Quantitative phase imaging andcomputational specificity (Popescu)

SUMMARY
TRD 1 aims to translate QPI technology to in-vivo and deep-tissue imaging with specific markers developed via
computation and deep-learning. Quantitative phase imaging (QPI) is emerging as a powerful, label-free approach
to imaging cells and tissues, especially because it combines qualities found in microscopy, holography, and light
scattering techniques: nanoscale sensitivity to morphology and dynamics, 2D, 3D, and 4D (i.e., time-resolved
tomography) nondestructive imaging of completely transparent structures, and quantitative signals based on
intrinsic contrast. These capabilities have allowed QPI to be successfully applied in numerous biomedical
applications, including cancer diagnosis in histopathology and cell therapy. Recently, we have expanded QPI for
the first time to thick structures, such as embryos and spheroids, by developing gradient light interference
microcopy (GLIM, the 2018 Microscopy Today Method of the Year). However, despite enormous progress,
current QPI techniques are virtually absent from in-vivo and POC applications.
We will advance the QPI technology to a confocal reflection geometry, thus, boosting the out of focus light
rejection and improving high-resolution 3D imaging of thick tissue structures. Specifically, we will target first
imaging the 3D orientation of skin collagen in-vivo. We will develop a label-free endoscopic system (eGLIM)
capable of sub-micron spatial and millisecond temporal resolution, while maintaining nanometer pathlength
sensitivity. We will advance phase imaging with computational specificity (PICS) to real-time operation on in-vivo
data from CPT (Aim 1) and eGLIM (Aim 2). Specifically, in close collaboration with TRD 3, we will develop
computational tools for segmenting cellular and subcellular structures in spheroids, identifying collagen fibers
from in-vivo CPT skin data, developing rapid label-free viral testing, nondestructive live/dead cell assays, label-
free cell cycle phase identification.

Grant Number: 5P41EB031772-04
NIH Institute/Center: NIH
Principal Investigator: Rohit Bhargava