CAREER: Computational Optics and Photonics for Deep Imaging of Live Tissue

University Of Puerto Rico Mayaguez, Mayaguez PR

Investigators

Abstract

Innovation in non-invasive imaging techniques is part of the effort to provide rapid screening, diagnosis as well as to guide treatment in numerous settings that aspire to offer affordable and efficient healthcare. Most of the existing high-resolution methods are effective primarily on thin and nearly homogeneous transparent samples or over tissue surface. In most realistic scenarios, it is important to acquire information at depth within tissue. High-resolution volumetric imaging approaches may require expensive computational tools for data analysis and complex hardware configurations. Computational optics grounded on signal processing and image reconstruction concepts offers promising alternatives. This research contributes to advance the related state-of-the-art in translational cyberinfrastructure and biomedical technology. Results from this research can improve non-invasive imaging systems for research and patient care while supporting the NSF mission to promote the progress of science and advance the national health. The development of this project involves multidisciplinary efforts from computer science, bioengineering and electrical engineering as well as educational activities with the participation of students from underrepresented groups. This project focuses on providing a framework to support advances on optical imaging techniques that can perform at the needed resolution and speed for various scenarios such as healthcare and biomedical research. The research plan is geared to creating an advanced cyberinfrastructure with simulation and analysis tools to build a computational optical system for deep imaging of live tissues. The components of the framework include three-dimensional optical imaging models employing nonlinear scattering theory that integrate tissue optical properties to characterize their effect into the imaging resolution performance. Additionally, it includes the integration of light-tissue-interaction modeling parameters with compressive sensing concepts and machine learning algorithms for advanced data management. This project targets realistic challenges in biomedical research, including (i) a gap between complex physics of light propagation in tissues and the design of efficient high-resolution imaging systems, (ii) computational optics and photonics for deep imaging of live tissues, and (iii) integration with reliable and state-of-the-art data analytics and visualization environments. The simulations and computational optics tools focus on confocal imaging of skin tissue, which is widely used in biomedical research, and is potentially adoptable in the clinic to guide diagnosis of skin conditions. The education plan addresses three major areas: i) research training and experiences for graduate and undergraduate students, i) course development in topics related with computational optics and data analytics, and iii) outreach to K-12 students and professionals to introduce research issues and opportunities in computational imaging and data analytics. 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.