As a promising branch of optical absorption-based photoacoustic microscopy, photoacoustic dermoscopy (PAD) can provide manifold morphologic and functional information in clinical diagnosis and the assessment of dermatological conditions. However, most PAD setups are insufficient for clinical dermatology, given their single optical resolution (OR) or acoustic resolution (AR) mode, which results in poor spatiotemporal resolution or imaging depth for visualizing the internal texture of skin. Here, a switchable optical and acoustic resolution photoacoustic dermoscope (S-OR-ARPAD) system is developed, which provides a smooth transition from OR mode in microscopic imaging of superficial skin layers to AR mode when imaging at greater depths within intensely scattering deep skin layers. The lateral resolution can be seamlessly switched between 4.4 and 47 μm as the maximum imaging depth is switched between 1.2 and 1.8 mm for skin imaging. Using the S-OR-ARPAD, we identified the two distinct resolution modes responsible for resolving features of different skin layers and demonstrated the fine structures with strong contrast in the stratum corneum, dermal papillae, and microvascular structures in the horizontal plexus by imaging the healthy human skin at different locations.

Photoacoustic dermoscopy (PAD) is an emerging non-invasive imaging technology aids in the diagnosis of dermatological conditions by obtaining optical absorption information of skin tissues. Despite advances in PAD, it remains unclear how to obtain quantitative accuracy of the reconstructed PAD images according to the optical and acoustic properties of multilayered skin, the wavelength and distribution of excitation light, and the detection performance of ultrasound transducers. In this work, a computing method of four-dimensional (4D) spectral-spatial imaging for PAD is developed to enable quantitative analysis and optimization of structural and functional imaging of skin. This method takes the optical and acoustic properties of heterogeneous skin tissues into account, which can be used to correct the optical field of excitation light, detectable ultrasonic field, and provide accurate single-spectrum analysis or multi-spectral imaging solutions of PAD for multilayered skin tissues. A series of experiments were performed, and simulation datasets obtained from the computational model were used to train neural networks to further improve the imaging quality of the PAD system. All the results demonstrated the method could contribute to the development and optimization of clinical PADs by datasets with multiple variable parameters, and provide clinical predictability of photoacoustic (PA) data for human skin.

Multiscale confocal photoacoustic dermoscopy to evaluate skin health Haigang Ma, Zhiyang Wang, Zhongwen Cheng, Guo He, Ting Feng, Chao Zuo, Haixia Qiu

Deep learning (DL) is making significant inroads into biomedical imaging as it provides novel and powerful ways of accurately and efficiently improving the image quality of photoacoustic microscopy (PAM). Off-the-shelf DL models, however, do not necessarily obey the fundamental governing laws of PAM physical systems, nor do they generalize well to scenarios on which they have not been trained. In this work, a physics-embedded degeneration learning (PEDL) approach is proposed to enhance the image quality of PAM with a self-attention enhanced U-Net network, which obtains greater physical consistency, improves data efficiency, and higher adaptability. The proposed method is demonstrated on both synthetic and real datasets, including animal experiments in vivo (blood vessels of mouse's ear and brain). And the results show that compared with previous DL methods, the PEDL algorithm exhibits good performance in recovering PAM images qualitatively and quantitatively. It overcomes the challenges related to training data, accuracy, and robustness which a typical data-driven approach encounters, whose exemplary application envisions to provide a new perspective for existing DL tools of enhanced PAM.