Friday, April 19, 2024
4/19/2024
Project Summary/Abstract: Overview of research in the laboratory: The Optical Imaging and Spectroscopy (OIS) laboratory focuses in de- veloping and advancing optical technologies to help improve the understanding of biological processes and the ability to identify disease. Specifically, the OIS lab focuses on label-free imaging, linear and nonlinear spectros- copy, and advanced signal processing techniques to gain access to novel forms of functional and molecular in- formation for a variety of applications. To date, these efforts have led to advances in spectroscopic optical co- herence microscopy (SOCT), quantitative phase imaging (QPI), nonlinear pump-probe microscopy, and stimu- lated Raman scattering (SRS). The lab has pioneered several new forms of molecular contrast, including molec- ular reorientation, nonlinear phase dispersion spectroscopy, ultraviolet hyperspectral imaging, and dispersion- based SRS. The lab has also developed new computational methods for analyzing spectral and morphological features in an unsupervised manner. Finally, these methods have been applied to help in cell identification and phenotyping, basic biological studies, disease detection and staging, and more. Goals for the next five years, and the overall vision of the research program: Recently, the OIS lab pioneered quantitative oblique back illumination microscopy (qOBM) and has made important advances in deep ultravio- let (UV) microscopy as a means to address critical challenges in biology and medicine. Over the five years of this proposal, this program seeks to explore the capabilities of these highly promising new tools. Through a combination of system/hardware and computational/software developments, the proposed program will show the tremendous utility of these label-free optical methods to reveal critical molecular, functional, and structural detail that is currently impossible to obtain or requires prohibitive expensive or complex systems.. The main focus of the work is on technology development, but it will also explore a number of applications that are in dire need of such label-free, non-invasive, low-cost, easy-to-use imaging methods. The proposed program will lead to a robust platform that will guide future innovation. A clear example is tying in the tremendous power of emerging computational methods, such deep neural networks for image transla- tion and classification. Additionally, with the advent of the proposed tools and a better understanding of their capabilities, researchers will be able to venture into more targeted effort to tailor these tools for specific appli- cations. Thus, the proposed work and the capabilities it will enable in the future can have tremendous potential to improve and influence biology and medicine.
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