# A Targeted Sampling Strategy for Compressive Cryo Focused Ion Beam Scanning Electron Microscopy

arXiv: https://arxiv.org/abs/2211.03494 · HTML: https://mlsanity.com/paper/2211.03494/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 24 references that are in the map.
- First posted on arXiv 7 Nov 2022.

## Builds on (most cited first, 3 of 3)

- [Compressive Scanning Transmission Electron Microscopy](https://mlsanity.com/paper/2112.11955/) (2021)
- [Time-Resolved Focused Ion Beam Microscopy: Modeling, Estimation Methods, and Analyses](https://mlsanity.com/paper/2011.08402/) (2020)
- [SIM-STEM Lab: Incorporating Compressed Sensing Theory for Fast STEM Simulation](https://mlsanity.com/paper/2207.10984/) (2022)

## Abstract (arXiv)

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.