Photoacoustic tomography (PAT) has the unique capability of visualizing optical absorption inside several centimeters-deep biological tissue with a high spatial resolution. However, single linear-array transducer-based PAT suffers from the limited-view challenge, and thus the synthetic aperture configuration is designed that still requires multichannel data acquisition hardware. Herein, a feasible synthetic aperture PAT based on compressed sensing reconstruction is proposed. Both the simulation and experimental results tested the theoretical model and validated that this approach can improve the image resolution and address the limited-view problem while preserving the target information with a fewer number of measurements.

Photoacoustic tomography is a noninvasive and nonionized biomedical imaging modality but it cannot reveal the inner structure and sideward boundary information of blood vessels in the linear array detection mode. In contrast, Monte Carlo (MC) light transport could provide the optical fluence distribution around the entire vascular area. This research explores the combination of linear array transducer-based photoacoustic tomography and MC light transport in the blood vessel quantification. Simulation, phantom, and in vivo experiments are in good correlation with the ultrasound imaging, validating this approach can clearly visualize the internal region of blood vessels from background tissue.

As a high-resulotion biological imaging technology, photoacoustic microscopy (PAM) is difficult to use in real-time imaging due to the long data acquisition time. Herein, a fast data acquisition and image recovery method named sparse PAM based on a low-rank matrix approximation is proposed. Specifically, the process to recover the final image from incomplete data is formulated into a low-rank matrix completion framework, and the “Go Decomposition” algorithm is utilized to solve the problem. Finally, both simulated and real PAM experiments are conducted to verify the performance of the proposed method and demonstrate clinical potential for many biological diseases.

The appearance of blood vessels is an important biomarker to distinguish diseased from healthy tissues in several fields of medical applications. Photoacoustic microangiography has the advantage of directly visualizing blood vessel networks within microcirculatory tissue. Usually these images are interpreted qualitatively. However, a quantitative analysis is needed to better describe the characteristics of the blood vessels. This Letter addresses this problem by leveraging an efficient multiscale Hessian filter-based segmentation method, and four measurement parameters are acquired. The feasibility of our approach is demonstrated on experimental data and we expect the proposed method to be beneficial for several microcirculatory disease studies.