Much of the success of today's healthcare is due to rapid advances in our ability to collect and analyze high-resolution data about the human body. However, current methods to achieve cellular resolution are invasive (e.g., blood test or tissue biopsy), and non-invasive imaging modalities do not achieve cellular resolution. The principal goal of this Expeditions project is to develop computational imaging systems for non-invasive bio-imaging, deep beneath the skin, and at cellular-level resolutions. This project has the potential to fundamentally impact healthcare and medicine, by enabling live views of cross sections of human anatomy, simply by pointing a camera at any part of the body. This would put individual users at the center of their healthcare experience and make them true partners in their healthcare delivery. The health imaging devices that result from this project will act as an important pillar in the personalized medicine revolution. This research expedition also holds the potential to launch new healthcare paradigms for chronic disease management, pediatrics, low-resource healthcare, and disaster medical care. Beyond healthcare, making progress on the problem of cellular-scale deep-tissue imaging using light will push the frontiers of the fundamental problem of inverse scattering, which impacts numerous areas of science and engineering. The order of magnitude advances made in inverse scattering and imaging through scattering media will have significant cross-cutting applications in diverse areas such as basic science, consumer imaging, automotive navigation, robotics, surveillance, atmospheric science, and material science. Finally, projects with a single, easy-to-appreciate, and high-impact goal have the potential to inspire the next generation of scientists, attract diverse set of students driven by humanitarian and social causes, and become a platform for inclusion and innovation.
The overarching goal of this project is to develop, test, and validate new computational imaging systems, to non-invasively image below the skin at tunable depths, in highly portable form-factors such as wearables or point-of-care devices. The main challenge is that light scatters as it travels through the human body, and in this process, the spatial information from different points within the body gets mixed up. A new concept, Computational Photo-Scatterography (CPS), is being applied in this project in order to computationally unravel the scattered photons in an imaging system, and allow creation of sharp images and accurate inferences. Recognizing that the brute-force complexity of unraveling scattered photons is prohibitively high, the project uses a computational co-design framework that leverages advances by team members from multiple domains: programmable illumination and optics, image sensors, machine learning, inverse graphics, and hybrid analog-digital computing. The project will use machine learning (ML) instead of physics-based de-scattering to speed up the solution of the underlying inverse problem. A combination of physics-based inverse graphics algorithms, and ML algorithms combining deep learning and generative modeling will be used to estimate tissue scattering parameters - motion due to blood flow induces time-variation in tissue parameters, which makes solving the inverse scattering problem more difficult. The project will use ML to create fast but approximate estimators, which will serve as accelerators for inverse scattering. The development of new sensors, able to capture the data necessary to reconstruct the structure of the tissue deep below the skin, constitutes the most important contribution of the project. These systems and algorithms will have the potential to break the current resolution limits of noninvasive bio-imaging by nearly two orders of magnitude, enabling cellular-level imaging at depths far beyond currently possible.
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.
