Deep Learning-powered Bessel-beam Multi-parametric Photoacoustic Microscopy

Abstract

Enabling simultaneous and high resolution quantification of the total concentration of hemoglobin (CHb), 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 CHb, sO2, and CBF over a depth range of ~600 μm in the live mouse brain, with errors 13 to 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).