Photoacoustic imaging (PAI) is a promising emerging imaging modality that enables
spatially resolved imaging of optical tissue properties up to several centimeters deep in …

Abstract Machine learning has been developed dramatically and witnessed a lot of
applications in various fields over the past few years. This boom originated in 2009, when a …

Deep learning (DL) has become widely used for medical image segmentation in recent
years. However, despite these advances, there are still problems for which DL-based …

Significance: Photoacoustic (PA) imaging can provide structural, functional, and molecular
information for preclinical and clinical studies. For PA imaging (PAI), non-ideal signal …

Significance: Acoustically detecting the rich optical absorption contrast in biological tissues,
photoacoustic tomography (PAT) seamlessly bridges the functional and molecular sensitivity …

This article reviews deep learning applications in biomedical optics with a particular
emphasis on image formation. The review is organized by imaging domains within …

Conventional reconstruction methods for photoacoustic images are not suitable for the
scenario of sparse sensing and geometrical limitation. To overcome these challenges and …

The reconstruction of computed tomography (CT) images is an active area of research.
Following the rise of deep learning methods, many data-driven models have been proposed …

Significance: The proposed binary tomography approach was able to recover the
vasculature structures accurately, which could potentially enable the utilization of binary …

Photoacoustic computed tomography (PACT) combines the optical contrast of optical
imaging and the penetrability of sonography. In this work, we develop a novel PACT system …