  Cryo Focused Ion-Beam Scanning Electron Microscopy (cryo FIB-SEM) enables
three-dimensional and nanoscale imaging of biological specimens via a slice and
view mechanism. The FIB-SEM experiments are, however, limited by a slow
(typically, several hours) acquisition process and the high electron doses
imposed on the beam sensitive specimen can cause damage. In this work, we
present a compressive sensing variant of cryo FIB-SEM capable of reducing the
operational electron dose and increasing speed. We propose two Targeted
Sampling (TS) strategies that leverage the reconstructed image of the previous
sample layer as a prior for designing the next subsampling mask. Our image
recovery is based on a blind Bayesian dictionary learning approach, i.e., Beta
Process Factor Analysis (BPFA). This method is experimentally viable due to our
ultra-fast GPU-based implementation of BPFA. Simulations on artificial
compressive FIB-SEM measurements validate the success of proposed methods: the
operational electron dose can be reduced by up to 20 times. These methods have
large implications for the cryo FIB-SEM community, in which the imaging of beam
sensitive biological materials without beam damage is crucial.
