Kim Ganghun, Menon Rajesh
Appl Opt. 2017 Mar 20;56(9):D1-D7. doi: 10.1364/AO.56.0000D1.
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 (∼250 μm 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× 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.
计算插管显微镜（CCM）可实现对样本难以触及部位（如深部脑区）的显微镜检查，在该技术中，光从实心玻璃插管的一端传输至另一端。应用计算方法对记录的信号进行解译，以获取物体细节。由于插管本身可以是微观尺寸（直径约250μm），CCM能够实现微创成像。在此，我们描述了一种用于CCM的全尺寸模拟模型，并将其应用于不仅探索该技术的极限，还用于改善成像性能。具体而言，我们表明，恢复CCM图像的逆问题的复杂性随插管几何形状的纵横比（长度/直径）增加。我们还进行了噪声容限模拟，结果表明纵横比小的插管比长的插管对噪声的耐受性更好。使用所提出的模拟模型进行的噪声容限分析表明，当纵横比减半时，噪声容限提高了2至3倍。我们可以利用这些模拟工具进一步提高CCM的性能，并扩展计算显微镜的应用范围。
