New imaging technique sees through deep tissue, dense fog, and other obstacles
The AI-enhanced technology could improve non-invasive cancer imaging, lower costs, and make LiDAR systems more effective in poor visibility
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As light passes through a a thin film made of indium tin oxide, any near-infrared photons that hit it are converted to visible light for a clear picture in real-time. The technology is part of a new imaging system developed by URochester researchers to see through deep tissue, dense fog, and other obstacles.
view moreCredit: URochester photo / J. Adam Fenster
From helping doctors detect cancer to guiding self-driving cars through traffic, many modern imaging systems rely on near-infrared light, producing a crisp picture when visible light would scatter and yield a blurry picture. But near-infrared systems struggle when light passes through materials like deep tissue or dense fog, succumbing to the same scattering effect where photons deviate from their path. Existing near-infrared imaging systems also rely on specialized detectors made from expensive materials, limiting their affordability and widespread use.
University of Rochester researchers have now developed a lower-cost imaging system that overcomes both challenges. Using inexpensive silicon-based detectors, the system quickly converts near-infrared light to visible light while producing clearer images through these difficult environments. The technology, outlined in a recent Nature Communications paper, uses a technique called time-gating that the laboratory of Robert Boyd, the William F. Krupke Distinguished Professor in Optics, has spent more than a decade refining.
Light controlling light
“Time-gating essentially works like the shutter in a camera,” says Yang Xu ’26 (PhD), the lead author of the paper. “In a traditional camera, the shutter is mechanical—when it opens, light comes in, and when it closes, light is rejected. In this case, we use light to control light.”
Ultrafast bursts of light act as the shutter, letting infrared particles through the gate for only about a picosecond. For reference, a picosecond is the time it takes for light to travel a distance of the size of a period at the end of a sentence.
The gate is a thin film made of indium tin oxide, and any near-infrared photons that hit it are converted to visible light for a clear picture in real-time.
The approach could improve image quality for applications ranging from biomedical imaging for cancer detection to LiDAR (light detection and ranging) systems used in autonomous vehicles, where fog and other light-scattering conditions can limit performance.
AI broadens the view
While the time-gating technique produced remarkably clear images, Boyd, Xu, and their colleagues found a way to make the system even more useful. Working with researchers at UCLA, they combined their approach with machine learning to dramatically expand the system’s field of view. Their findings appear in a recent paper published in Light: Science and Applications.
“Before applying artificial intelligence, we could see only a limited field of view,” says Xu. “By adding our collaborators’ methods, we can essentially reconstruct a much larger target area, enlarging the field of view our ultrafast time-gating technique can capture.”
Other University of Rochester collaborators involved in the studies include optics alumna Saumya Choudhary ’23 (PhD) and physics doctoral student Long Nguyen. The US Office of Naval Research, the National Science Foundation, and the Department of Energy provided funding for the research.
The lab of Robert Boyd, right, the William F. Krupke Distinguished Professor in Optics, has spent more than a decade refining the new imaging system, with contributions from PhD students including Yang Xu ’26 (PhD), center, and physics doctoral student Long Nguyen, left.
Credit
URochester photo / J. Adam Fenster
Journal
Light: Science & Applications
Article Title
Hybrid deep reconstruction for vignetting-free upconversion imaging through scattering in epsilon-near-zero materials
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