What’s the size of a grain of salt, and could diagnose disease from inside your body? A microscopic camera, of course.
It’s long been thought that microscopic cameras could revolutionise diagnostic capability in medicine, enabling doctors to spot problems inside the human body, or even to augment sensing by super-small robots.
But while scientists have made microscopic cameras in the past, they’ve been hampered by fuzzy imagery and limited fields of vision. Now, a newly developed system can produce crisp, full-colour images that rival their full-sized counterparts, according to a new paper out today in Nature Communications.
The design was developed by a team of researchers from Princeton University and the University of Washington, US.
A new way to capture images
While traditional cameras use a series of curved glass or plastic lenses to bend light rays into focus, the system used in this new microscopic camera relies on a technology called metasurface, which can be produced much like a computer chip.
The metasurface is half a millimetre wide, and studded with 1.6 million cylindrical posts, each of which, for scale, is roughly the size of the human immunodeficiency virus (HIV).
Each post has its own unique geometry – so that the entire optical wavefront is picked up – and works as an optical antenna. Using machine learning algorithms, the posts’ interactions with light are able to produce high quality images with a wide field of view.
“It’s been a challenge to design and configure these little microstructures to do what you want,” says Ethan Tseng, a computer science PhD student at Princeton who co-led the study.
“For this specific task of capturing large field-of-view RGB images, it’s challenging because there are millions of these little microstructures, and it’s not clear how to design them in an optimal way.”
To address this major challenge, co-author Shane Colburn created a computational simulator to automate testing of different nano-antenna configurations; otherwise, establishing how and where to put each post, and what shape it should be, would have been difficult to impossible.
Machine-learning algorithms also help the camera to image objects and surfaces in natural light, rather than under the intense laser light of a laboratory – making the camera ideal for imaging within the body, where light is obviously scarce.
These complex surfaces were fabricated by co-author James Whitehead, basing them on silicon nitride, a glass-like material that can be produced in much the same way as standard computer chips – meaning production of these nano-cameras could easily be scaled up.
When their nano-camera’s images were compared with the image quality of a conventional camera some 500,000 times larger, the team found comparable results, highlighting the remarkable efficacy of this minute miracle camera.
Joseph Mait, a former senior researcher and chief scientist at the US Army Research Laboratory and an expert in optic design, says this is the first microscopic optical system that combines innovative surface technology for capturing imagery with machine learning.
“The significance of the published work is completing the Herculean task to jointly design the size, shape and location of the metasurface’s million features and the parameters of the post-detection processing to achieve the desired imaging performance,” says Mait, who was not involved in the study.
Felix Heide, senior author of the study and an assistant professor of computer science at Princeton, envisions a bold future for this kind of technology. “We could turn individual surfaces into cameras that have ultra-high resolution, so you wouldn’t need three cameras on the back of your phone anymore, but the whole back of your phone would become one giant camera,” he says. “We can think of completely different ways to build devices in the future.”
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