Image registration of low signal-to-noise cryo-STEM data

Abstract

Combining multiple fast image acquisitions to mitigate scan noise and drift artifacts has proven essential for picometer precision, quantitative analysis of atomic resolution scanning transmission electron microscopy (STEM) data. For very low signal-to-noise ratio (SNR) image stacks - frequently required for undistorted imaging at liquid nitrogen temperatures - image registration is particularly delicate, and standard approaches may either fail, or produce subtly specious reconstructed lattice images. We present an approach which effectively registers and averages image stacks which are challenging due to their low-SNR and propensity for unit cell misalignments. Registering all possible image pairs in a multi-image stack leads to significant information surplus. In combination with a simple physical picture of stage drift, this enables identification of incorrect image registrations, and determination of the optimal image shifts from the complete set of relative shifts. We demonstrate the effectiveness of our approach on experimental, cryogenic STEM datasets, highlighting subtle artifacts endemic to low-SNR lattice images and how they can be avoided. High-SNR average images with information transfer out to 0.72 Å are achieved at 300 kV and with the sample cooled to near liquid nitrogen temperature.

Spatial and spectral dynamics in STEM hyperspectral imaging using random scan patterns

“…Drift is, for instance, very difficult to fully eliminate in cryo-STEM, as the sample holder is cooled to liquid nitrogen temperatures and thus experiences much larger thermal gradients. 20 Instability-induced image deformations can strongly deteriorate the imaging capabilities of modern STEM microscopes which can reach sub-Ångstrom resolution and sub-picometer precision. Distortions are even more pronounced in hyperspectral imaging compared to HAADF imaging due to the longer dwell times.…”

Fast Pixelated Detectors in Scanning Transmission Electron Microscopy. Part I: Data Acquisition, Live Processing, and Storage

“…It also serves as a base for several other packages, such as pyXem for analyzing SPED data (Johnstone et al, 2019), Atomap for processing atomic resolution STEM data (Nord et al, 2017), and pixStem for working with data from fast pixelated STEM detectors (pixStem devs, 2015). Several other packages exist, like rigidRegistration for doing rigid image registration of atomic resolution image stacks (Savitzky et al, 2018), and wrappers for doing STEM simulations, like PyPrismatic (Ophus, 2017). Other packages for processing data from fast pixelated STEM detectors include py4DSTEM (Savitzky et al, 2019), LiberTEM (Clausen et al, 2019), pycroscopy (Somnath et al, 2019), and fpd (fpd devs, 2015).…”

Atomic-Scale Characterization Reveals Core-Shell Structure of Enamel Crystallites

“…Several images were collected, registered by cross-correlation, and averaged to minimize noise ( Fig. 1D) [3]. A careful evaluation of crystallites using medium angle annular dark field (MAADF) imaging revealed that crystallites are clearly separated by thin regions containing lower atomic number (Z) contrast ( Fig.…”