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Related Experiment Video

Updated: Jul 3, 2026

Sample Drift Correction Following 4D Confocal Time-lapse Imaging
10:04

Sample Drift Correction Following 4D Confocal Time-lapse Imaging

Published on: April 12, 2014

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

Matthew Mosse1, Jonathan J P Peters1, Eoin Moynihan2

  • 1Advanced Microscopy Laboratory, CRANN, Trinity College Dublin, The University of Dublin, Dublin, Ireland; School of Physics, Trinity College Dublin, The University of Dublin, Dublin, Ireland.

Ultramicroscopy
|July 1, 2026
PubMed
Summary

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This summary is machine-generated.

This study introduces a novel method to counteract distortions in scanning transmission electron microscopy (STEM) caused by sample drift. By predicting future movements, this technique enhances image quality and usable data area in materials characterization.

Area of Science:

  • Materials Science
  • Microscopy Techniques
  • Image Analysis

Background:

  • Scanning transmission electron microscopy (STEM) is crucial for materials characterization.
  • Sample, stage, or beam drift during STEM imaging causes distortions and reduces usable data.
  • Existing post-acquisition drift correction methods limit the common area across multiple frames.

Purpose of the Study:

  • To develop a predictive method for mitigating sample drift in real-time during STEM imaging.
  • To improve image quality and maximize the common usable area in multi-frame STEM datasets.
  • To provide a generalizable framework applicable to various STEM imaging modes and scan patterns.

Main Methods:

  • Analysis of past image frames to predict future sampling grid points.
Keywords:
Drift correctionImage distortionImage registrationIn-situ STEMScanning transmission electron microscopy (STEM)

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Last Updated: Jul 3, 2026

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  • Real-time adjustment of the scan grid to compensate for drift.
  • Application of corrections at both framewise (long-range drift) and pixelwise (intra-image warping) scales.
  • Validation across different imaging scales (atomic resolution, in-situ video) and scan patterns (raster, serpentine, interlaced).

    • Main Results:

    • Successful mitigation of long-range drift through framewise scan-grid offsetting.
    • Minimization of intra-image warping via pixelwise offsetting.
    • Demonstrated effectiveness in both high-resolution atomic imaging and lower-magnification in-situ video capture.
    • Preservation of a larger common area across multiple frames compared to post-acquisition correction.

    Conclusions:

    • The presented predictive drift correction method significantly enhances the reliability and data yield of STEM imaging.
    • This framework offers a versatile solution for drift issues in diverse STEM applications and scan strategies.
    • The real-time correction approach overcomes limitations of traditional post-processing techniques, improving materials characterization efficiency.