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Deep Learning Approach Speeds Up Fluorescence Lifetime Imaging

An article published in Sensors reveals fluorescence lifetime imaging (FLIM), an important tool that provides unique information for biomedical research. The FLIM-MLP-Mixer, a multi-layer perceptron-based mixer (MLP Mixer), and a deep learning (DL) technique are used for quick and reliable fluorescence lifetime imaging analysis.

New imaging method finds causes of cerebral edema

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Redefining Fluorescence Lifetime Imaging Microscopy

Redefining Fluorescence Lifetime Imaging Microscopy Image Credit: Micha Weber/Shutterstock.com Conventional fluorescence lifetime imaging microscopy (FLIM) relies on point-to-point scanning of a tightly focused illumination beam across the sample to form an image, impeding rapid imaging. To overcome this limitation, researchers from Japan and the USA have developed scan-less wide-field imaging methods that can image fluorescence lifetime at unprecedented speeds and capture ultrafast transient phenomena relevant to biomedical research and life sciences. FLIM is an imaging technique that creates spatially resolved images of a fluorophore (fluorescent molecule used as a marker) lifetime. The fluorescence lifetime is an intrinsic property of the fluorophore, which describes, on average, how rapidly the fluorophore s excited state decays.

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