Abstract

Microscopy in hard-to-reach parts of a sample, such as the deep brain, can be enabled by computational-cannula microscopy (CCM), where light is transported from one end to the other end of a solid-glass cannula. Computational methods are applied to unscramble the recorded signal to obtain the object details. Since the cannula itself can be microscopic ($\sim 250\mathrm{\mu m}$ in diameter), CCM can enable minimally invasive imaging. Here, we describe a full-scale simulation model for CCM and apply it to not only explore the limits of the technology, but also use it to improve the imaging performance. Specifically, we show that the complexity of the inverse problem to recover CCM images increases with the aspect ratio (length/diameter) of the cannula geometry. We also perform noise tolerance simulations, which indicate that the smaller aspect ratio cannula tolerate noise better than the longer ones. Analysis on noise tolerance using the proposed simulation model showed $2–3\times$ improvement in noise tolerance when the aspect ratio is reduced in half. We can utilize these simulation tools to further improve the performance of CCM and extend the reach of computational microscopy.

© 2017 Optical Society of America

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