Compressed sensing for dose reduction in STEM tomography

Abstract

We designed a complete acquisition-reconstruction framework to reduce the radiation dosage in 3D scanning transmission electron microscopy (STEM). Projection measurements are acquired by randomly scanning a subset of pixels at every tilt-view (i.e., random-beam STEM or "RB-STEM"). Highquality images are then recovered from the randomly downsampled measurements through a regularized tomographic reconstruction framework. By fulfilling the compressed sensing requirements, the proposed approach improves the reconstruction of heavily-downsampled RB-STEM measurements over the current state-of-the-art technique. This development opens new perspectives in the search for methods permitting lower-dose 3D STEM imaging of electron-sensitive samples without degrading the quality of the reconstructed volume. A Matlab code implementing the proposed reconstruction algorithm has been made available online.

Various Compressed Sensing Setups Evaluated Against Shannon Sampling Under Constraint of Constant Illumination

“…Furthermore, with the increased occurrence of soft and hard matter being interfaced into so-called hybrid materials [9] biology's low upper bound for the electron dose now encroaches on the realm of materials physics too. A current trend in scanning transmission electron microscopy (STEM) seeks to limit electron exposure of the specimen by invoking compressed sensing [10] (CS) in the recording process [11,12,13,14].…”