Deep Learning-Powered Bessel-Beam Multiparametric Photoacoustic Microscopy

Enabling simultaneous and high-resolution quantification of the total concentration of hemoglobin (
$\text{C}_{{\text {Hb}}}$
), oxygen saturation of hemoglobin (sO2), and cerebral blood flow (CBF), multi-parametric photoacoustic microscopy (PAM) has emerged as a promising tool for functional and metabolic imaging of the live mouse brain. However, due to the limited depth of focus imposed by the Gaussian-beam excitation, the quantitative measurements become inaccurate when the imaging object is out of focus. To address this problem, we have developed a hardware-software combined approach by integrating Bessel-beam excitation and conditional generative adversarial network (cGAN)-based deep learning. Side-by-side comparison of the new cGAN-powered Bessel-beam multi-parametric PAM against the conventional Gaussian-beam multi-parametric PAM shows that the new system enables high-resolution, quantitative imaging of
$\text{C}_{{\text {Hb}}}$
, sO2, and CBF over a depth range of
$\sim 600~\mu \text{m}$
in the live mouse brain, with errors 13–58 times lower than those of the conventional system. Better fulfilling the rigid requirement of light focusing for accurate hemodynamic measurements, the deep learning-powered Bessel-beam multi-parametric PAM may find applications in large-field functional recording across the uneven brain surface and beyond (e.g., tumor imaging).