Sounding out the dynamics: a concise review of high-speed photoacoustic microscopy.

Significance: Photoacoustic microscopy (PAM) offers advantages in high-resolution and high-contrast imaging of biomedical chromophores. The speed of imaging is critical for leveraging these benefits in both preclinical and clinical settings. Ongoing technological innovations have substantially boosted PAM's imaging speed, enabling real-time monitoring of dynamic biological processes.

Real-time whole-brain imaging of hemodynamics and oxygenation at micro-vessel resolution with ultrafast wide-field photoacoustic microscopy.

High-speed high-resolution imaging of the whole-brain hemodynamics is critically important to facilitating neurovascular research. High imaging speed and image quality are crucial to visualizing real-time hemodynamics in complex brain vascular networks, and tracking fast pathophysiological activities at the microvessel level, which will enable advances in current queries in neurovascular and brain metabolism research, including stroke, dementia, and acute brain injury. Further, real-time imaging of oxygen saturation of hemoglobin (sO) can capture fast-paced oxygen delivery dynamics, which is needed to solve pertinent questions in these fields and beyond.

Deep-E: A Fully-Dense Neural Network for Improving the Elevation Resolution in Linear-Array-Based Photoacoustic Tomography.

Linear-array-based photoacoustic tomography has shown broad applications in biomedical research and preclinical imaging. However, the elevational resolution of a linear array is fundamentally limited due to the weak cylindrical focus of the transducer element. While several methods have been proposed to address this issue, they have all handled the problem in a less time-efficient way.

Deep image prior for undersampling high-speed photoacoustic microscopy.

Photoacoustic microscopy (PAM) is an emerging imaging method combining light and sound. However, limited by the laser's repetition rate, state-of-the-art high-speed PAM technology often sacrifices spatial sampling density (, undersampling) for increased imaging speed over a large field-of-view. Deep learning (DL) methods have recently been used to improve sparsely sampled PAM images; however, these methods often require time-consuming pre-training and large training dataset with ground truth.

Sounding out the hidden data: A concise review of deep learning in photoacoustic imaging.

The rapidly evolving field of photoacoustic tomography utilizes endogenous chromophores to extract both functional and structural information from deep within tissues. It is this power to perform precise quantitative measurements with endogenous or exogenous contrastthat makes photoacoustic tomography highly promising for clinical translation in functional brain imaging, early cancer detection, real-time surgical guidance, and the visualization of dynamic drug responses. Considering photoacoustic tomography has benefited from numerous engineering innovations, it is of no surprise that many of photoacoustic tomography's current cutting-edge developments incorporate advances from the equally novel field of artificial intelligence.

Reconstructing Undersampled Photoacoustic Microscopy Images Using Deep Learning.

One primary technical challenge in photoacoustic microscopy (PAM) is the necessary compromise between spatial resolution and imaging speed. In this study, we propose a novel application of deep learning principles to reconstruct undersampled PAM images and transcend the trade-off between spatial resolution and imaging speed. We compared various convolutional neural network (CNN) architectures, and selected a Fully Dense U-net (FD U-net) model that produced the best results.