Adaptive optics, computer software pair to correct aberrations in optical imaging

April 30, 2012
University of Illinois researchers have developed a technique to computationally correct for aberrations, enabling higher-quality images and 3D datasets in real-time imaging applications such as image-guided surgery.

University of Illinois (U of I; Champaign, IL) researchers have developed a technique to computationally correct for aberrations, enabling higher-quality images and 3D datasets in real-time imaging applications such as image-guided surgery, according to Steven Adie, a postdoctoral researcher at the Beckman Institute for Advanced Science and Technology at the U of I. The technique, which pairs adaptive optics with computer software, is promising for cancer diagnosis, minimally invasive surgery, and ophthalmology.

Adaptive optics—already used in astronomy to correct for distortion as starlight filters through the atmosphere—can correct aberrations in imaging: A complex system of mirrors smooth out the scattered light before it enters the lens. Medical scientists have begun applying adaptive optics hardware to microscopes, hoping to improve cell and tissue imaging.

Postdoctoral researcher Steven Adie, professor P. Scott Carney, graduate students Adeel Ahmad and Benedikt Graf, and professor Stephen Boppart, all of the University of Illinois, developed a method to computationally correct aberrations in 3D tissue microscopy.

But rather than using expensive, tedious hardware to correct a light profile before it enters the lens, the research team—led by Stephen Boppart, a professor of electrical and computer engineering, bioengineering, and internal medicine at the U of I—used computer software to find and correct aberrations after the image is taken.

Boppart's group teamed up with Scott Carney, a professor of electrical and computer engineering and the head of the Optical Science Group at the Beckman Institute, to develop the technique, called computational adaptive optics. They demonstrated the technique in gel-based phantoms laced with microparticles as well as in rat lung tissue. They scanned a tissue sample with an interferometric microscope (which uses two beams of light); then, the computer collects all of the data and then corrects the images at all depths within the volume. Blurry streaks become sharp points, features emerge from noise, and users can change parameters with the click of a mouse.

Aberrations in imaging can make points appear as slashes or blurs. (Image courtesy of Steven Adie)

Computational adaptive optics can be applied to any type of interferometric imaging, such as optical coherence tomography (OCT), and the computations can be performed on an ordinary desktop computer, making it accessible for many hospitals and clinics.

Next, the researchers are working to refine the algorithms and explore applications. They are combining their computational adaptive optics with graphics processors, looking forward to real-time in-vivo applications for surgery, minimally invasive biopsy, and others.

In ophthalmology, computational adaptive optics could be very useful. Boppart's group previously has developed various handheld optical tomography devices for imaging inside the eye, particularly retinal scanning. Aberrations are very common in the human eye, making it difficult to acquire clear images. But adaptive optics hardware is too expensive or too complicated for most practicing ophthalmologists. With a computational solution, more ophthalmologists could more effectively examine and treat their patients.

"Because of the aberrations of the human eye, when you look at the retina without adaptive optics you just see variations of light and dark areas that represent the rods and cones. But when you use adaptive optics, you see the rods and cones as distinct objects," explains Boppart.

In addition, the ability to correct data post-acquisition allowed the researchers to develop microscope systems that maximize light collection instead of worrying about minimizing aberrations. This could lead to better data for better image rendering.

The work has been published in the Proceedings of the National Academy of Sciences; for more information, please visit http://www.pnas.org/content/early/2012/04/17/1121193109.abstract?sid=c139a59d-8084-4606-80ee-c16314baa310.

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