CAREER: Smart Imaging and Metrology enabled by Liquid Crystals

Imaging systems are used in a wide range of scientific and industrial applications, from examining biological samples in medical labs to manufacturing airplanes. There are two main reasons why an object is difficult to image optically: 1) the object does not significantly scatter or absorb light and is practically invisible, and/or 2) the object information is reduced by the imaging systems performance and resolving power. The challenge in current imaging technologies is the crucial need for smart and active systems that adapt themselves to increase the visibility of objects or features of interest with high specificity. This project addresses the requirements for a reconfigurable imaging system that provides quantitative information with high specificity and selectivity. This will enable faster and more accurate studies of biological and industrial samples. For example, automated and more accurate diagnoses will dramatically improve advances and outcomes in the fields of microbiology, personalized medicine, pathology, visual optics, brain imaging, and ultra-precision manufacturing industries. These applications can directly impact US national security, productivity, economic growth, and the health of its citizens. Improved quantitative information will provide more effective ways to harness 'big data' using artificial intelligence and machine learning algorithms. The project includes hands-on research and mentoring experiences in physics and photonics for high-school students and teaching strategies to foster the curiosity and talent of undergraduates, especially those from groups traditionally underrepresented in higher education. Specific outreach components are designed to make STEM education more inclusive and diverse, especially focusing on Hispanics and women including programs for Hispanic-serving high schools in North Carolina.

TECHNICAL DESCRIPTION:

This CAREER research project addresses the critical need for a reconfigurable imaging system that provides the required image information with quantitative attributes and high specificity by smart and adaptive selectivity of spatial frequency content. The spatial frequency content of an image carries information on the size, shape, and orientation of the object or features to be analyzed. Selective enhancement, or isolation of key spatial frequencies, for the object will include background noise from small-angle scatter, for example, which reduces visibility. The goal of this project is to address these issues and provide a new mathematical framework for wavefront, and hence spatial frequency, shaping to enhance those spatial frequencies relevant to improved imaging. This framework will provide new fundamental knowledge to advance nonlinear imaging and smart adaptive methods based on nonlinear effects for general imaging needs. The derived models will be validated in an optical system and contribute to study objects with low visibility (transparent and small area compared to the field of view). This research project will leverage smart optical materials for adaptive use and wavefront shaping techniques to extend the frontier of today's imaging systems. Research outcomes are expected to overcome signal-to-noise ratio limitations, provide active selectivity of an object's or it's feature's visibility, and thereby provide accurate 3D measurements and advance the field of quantitative imaging. This project aligns with the Ten Big Ideas from NSF by developing a method that can deliver quantitative information and parameters to harness massive data, enable automated analysis allowing collaboration between machines with humans, and strengthen networks of underrepresented minorities in STEM fields.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.