Virtual-point-based deconvolution for optical-resolution photoacoustic microscopy

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.