Quantitative phase imaging trends in biomedical applications. (December 2020)

• Record Type: Journal Article
• Authors: Cacace, Teresa; Bianco, Vittorio; Ferraro, Pietro
• Is Part Of: Optics and lasers in engineering. Volume 135(2020)
• Publication Date: 2020-12
• Subjects: Quantitative phase imaging -- Microscopy -- Tomography -- Image cytometry -- Compact imaging -- Machine learning -- Label-free; Lasers in engineering -- Periodicals; Optical measurements -- Periodicals; Optics -- Periodicals; Lasers en ingénierie -- Périodiques; Mesures optiques -- Périodiques; Optique -- Périodiques; 621.36605
• DOI: 10.1016/j.optlaseng.2020.106188
• Languages: English
• ISSNs: 0143-8166

Abstract

Highlights: Provided an overview of the recent developments in the use of quantitative phase information for biomedical applications. Provided a schematic overview of the currently developed techniques. Identified four trends of relevance, i.e. imaging cytometry, setup compactness, 3D phase imaging, AI-assisted methods. Analysed key aspects of these trends, stressing relevant applications and potentialities. Abstract: In the last decades, the understanding of the complex biological mechanisms that regulate the life and growth of single cells and their reciprocal interaction has steadily increased. In this process, Quantitative Phase Imaging (QPI) has emerged as an invaluable tool, thus providing a new way of investigating live cell behaviour in a dynamic, label-free and non-invasive modality. It has proved especially relevant to biomedical and diagnostic applications, as the information-rich quantitative phase data opens new channels to investigate pathophysiology. In this review, we expose the recent developments in the extraction of unique biological information, and we provide a schematic overview of the currently developed techniques. Specifically, we identify four different trends of relevance in the context of biomedical applications, namely QPI-cytometry, development of point-of-care devices, tomographic phase reconstruction techniques and learning-based approaches, providing a current insight in the way they are shifting our approach to the acquisition and treatment of QPI data.