WASHINGTON--(BUSINESS WIRE)--Researchers have repurposed an algorithm originally developed for Netflix’s 2009 movie preference prediction competition to create a method for acquiring classical Raman spectroscopy images of biological tissues at unprecedented speeds. The advance could make the simple, label-free imaging method practical for clinical applications such as tumor detection or tissue analysis.
In Optica, The Optical Society's journal for high-impact research, a multi-institutional group of researchers report that a computational imaging approach known as compressive imaging can increase imaging speed by reducing the amount of Raman spectral data acquired. They demonstrate imaging speeds of a few tens of seconds for an image that would typically take minutes to acquire and say that future implementations could achieve sub-second speeds.
The researchers accomplished this feat by acquiring only a portion of the data typically required for Raman spectroscopy and then filling in the missing information with an algorithm developed to find patterns in Netflix movie preferences. While the algorithm did not win Netflix’s $1 million prize, it has been used to meet other real-world needs, in this case a need for better biological imaging.
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“Although compressive Raman approaches have been reported previously, they couldn’t be used with biological tissues because of their chemical complexity,” said Hilton de Aguiar, leader of the research team at École Normale Supérieure in France. “We combined compressive imaging with fast computer algorithms that provide the kind of images clinicians use to diagnose patients, but rapidly and without laborious manual post-processing.”
Capturing biomedical processes
Raman spectroscopy is a non-invasive technique that requires no sample preparation to determine the chemical composition of complex samples. Although it has shown promise for identifying cancer cells and analyzing tissue for disease, it typically requires image acquisition speeds that are too slow to capture the dynamics of biological specimens. Processing the massive amount of data generated by spectroscopic imaging is also time-consuming, especially when analyzing a large area.
“With the methodology we developed, we addressed these two challenges simultaneously —increasing the speed and introducing a more straightforward way to acquire useful information from the spectroscopic images,” said de Aguiar.