Medical ultrasound is an imaging technique that utilizes ultrasonic signals as information carriers, and has wide applications such as seeing internal body structures, finding a source of a disease, and examining pregnant women. The most commonly used ultrasonic transducer today is based on piezoelectricity. The piezoelectric transducer, however, may have a limited bandwidth and insufficient sensitivity for reduced element size. Laser-generated ultrasound (LGUS) technique is an effective way to resolve these issues. The LGUS approach based on photoacoustic effect is able to greatly enhance the bandwidth of ultrasound signals and has the potential for high-resolution imaging. High-amplitude LGUS could also be used for therapy to accomplish high precision surgery without an incision. Furthermore, LGUS in conjunction with optical detection of ultrasound allows all-optical ultrasound imaging (i.e., ultrasound is generated and received optically). The all-optical platform offers unique advantages in providing high-resolution information and in facilitating the construction of miniature probes for endoscopic ultrasound. In this article, a detailed review of the recent development of various LGUS transmitters is presented. In addition, a recent research interest in all-optical ultrasound imaging, as well as its applications, is also discussed.

The point-by-point scanning mechanism of photoacoustic microscopy (PAM) results in low-speed imaging, limiting the application of PAM. In this work, we propose a method to improve the quality of sparse PAM images using convolutional neural networks (CNNs), thereby speeding up image acquisition while maintaining good image quality. The CNN model utilizes attention modules, residual blocks, and perceptual losses to reconstruct the sparse PAM image, which is a mapping from a 1/4 or 1/16 low-sampling sparse PAM image to a latent fully-sampled one. The model is trained and validated mainly on PAM images of leaf veins, showing effective improvements quantitatively and qualitatively. Our model is also tested using in vivo PAM images of blood vessels of mouse ears and eyes. The results suggest that the model can enhance the quality of the sparse PAM image of blood vessels in several aspects, which facilitates fast PAM and its clinical applications.

Acoustic-resolution photoacoustic microscopy (ARPAM) plays an important role in studying the microcirculation system of biological tissues with deep penetration. High lateral resolution of ARPAM is achieved by using a high numerical aperture acoustic transducer. The deteriorated lateral resolution in the out-of-focus region can be alleviated by synthetic aperture focusing technique (SAFT). Previously, we reported a three-dimensional (3D) deconvolution ARPAM to improve both lateral and axial resolutions in the focus region. In this study, we present our extension of resolution enhancement to the out-of-focus region based on two-dimensional SAFT combined with the 3D deconvolution (SAFT+Deconv). In both the focus and out-of-focus regions, depth-independent lateral resolution provided by SAFT, together with inherently depth-independent axial resolution, ensures a depth-independent point spread function for 3D deconvolution algorithm. Imaging of 10 μm polymer beads shows that SAFT+Deconv ARPAM improves the −6 dB lateral resolutions from 65–700 μm to 20–29 μm, and the −6 dB axial resolutions from 35–42 μm to 12–19 μm in an extended depth of focus (DOF) of ∼2 mm. The signal-to-noise ratio is also increased by 6–30 dB. The resolution enhancement in three dimensions is validated by in vivo imaging of a mouse’s dorsal subcutaneous microvasculature. Our results suggest that SAFT+Deconv ARPAM may allow fine spatial resolution with deep penetration and extended DOF for biomedical photoacoustic applications.

As a hybrid imaging technology, photoacoustic microscopy (PAM) imaging suffers from noise due to the maximum permissible exposure of laser intensity, attenuation of ultrasound in the tissue, and the inherent noise of the transducer. De-noising is an image processing method to reduce noise, and PAM image quality can be recovered. However, previous de-noising techniques usually heavily rely on manually selected parameters, resulting in unsatisfactory and slow de-noising performance for different noisy images, which greatly hinders practical and clinical applications. In this work, we propose a deep learning-based method to remove noise from PAM images without manual selection of settings for different noisy images. An attention enhanced generative adversarial network is used to extract image features and adaptively remove various levels of Gaussian, Poisson, and Rayleigh noise. The proposed method is demonstrated on both synthetic and real datasets, including phantom (leaf veins) and in vivo (mouse ear blood vessels and zebrafish pigment) experiments. In the in vivo experiments using synthetic datasets, our method achieves the improvement of 6.53 dB and 0.26 in peak signal-to-noise ratio and structural similarity metrics, respectively. The results show that compared with previous PAM de-noising methods, our method exhibits good performance in recovering images qualitatively and quantitatively. In addition, the de-noising processing speed of 0.016 s is achieved for an image with 256×256 pixels, which has the potential for real-time applications. Our approach is effective and practical for the de-noising of PAM images.