Compressed sensing for STEM tomography

Laurène Donati, Masih Nilchian, Sylvain Trépout, Cédric Messaoudi, Sergio Marco, Michael Unser

Research output: Contribution to journalArticleResearchpeer-review

26 Citations (Scopus)

Abstract

A central challenge in scanning transmission electron microscopy (STEM) is to reduce the electron radiation dosage required for accurate imaging of 3D biological nano-structures. Methods that permit tomographic reconstruction from a reduced number of STEM acquisitions without introducing significant degradation in the final volume are thus of particular importance. In random-beam STEM (RB-STEM), the projection measurements are acquired by randomly scanning a subset of pixels at every tilt view. In this work, we present a tailored RB-STEM acquisition-reconstruction framework that fully exploits the compressed sensing principles. We first demonstrate that RB-STEM acquisition fulfills the “incoherence” condition when the image is expressed in terms of wavelets. We then propose a regularized tomographic reconstruction framework to recover volumes from RB-STEM measurements. We demonstrate through simulations on synthetic and real projection measurements that the proposed framework reconstructs high-quality volumes from strongly downsampled RB-STEM data and outperforms existing techniques at doing so. This application of compressed sensing principles to STEM paves the way for a practical implementation of RB-STEM and opens new perspectives for high-quality reconstructions in STEM tomography.

Original languageEnglish
Pages (from-to)47-56
Number of pages10
JournalUltramicroscopy
Volume179
DOIs
Publication statusPublished - 1 Aug 2017
Externally publishedYes

Keywords

  • Compressed sensing
  • Electron tomography
  • Image reconstruction
  • Random-beam scanning
  • RB-STEM
  • STEM

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