Compressed Sensing for STEM Tomography
L. Donati, M. Nilchian, S. Trépout, C. Messaoudi, S. Marco, M. Unser
Ultramicroscopy, vol. 179, pp. 47-56, August 2017.
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.
@ARTICLE(http://bigwww.epfl.ch/publications/donati1702.html,
AUTHOR="Donati, L. and Nilchian, M. and Tr{\'{e}}pout, S. and Messaoudi,
C. and Marco, S. and Unser, M.",
TITLE="Compressed Sensing for {STEM} Tomography",
JOURNAL="Ultramicroscopy",
YEAR="2017",
volume="179",
number="",
pages="47--56",
month="August",
note="")