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 eye imaging technology


How eye imaging technology could help robots and cars see better

#artificialintelligence

One of the imaging technologies that many robotics companies are integrating into their sensor packages is Light Detection and Ranging, or LiDAR for short. Currently commanding great attention and investment from self-driving car developers, the approach essentially works like radar, but instead of sending out broad radio waves and looking for reflections, it uses short pulses of light from lasers. Traditional time-of-flight LiDAR, however, has many drawbacks that make it difficult to use in many 3D vision applications. Because it requires detection of very weak reflected light signals, other LiDAR systems or even ambient sunlight can easily overwhelm the detector. It also has limited depth resolution and can take a dangerously long time to densely scan a large area such as a highway or factory floor.


Machine learning increases resolution of eye imaging technology

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Biomedical engineers at Duke University have devised a method for increasing the resolution of optical coherence tomography (OCT) down to a single micrometer in all directions, even in a living patient. The new technique, called optical coherence refraction tomography (OCRT), could improve medical images obtained in the multibillion-dollar OCT industry for medical fields ranging from cardiology to oncology. The results appear in a paper published online on August 19 in the journal Nature Photonics. "An historic issue with OCT is that the depth resolution is typically several times better than the lateral resolution," said Joseph Izatt, the Michael J. Fitzpatrick Professor of Engineering at Duke. "If the layers of imaged tissues happen to be horizontal, then they're well defined in the scan. But to extend the full power of OCT for live imaging of tissues throughout the body, a method for overcoming the tradeoff between lateral resolution and depth of imaging was needed."


Machine learning increases resolution of eye imaging technology

#artificialintelligence

The results appear in a paper published online on August 19 in the journal Nature Photonics. "An historic issue with OCT is that the depth resolution is typically several times better than the lateral resolution," said Joseph Izatt, the Michael J. Fitzpatrick Professor of Engineering at Duke. "If the layers of imaged tissues happen to be horizontal, then they're well defined in the scan. But to extend the full power of OCT for live imaging of tissues throughout the body, a method for overcoming the tradeoff between lateral resolution and depth of imaging was needed." OCT is an imaging technology analogous to ultrasound that uses light rather than soundwaves. A probe shoots a beam of light into a tissue and, based on the delays of the light waves as they bounce back, determines the boundaries of the features within.