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Autonomous Electron Tomography Reconstruction with Machine Learning.

William Millsaps1, Jonathan Schwartz2, Zichao Wendy Di3

  • 1Department of Nuclear Engineering & Radiological Sciences, University of Michigan, 2300 Hayward St, Ann Arbor, MI 48109, USA.

Microscopy and Microanalysis : the Official Journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada
|August 28, 2023
PubMed
Summary
This summary is machine-generated.

Compressed sensing (CS) in electron tomography improves resolution but requires careful parameter tuning. This study introduces Bayesian optimization with momentum-based CS to automate parameter selection, significantly reducing computation time and improving 3D reconstruction quality.

Keywords:
3D imagingSTEMTEMbayesian optimizationcompressed sensingcryo-ETelectron tomographygaussian processesmachine learningnesterov momentum

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Area of Science:

  • Materials Science
  • Imaging Science
  • Computational Science

Background:

  • Modern electron tomography achieves higher resolution at lower doses using compressed sensing (CS) methods.
  • Sparsity-emphasized reconstruction algorithms in CS tomography rely on tunable parameters that critically affect reconstruction quality.

Purpose of the Study:

  • To investigate the impact of tunable parameters in CS tomography on reconstruction quality.
  • To develop an automated method for efficient parameter selection in CS tomography.
  • To reduce computational time for 3D reconstructions in electron tomography.

Main Methods:

  • Pareto front analysis to identify optimal parameter weighting for total variation (TV) minimization.
  • Incorporation of momentum into gradient descent algorithms to mitigate over-smoothing in CS reconstructions.
  • Application of Bayesian optimization with Gaussian processes for automated tomography parameter selection.

Main Results:

  • Heavily weighted TV minimization reproducibly yields high-quality tomograms.
  • Excessive TV minimization leads to overly smoothed three-dimensional (3D) reconstructions.
  • Momentum-based CS and Bayesian optimization reduced compute time by 80% for SrTiO3 nanocube reconstruction.
  • Automated parameter selection is crucial for large-scale tomographic simulations.

Conclusions:

  • Momentum-based compressed sensing tomography, combined with Bayesian optimization, offers an efficient and robust approach for 3D material characterization.
  • Automated parameter selection is essential for advancing the 3D characterization capabilities of electron tomography for diverse materials.