Super-resolution technology to simultaneously improve optical & digital resolution of optical coherence tomography via deep learning

Cao, Shengting; Yao, Xinwen; Koirala, Nischal; Brott, Brigitta; Litovsky, Silvio; Ling, Yuye; Gan, Yu

doi:10.1109/EMBC44109.2020.9175777

Abstract Optical‐resolution photoacoustic microscopy (OR‐PAM) has been increasingly utilized for in vivo imaging of biological tissues, offering structural, functional, and molecular information. In OR‐PAM, it is often necessary to make a trade‐off between imaging depth, lateral resolution, field of view, and imaging speed. To improve the lateral resolution without sacrificing other performance metrics, we developed a virtual‐point‐based deconvolution algorithm for OR‐PAM (VP‐PAM). VP‐PAM has achieved a resolution improvement ranging from 43% to 62.5% on a single‐line target. In addition, it has outperformed Richardson‐Lucy deconvolution with 15 iterations in both structural similarity index and peak signal‐to‐noise ratio on an OR‐PAM image of mouse brain vasculature. When applied to an in vivo glass frog image obtained by a deep‐penetrating OR‐PAM system with compromised lateral resolution, VP‐PAM yielded enhanced resolution and contrast with better‐resolved microvessels.

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