Basal cell carcinoma (BCC) and melanoma (MM), with the highest morbidity and mortality, respectively, are considered as two skin cancers of concern in dermatology. Histological studies have demonstrated that vascular patterns and collagenous stroma serve as key parameters for BCC and MM classification. In this Letter, we sought to identify BCC and MM based on the dual parameters of vascular patterns and scattering structures provided by all-optically integrated photoacoustic and optical coherence tomography (AOPA/OCT). The imaging capability of the AOPA/OCT was verified by the mimic phantoms. Furthermore, in vivo characterization of vasculatures and tissue structures from BCC and MM mice were successfully achieved with high resolution. Results prove the feasibility of AOPA/OCT as a novel method to dedicate to the in vivo biopsy of skin cancers which shows new insights into the study of skin diseases in early stages.

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Background: Projection tomography (PT) is a very important and valuable method for fast volumetric imaging with isotropic spatial resolution. Sparse-view or limited-angle reconstruction-based PT can greatly reduce data acquisition time, lower radiation doses, and simplify sample fixation modes. However, few techniques can currently achieve image reconstruction based on few-view projection data, which is especially important for in vivo PT in living organisms.Methods: A 2-stage deep learning network (TSDLN)-based framework was proposed for parallel-beam PT reconstructions using few-view projections. The framework is composed of a reconstruction network (R-net) and a correction network (C-net). The R-net is a generative adversarial network (GAN) used to complete image information with direct back-projection (BP) of a sparse signal, bringing the reconstructed image close to reconstruction results obtained from fully projected data. The C-net is a U-net array that denoises the compensation result to obtain a high-quality reconstructed image. Results:The accuracy and feasibility of the proposed TSDLN-based framework in few-view projectionbased reconstruction were first evaluated with simulations, using images from the DeepLesion public dataset. The framework exhibited better reconstruction performance than traditional analytic reconstruction algorithms and iterative algorithms, especially in cases using sparse-view projection images. For example, with as few as two projections, the TSDLN-based framework reconstructed high-quality images very close to the original image, with structural similarities greater than 0.8. By using previously acquired optical PT (OPT) data in the TSDLN-based framework trained on computed tomography (CT) data, we further exemplified the migration capabilities of the TSDLN-based framework. The results showed that when the number of projections was reduced to 5, the contours and distribution information of the samples in question could still be seen in the reconstructed images. Conclusions:The simulations and experimental results showed that the TSDLN-based framework has strong reconstruction abilities using few-view projection images, and has great potential in the application of in vivo PT.

Accurate and timely assessment of the severity of burn is essential for the treatment of burns. Currently, although most first-degree and thirddegree burns are easily diagnosed through visual inspection or auxiliary diagnostic methods, the seconddegree burn is still difficult to distinguish due to the ambiguity boundaries of second-degree with first-degree and third-degree burns. In this study, we proposed a non-invasive technique by combing photoacoustic imaging (PAI) and optical coherence tomography (OCT) to multi-parameter quantitatively assess the burns. The feasibility and capacity of the dualmode PAT/OCT for assessing the burns was first testified by tissuemimicking phantom and burn wounds in mouse pinna in vivo. The further

The method of measuring blood flow in photoacoustic microscopy usually relies on ultrasonic transducers in contact fashion, which is not favored in many applications, such as wound areas, burns, and anabrosis. Here we present a noncontact photoacoustic velocity measurement method to quantitatively map transverse blood flow based on the photoacoustic Doppler (PAD) bandwidth broadening method with an all-optical photoacoustic microscopy system. It is validated that the PAD bandwidth broadening is proportional to the transverse flow within a certain range. The transverse flow speed ranging from 0 to 5.5 mm/s, as well as sectional flow images, was obtained in the blood-mimicking flow phantoms. Furthermore, the blood flow image of the mouse ear demonstrates that the all-optical photoacoustic Doppler method can acquire the information of blood flow in vivo, which could significantly broaden the scope of applications for obtaining the blood flow velocity of the microvasculature in biomedicine.