2024/03/07 by Ben Ecclestone, Ecclestone, Benjamin R., Alex Tummon Simmons +9 · 1 citation
Biochemistry, Genetics and Molecular Biology · #Advanced Biosensing Techniques and Applications #Advanced Fluorescence Microscopy Techniques #FOS: Physical sciences #Medical Physics (physics.med-ph) #Optics (physics.optics)
paper · pdf · doi:10.48550/arxiv.2403.04229
openalex publication_date 2024/03/07 · openalex created_date 2024/03/09 · openalex updated_date 2026/07/28
Label-free optical absorption microscopy techniques have evolved as effective tools for non-invasive chemical specific structural, and functional imaging. Yet most modern label-free microscopy modalities target only a fraction of the contrast afforded by an optical absorption interaction. We introduce a comprehensive optical absorption microscopy technique, Photon Absorption Remote Sensing (PARS), which simultaneously captures the dominant light matter interactions which occur as a pulse of light is absorbed by a molecule. In PARS, the optical scattering, attenuation, and the transient radiative and non-radiative relaxation processes are collected at each optical absorption event. This provides a complete representation of the absorption event, providing unique contrast presented here as the total absorption (TA) and quantum efficiency ratio (QER) measurements. By capturing a complete view of each absorption interaction, PARS bridges many of the specificity challenges associated with label-free imaging, facilitating recovery of a wider range of biomolecules than independent radiative or non-radiative modalities. To show the versatility of PARS, we explore imaging across a wide range of biological specimens, from single cells to in-vivo imaging of living subjects. These examples of label-free histopathological imaging, and vascular imaging illustrate some of the numerous fields where PARS may have profound impacts. Overall PARS may provide comprehensive label-free contrast in a wide variety of biological specimens, providing otherwise inaccessible visualizations, and representing a new a source of rich data to develop new AI and machine learning methods for diagnostics and visualization.