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bioRxiv · 10.1101/2021.12.21.473705

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

Zhou, Y.·Sun, N.·Hu, S.
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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 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).

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Zhou, Y., Sun, N., Hu, S.. 2021-12-23. Deep Learning-powered Bessel-beam Multi-parametric Photoacoustic Microscopy. https://doi.org/10.1101/2021.12.21.473705

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