4.5 Article

Real-Time Integration Center of Mass (riCOM) Reconstruction for 4D STEM

Journal

MICROSCOPY AND MICROANALYSIS
Volume 28, Issue 5, Pages 1526-1537

Publisher

OXFORD UNIV PRESS
DOI: 10.1017/S1431927622000617

Keywords

4D STEM; electron microscopy; low-dose imaging; real-time reconstruction

Funding

  1. European Research Council (ERC) under the European Union [770887 PICOMETRICS]
  2. European Union [823717 ESTEEM3]
  3. FWO
  4. FNRS [30489208]
  5. University of Antwerp through a TOP BOF project - Hercules fund from the Flemish Government

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This paper proposes a real-time image reconstruction method for scanning transmission electron microscopy (STEM) that can update the resulting image without storing any intermediate diffraction patterns. The method produces high-quality reconstructions with good noise robustness and low computational requirements. An efficient open source implementation is also provided.
A real-time image reconstruction method for scanning transmission electron microscopy (STEM) is proposed. With an algorithm requiring only the center of mass of the diffraction pattern at one probe position at a time, it is able to update the resulting image each time a new probe position is visited without storing any intermediate diffraction patterns. The results show clear features at high spatial frequency, such as atomic column positions. It is also demonstrated that some common post-processing methods, such as band-pass filtering, can be directly integrated in the real-time processing flow. Compared with other reconstruction methods, the proposed method produces high-quality reconstructions with good noise robustness at extremely low memory and computational requirements. An efficient, interactive open source implementation of the concept is further presented, which is compatible with frame-based, as well as event-based camera/file types. This method provides the attractive feature of immediate feedback that microscope operators have become used to, for example, conventional high-angle annular dark field STEM imaging, allowing for rapid decision-making and fine-tuning to obtain the best possible images for beam-sensitive samples at the lowest possible dose.

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