A novel mixed-signal adaptable silicon-based coherent photoacoustic (PA) sensing system-on-chip (SoC) is proposed to detect various kinds of target signals robustly under high noise and strong interferences in compact chip-level, attaining precise in vivo sensing for physiological signs monitoring and enhanced bio-imaging. Based on the configurable coherent PA sensing SoC architecture supported by on-chip Gilbert cell-based multiplier, a digital processing module, and DACs, in-phase (I) and quadrature (Q) templates generated by digital module on-chip are configurable to be with a high correlation coefficient to the target PA signal, attaining detection and reconstruction of target signals in a coherent detection mode. The correlation between the received PA signal and the templates is implemented efficiently, assuring accurate tracking and precise reconstruction on the target PA signals at the chip level. Based on the integrated PA SoC fabricated by the TSMC 65-nm CMOS process, precise in vivo sensing and imaging can be assured at the edge. Further, as PA detection leverages optical and ultrasound sensing, in vivo imaging on in-depth vessels or other tissues can be attained. The mixed-signal PA SoC paves the way for sustainable health monitoring and owns immense potential for early disease diagnostics based on in vivo blood temperature sensing and vessel imaging.

Optical resolution photoacoustic microscopy (OR-PAM) imaging method can achieve high lateral resolution $(\lt 5 \mu \mathrm{m})$, while the penetration depth for OR is shallow (up to $1 \sim 2$ mm). In contrast, acoustic resolution photoacoustic microscopy (AR-PAM) imaging only has limited lateral resolution $(\gt 50 \mu \mathrm{m})$ but with deeper penetration depth up to several millimeters (3-10 mm). Enlighted by the recent progress in the field of machine learning, we proposed to enhance AR-PAM to OR-PAM while maintaining its high penetration depth merit with deep neural network, where a novel network structure named MultiResU-Net is employed. By training the network with OR images obtained with real setup and AR images simulated with physical model, the network is able to enhance the image quality of simulated AR image a huge extent that is similar to OR image. More importantly, the trained model is applied to real AR imaging system for both phantom and in vivo image enhancement. When compared with corresponding ground truth OR images, it can be fully substantiated that our proposed method realized the AR to OR target in real photoacoustic microscopy imaging system.

Acoustic resolution photoacoustic micros- copy (AR-PAM) can achieve deeper imaging depth in biological tissue, with the sacrifice of imaging resolution compared with optical resolution photoacoustic microscopy (OR-PAM). Here we aim to enhance the AR-PAM image quality towards OR-PAM image, which specifically includes the enhancement of imaging resolution, restoration of micro-vasculatures, and reduction of artifacts. To address this issue, a network (MultiResU-Net) is first trained as generative model with simulated AR-OR image pairs, which are synthesized with physical transducer model. Moderate enhancement results can already be obtained when applying this model to in vivo AR imaging data. Nevertheless, the perceptual quality is unsatisfactory due to domain shift. Further, domain transfer learning technique under generative adversarial network (GAN) framework is proposed to drive the enhanced image's manifold towards that of real OR image. In this way, perceptually convincing AR to OR enhancement result is obtained, which can also be supported by quantitative analysis. Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM) values are significantly increased from 14.74 dB to 19.01 dB and from 0.1974 to 0.2937, respectively, validating the improvement of reconstruction correctness and overall perceptual quality. The proposed algorithm has also been validated across different imaging depths with experiments conducted in both shallow and deep tissue. The above AR to OR domain transfer learning with GAN (AODTL-GAN) framework has enabled the enhancement target with limited amount of matched in vivo AR-OR imaging data.

Photoacoustic Microscopy (PAM) optical resolution (OR) imaging method is suited to get high resolution bio-tissue image but suffers from shallow penetration depth. By contrast, photoacoustic acoustic resolution (AR) imaging has deeper penetration depth but with degraded imaging resolution. Inspired by the current advances in the field of deep neural network (DNN), we proposed a new DNN framework named Prior Residual U-Net (PRU-Net), which combines U-Net with global residual block and image prior for AR image to OR image resolution enhancement. It helps to aggregate the advantages of both imaging methods without the cost of building extra physical setup. By training the model with experimentally obtained OR image and simulated AR image pairs, the model is able to enhance the image quality from AR image towards OR image to a huge extent. The enhancement results of sub-images and complete image have both validated this method's effectiveness qualitatively and quantitatively.

Acoustic-resolution microscopy is an important imaging method in studying biological tissues with deep penetration. Using high-frequency transducer could reduce the size of the acoustic focal spot, thereby improving the resolution of microscopy. However, high-frequency photoacoustic signals usually suffer from acoustic attenuation which weakens their energy and distorts the image. In this paper, an attenuation compensation for high-frequency acoustic-resolution photoacoustic imaging is proposed. This technique upgrades the wavenumber term by considering acoustic attenuation and dispersion during wavefield extrapolation, which is a Fourier-domain image reconstruction. It is able to deal with the space-variant attenuation effect and inherits the high computational efficiency of wavefield extrapolation methods. According to the results of simulations and experiments, attenuation compensation successfully eliminates the dispersion induced reconstruction errors, obviously improves the resolution of the image and clearly presents the edges of targets.

Photoacoustic endoscopy (PAE), combining both advantages of optical contrast and acoustic resolution, can visualize the chemical-specific optical information of tissues inside human-body. Recently, its corresponding reconstruction methods have been extensively researched. However, most of them are limited on cylindrical scan trajectories, rather than a helical scan which is more clinically practical. On this note, this article proposes a methodology of imaging reconstruction and evaluation for helical scan guided PAE. Different from traditional reconstruction method, synthetic aperture focusing technique (SAFT), our method reconstructs image using wavefield extrapolation which significantly improves computational efficiency and even takes only 0.25 seconds for 3-D reconstructions. In addition, the proposed evaluation methodology can estimate the resolutions and deviations of reconstructed images in advance, and then can be used to optimize the PAE scan parameters. Groups of simulations as well as ex-vivo experiments with different scan parameters are provided to fully demonstrate the performance of the proposed techniques. The quantitatively measured angular resolutions and deviations agree well with our theoretical derivation results ${D}{\sqrt {r_{s}^{2} + \overline {h}^{{2}}} } / {[{1.25}({r}_{s} {r}_{d} + \overline {h}^{{2}})] }$ (rad) and $- \overline {h} {l} / ({r}_{s} {r}_{d} + \overline {h}^{{2}})$ (rad), respectively ${D},{r}_{d},\;{r}_{s},\overline {h} $ and ${l}$ represent transducer diameter, radius of scan trajectory, radius of source position, unit helical pitch and the distance from targets to helical scan plane, respectively). This theoretical result also suits for circular and cylindrical scan in case of $\overline {h} = {0}$ .

A novel coil-based electromagnetic-induced thermo-acoustic system is presented for detecting the flaws inside a rail. The fundamental is derived and the overall energy density distribution is simulated using finite element method. This paper gives an overview of the system architecture and describes the design process in detail. A mixed numerical experimental methodology is employed to extract the lumped parameters of a planar coil with the ferrite plate for designing the matching network, and then the coil and rail are co-simulated to observe the current density distributions and directions. Through the relationship of energy density and depth in the rail, it is found that the thermal energy mainly concentrates at the surface local area. From the interaction between the coil and rail, the inductive power transfer topology is illustrated and the simplified equivalent circuit model is further obtained. By analyzing the simulated and measured data, the changes in the resistance and inductance are shown with the frequency increasing. The induced ultrasonic wave propagation is simulated inside the rail with flaws, where the wavefronts and reflected signals are observed. Finally, the experimental results demonstrate that the proposed design is feasible and a crack with a diameter of 8 mm can be detected in the rail.

A handheld approach to 3D photoacoustic imaging is essential in clinical applications. To this end, we develop a 3D handheld photoacoustic imager for dynamic (temporally and spatially) volumetric visualization. In this 3D imager, the optically transmitting part and the acoustically receiving part are integrated into a single handheld probe with a compact size about 160 mm ×64 mm ×40 mm. Besides, a dedicated imaging reconstruction algorithm for the heterogeneous medium is developed based on the phase-shift migration method in the frequency domain, which deals well with the stratified condition in the designed system. Dynamic 3D imaging supporting flexible handheld operation is demonstrated with needle biopsy and in vitro temperature measurement for photothermal therapy. The development of such a 3D handheld photoacoustic system paves the way for compact and handheld-operating implementations, and its further clinical exploration is promising.