A Targeted Sampling Strategy for Compressive Cryo Focused Ion Beam Scanning Electron Microscopy
This work addresses beam damage in cryo FIB-SEM imaging for biological specimens, offering a significant improvement but is incremental as it builds on existing compressive sensing and dictionary learning methods.
The paper tackles the slow acquisition and high electron dose issues in cryo FIB-SEM imaging by introducing a compressive sensing variant with targeted sampling strategies, achieving up to a 20-fold reduction in operational electron dose.
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.