Opt Lett. 2023 May 15;48(10):2720-2723. doi: 10.1364/OL.486624.
Frequency-domain photoacoustic microscopy (FD-PAM) constitutes a powerful cost-efficient imaging method integrating intensity-modulated laser beams for the excitation of single-frequency photoacoustic waves. Nevertheless, FD-PAM provides an extremely small signal-to-noise ratio (SNR), which can be up to two orders of magnitude lower than the conventional time-domain (TD) systems. To overcome this inherent SNR limitation of FD-PAM, we utilize a U-Net neural network aiming at image augmentation without the need for excessive averaging or the application of high optical power. In this context, we improve the accessibility of PAM as the system's cost is dramatically reduced, and we expand its applicability to demanding observations while retaining sufficiently high image quality standards.
频域光声显微镜（FD-PAM）是一种强大的、具有成本效益的成像方法，它集成了强度调制的激光束，用于激发单频光声波。然而，FD-PAM 提供的信噪比（SNR）极低，比传统的时域（TD）系统低多达两个数量级。为了克服 FD-PAM 的这种固有 SNR 限制，我们利用 U-Net 神经网络来进行图像增强，而无需进行过度的平均或应用高功率光学。在这种情况下，我们提高了 PAM 的可及性，因为系统的成本大大降低，并且在保留足够高的图像质量标准的同时，将其适用性扩展到了苛刻的观察中。
