OR WAIT null SECS
Researchers have developed a way to acquire classical Raman spectroscopy images more quickly.
Researchers used compressive imaging as a way to acquire biological images at increased speeds, according to the results of found a study published in Optica.
The research team 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 increased speeds. ​​​​Typically, it would take minutes to acquire images, but with the help of the algorithm, imaging was completed in a few tens of seconds.
Along with speeding up the process, the method also accomplished a high-level of data compression — reducing the data up to 64 times.
To do this, the researchers acquired only a portion of the data typically required for Raman spectroscopy, then filled in the missing information with the algorithm that was developed to find patterns in Netflix movie preferences.
“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,” said Hilton de Aguiar, leader of the research team at École Normale Supérieure in France.
The team sped up the imaging process by making their Raman system more compatible with the algorithm by replacing the typical and expensive multi-pixel camera with a spatial light modulator — a fast digital micromirror device. The modulator selects groups of wavelengths that are detected by a highly-sensitive single-pixel detector and compresses the images as they are retrieved.
With traditional Raman spectroscopy, data would be gathered, then an algorithm would run to pull out useful information. With this approach, the algorithm does post-processing while retrieving the data with measurements, de Aguiar told Inside Digital Health™.
De Aguiar said that the new methodology can be used for many diagnostics and applications and could be practical for clinical applications such as tumor detection or tissue analysis.
“The idea of this new approach is that we can do this in a much more simplified way,” he told us.
Get the best insights in healthcare analytics directly to your inbox.