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Photoacoustic microscopy uses multiple wavelengths to measure concentrations of different absorbers. The speed of sound limits the shortest wavelength switching time to sub-microseconds, which is a bottleneck for high-speed broad-spectrum imaging. Via computational separation of overlapped signals, we can break the sound-speed limit on the wavelength switching time. This paper presents a new signal unmixing algorithm named two-step proximal gradient descent. It is advantageous in separating multiple wavelengths with long overlapping and high noise. In the simulation, we can unmix up to nine overlapped signals and successfully separate three overlapped signals with 12-ns delay and 15.9-dB signal-to-noise ratio. We apply this technique to separate three-wavelength photoacoustic images in microvessels. In vivo results show that the algorithm can successfully unmix overlapped multi-wavelength photoacoustic signals, and the unmixed data can improve accuracy in oxygen saturation imaging.

Optical-resolution photoacoustic microscopy (OR-PAM) enjoys superior spatial resolution and has received intense attention in recent years. The application, however, has been limited to shallow depths because of strong scattering of light in biological tissues. In this work, we propose to achieve deep-penetrating OR-PAM performance by using deep learning enabled image transformation on blurry living mouse vascular images that were acquired with an acoustic-resolution photoacoustic microscopy (AR-PAM) setup. A generative adversarial network (GAN) was trained in this study and improved the imaging lateral resolution of AR-PAM from 54.0 µm to 5.1 µm, comparable to that of a typical OR-PAM (4.7 µm). The feasibility of the network was evaluated with living mouse ear data, producing superior microvasculature images that outperforms blind deconvolution. The generalization of the network was validated with in vivo mouse brain data. Moreover, it was shown experimentally that the deep-learning method can retain high resolution at tissue depths beyond one optical transport mean free path. Whilst it can be further improved, the proposed method provides new horizons to expand the scope of OR-PAM towards deep-tissue imaging and wide applications in biomedicine.

Dichroism is a material property that causes anisotropic light-matter interactions for different optical polarizations. Dichroism relates to molecular types and material morphology and thus can be used to distinguish different dichroic tissues. In this paper, we present single-shot dichroism photoacoustic microscopy that can image tissue structure, linear dichroism, and polarization angle with a single raster scanning. We develop a fiber-based laser system to split one laser pulse into three with different polarization angles, sub-microseconds time delay, and identical pulse energy. A dual-fiber optical-resolution photoacoustic microscopy system is developed to acquire three A-lines per scanning step. In such a way, dichroism imaging can achieve the same speed as single-wavelength photoacoustic microscopy. Moreover, the three polarized pulses originate from one laser pulse, which decreases pulse energy fluctuations and reduces dichroism measurement noise by ∼35 %. The new dichroism photoacoustic imaging technique can be used to image endogenous or exogenous polarization-dependent absorption contrasts, such as dichroic tumor or molecule-labeled tissue. Keywords: Linear dichroism, Photoacoustic microscopy, Polarization