Proceedings of the National Academy of Sciences of the United States of America, 116(48), 24019-24030 (2019-11-14)
Fluorescence lifetime imaging (FLI) provides unique quantitative information in biomedical and molecular biology studies but relies on complex data-fitting techniques to derive the quantities of interest. Herein, we propose a fit-free approach in FLI image formation that is based on
Reactive oxygen species play an important role in cancer, however, their promiscuous reactivity, low abundance, and short-lived nature limit our ability to study them in real time in living subjects with conventional noninvasive imaging methods. Photoacoustic imaging is an emerging
Macroscopic fluorescence lifetime imaging (MFLI) via compressed sensed (CS) measurements enables efficient and accurate quantification of molecular interactions in vivo over a large field of view (FOV). However, the current data-processing workflow is slow, complex and performs poorly under photon-starved
Many imaging probes have been developed for a wide variety of imaging modalities. However, no optical imaging probe could be utilized for both microscopic and whole animal imaging. To fill the gap, the dual-wavelength fluorescent imaging nanoprobe was developed to
Single pixel imaging frameworks facilitate the acquisition of high-dimensional optical data in biological applications with photon starved conditions. However, they are still limited to slow acquisition times and low pixel resolution. Herein, we propose a convolutional neural network for fluorescence
Graphene is the building block for carbon nanomaterials with different dimensionalities.
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