Quantitative phase imaging (QPI) offers high-resolution, high-sensitivity imaging of transparent biological specimens without the need for contrast agents by measuring the optical delay induced by their inherent structures. QPI can provide an objective measure of cell morphology and dynamics based on the measured refractive index and dry mass. We have employed QPI to study cellular senescence, drug response, and glial development.

Combining AI tools with 2D-4D imaging of biological specimens unlocks new approaches to interpreting complex biological processes, dynamics, and mechanisms. We apply machine learning algorithms to analyze high-volume spectral and image datasets, distinguish subtle cues, and synthesize collective information to provide unbiased insights into cell and tissue specimens. Our work involves virtual staining of cells and tissues to support clinical decisions.

While additional modes of visualization complement the information obtained via a single modality imaging, effectively integrating this information is an extensive process. We develop a practical, streamlined workflow and platform that can minimize extra steps without compromising spectral/imaging quality.