Temporal and spectral unmixing of photoacoustic signals by deep learning.

Improving the imaging speed of multi-parametric photoacoustic microscopy (PAM) is essential to leveraging its impact in biomedicine. However, to avoid temporal overlap, the A-line rate is limited by the acoustic speed in biological tissues to a few megahertz. Moreover, to achieve high-speed PAM of the oxygen saturation of hemoglobin, the stimulated Raman scattering effect in optical fibers has been widely used to generate 558 nm from a commercial 532 nm laser for dual-wavelength excitation. However, the fiber length for effective wavelength conversion is typically short, corresponding to a small time delay that leads to a significant overlap of the A-lines acquired at the two wavelengths. Increasing the fiber length extends the time interval but limits the pulse energy at 558 nm. In this Letter, we report a conditional generative adversarial network-based approach that enables temporal unmixing of photoacoustic A-line signals with an interval as short as ${\sim}{38}\;{\rm ns}$, breaking the physical limit on the A-line rate. Moreover, this deep learning approach allows the use of multi-spectral laser pulses for PAM excitation, addressing the insufficient energy of monochromatic laser pulses. This technique lays the foundation for ultrahigh-speed multi-parametric PAM.