压缩冷聚焦离子束扫描电镜的靶向取样策略
A Targeted Sampling Strategy for Compressive Cryo Focused Ion Beam Scanning Electron Microscopy
摘要：
低温聚焦离子束扫描电子显微镜（低温FIB-SEM）通过切片和查看机制实现生物样本的三维和纳米级成像。然而，FIB-SEM实验受到缓慢（通常为几个小时）采集过程的限制，并且施加在束敏感样品上的高电子剂量可能导致损坏。在这项工作中，我们提出了一个压缩感知的低温FIB-扫描电镜能够减少操作电子剂量和提高速度的变体。我们提出了两个有针对性的采样（TS）的策略，利用重建图像的前一个样本层作为设计下一个子采样掩码之前。我们的图像恢复是基于盲贝叶斯字典学习方法，即，Beta过程因素分析（BPFA）。这种方法是实验可行的，由于我们的超快速基于GPU的实现BPFA。人工压缩FIB-SEM测量的模拟验证了所提出的方法的成功：操作电子剂量可以减少高达20倍。这些方法对低温FIB-SEM社区有很大的影响，其中对光束敏感的生物材料的成像而不受光束损伤是至关重要的。
英文摘要：
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.
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