Review of Deep Learning Approaches for Interleaved Photoacoustic and Ultrasound (PAUS) Imaging

Abstract

Photoacoustic (PA) imaging provides optical contrast at relatively large depths within the human body, compared to other optical methods, at ultrasound (US) spatial resolution. By integrating real-time PA and US (PAUS) modalities, PAUS imaging has the potential to become a routine clinical modality bringing the molecular sensitivity of optics to medical US imaging. For applications where the full capabilities of clinical US scanners must be maintained in PAUS, conventional limited view and bandwidth transducers must be used. This approach, however, cannot provide high-quality maps of PA sources, especially vascular structures. Deep learning (DL) using data-driven modeling with minimal human design has been very effective in medical imaging, medical data analysis, and disease diagnosis, and has the potential to overcome many of the technical limitations of current PAUS imaging systems. The primary purpose of this article is to summarize the background and current status of DL applications in PAUS imaging. It also looks beyond current approaches to identify remaining challenges and opportunities for robust translation of PAUS technologies to the clinic.

Deep Learning-Based Super-Resolution Reconstruction and Segmentation of Photoacoustic Images

“…Therefore, despite the great progress in PAI, there are still great challenges in image quality improvement and accurate segmentation of tissue structures [18,19]. Traditional photoacoustic image reconstruction and segmentation methods often rely on hand-designed feature extractors and mathematical models, which have many limitations in dealing with complex backgrounds, noise interference, and blurred organizational boundaries [20,21].…”