramicroscopy
|June 7, 2005
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Characterization and modulation of human insulin degrading enzyme conformational dynamics to control enzyme activity.

eLife·2026
Same author

Characterization Standard for <i>In-situ</i> Cryo-electron Tomography.

bioRxiv : the preprint server for biology·2026
Same author

AreTomoLive: automated reconstruction of comprehensively corrected and denoised cryo-electron tomograms in real time and at high throughput.

Nature methods·2026
Same author

copick: An open dataset interface and toolkit for collaborative annotation and analysis of cryo-electron tomography data.

Protein science : a publication of the Protein Society·2026
Same author

Evaluating the Volta phase plate for improved tomogram alignment in cryo-electron tomography.

IUCrJ·2026
Same author

A realistic phantom dataset for benchmarking cryo-ET data annotation.

Nature methods·2025
Same journal

Predictive drift compensation of multi-frame STEM via live scan modification.

Ultramicroscopy·2026
Same journal

Deep PACBED: Multitask analysis of PACBED images using deep neural networks.

Ultramicroscopy·2026
Same journal

Guided progressive reconstructive imaging: A new quantization-based framework for low-dose, high-throughput and real-time analytical ptychography.

Ultramicroscopy·2026
Same journal

Brightness optimization in a 200 keV DTEM source by geometry-driven aberration suppression.

Ultramicroscopy·2026
Same journal

Characterization of the Timepix4 hybrid pixel detector and its impact on four-dimensional scanning transmission electron microscopy (4D-STEM).

Ultramicroscopy·2026
Same journal

Contamination analysis of the residual gas composition in transmission electron microscopy.

Ultramicroscopy·2026
See all related articles

We developed an automated algorithm to estimate transmission electron microscope contrast transfer function (CTF) parameters. This method accurately determines astigmatism before CTF estimation, simplifying the process and improving results for various imaging conditions.

Area of Science:

  • Microscopy
  • Image Processing
  • Computational Science

Background:

  • Accurate contrast transfer function (CTF) estimation is crucial for high-resolution transmission electron microscopy (TEM).
  • Existing CTF estimation methods can be complex and require manual intervention, particularly for determining astigmatism.

Purpose of the Study:

  • To develop a fully automated algorithm for precise CTF parameter estimation in TEM.
  • To address the challenge of astigmatism determination prior to CTF parameter estimation.
  • To improve the efficiency and accuracy of CTF estimation for diverse TEM imaging applications.

Main Methods:

  • An automated algorithm, ACE, was developed for CTF parameter estimation.
  • Astigmatism is determined first, simplifying the subsequent CTF parameter estimation to a 1D problem via elliptical averaging.

Related Experiment Videos

  • Automated cutoff frequency determination is implemented to exclude perturbing regions of the power spectrum.
  • The algorithm involves three optimization subproblems, with two proven to be convex.

    • Main Results:

    • The algorithm successfully estimated CTF parameters for various samples, including carbon support films and single particles in ice.
    • Demonstrated accurate astigmatism determination as a key step in the automated CTF estimation process.
    • Validated the effectiveness of automated cutoff frequency selection in improving estimation robustness.

    Conclusions:

    • The developed automated algorithm (ACE) provides a robust and efficient method for CTF parameter estimation in TEM.
    • The approach simplifies complex CTF estimation by prioritizing astigmatism determination.
    • ACE is freely available as a MATLAB implementation, facilitating its adoption in the scientific community.