New algorithm expands use of scientific CMOS cameras for biological microscopy

Algorithm compensates for fluctuations in individual pixel responses, allowing imaging of complex structures.

New algorithm expands use of scientific CMOS cameras for biological microscopy
New algorithm expands use of scientific CMOS cameras for biological microscopy
This photo shows the difference before and after use of the new algorithm. (Purdue University photo/ Sheng Liu and Fang Huang)

A new computer algorithm created by researchers at Purdue University (West Lafayette, IN) allows scientists to use scientific complementary metal-oxide semiconductor (sCMOS) cameras for a wide range of biological research.1

"Scientific sCMOS cameras are rapidly gaining popularity in biological sciences, material sciences and astronomy," says Fang Huang, an assistant professor in Purdue University's Weldon School of Biomedical Engineering. "The sensor provides significant advances in imaging speed, sensitivity and field of view compared with traditional detectors such as charge-coupled devices or electron multiplying CCDs." However, the use of sCMOS cameras for biological research has been limited because of fluctuations in pixel quality, generating more noise than other cameras. In particular, every pixel fluctuates at its own rate.

"When you are trying to use this for biological studies, it's very difficult to determine whether this fluctuation comes from the sample (photons) or from the camera itself," says Sheng Liu, a postdoctoral research associate at the Weldon School of Biomedical Engineering.

Now, working with other researchers in Purdue’s Department of Biological Sciences, Liu and Huang have developed a new algorithm that corrects the noise, making the sCMOS cameras available for a wide range of biological microscopy.

"We have been trying to use this camera for live-cell single-molecule super-resolution imaging and introduced an algorithm for that purpose in 2013," Huang says. "However, the previous algorithm works only for single-molecule studies, which means all your objects have to be so-called point emitters." Biological research, however, often involves imaging complex structures such as cellular organelles. The new algorithm solves this problem.

"The fundamental challenge is to estimate one of the variables when you know the sum of two variables," explains Huang. "There's no unique answer to this question, but we want to make the best estimate given our additional knowledge of the two variables. We exploited a general property of imaging systems, the optical transfer function. Based on our knowledge of how each of the 4 million pixels on our camera chip behave, we are able to estimate the actual photon level at each pixel location. This is very exciting for us because this allows CMOS sensors to be used in a broad spectrum of imaging methods for quantitative biomedical and biological studies, improving their sensitivity, field of view, and imaging speed."

The researchers filed a patent application on the algorithm through the Purdue Research Foundation's Office of Technology Commercialization.

Portions of the research were funded by the National Institutes of Health (NIH (R35 GM119785)), the Defense Advanced Research Projects Agency (D16AP00093) and the National Science Foundation (1146944-IOS).



1. Sheng Liu et al., Nature Methods (2017); doi:10.1038/nmeth.4379

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