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

TL;DR

This paper introduces a compressive sensing approach with targeted sampling strategies for cryo FIB-SEM, significantly reducing electron dose and increasing imaging speed while preserving image quality for beam-sensitive biological specimens.

Contribution

It presents a novel targeted sampling method using prior reconstructed images and a Bayesian dictionary learning approach to improve cryo FIB-SEM imaging efficiency.

Findings

Electron dose reduced by up to 20 times

Validated effectiveness through simulations on artificial data

Potential to enable damage-free biological imaging

Abstract

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.