The development of effective and safe agricultural treatments requires sub-cellular insight of the biochemical effects of treatments in living tissue in real-time. Industry-standard mass spectroscopic imaging lacks real-time
Fluorescence lifetime imaging microscopy (FLIM) is a powerful tool to quantify molecular compositions and study molecular states in complex cellular environment as the lifetime readings are not biased by fluorophore concentration or excitation power. However, the current methods to generate FLIM images are either computationally intensive or unreliable when the number of photons acquired at each pixel is low. Here we introduce a new deep learning-based method termed
- Award ID(s):
- 2041345
- NSF-PAR ID:
- 10361438
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Communications Biology
- Volume:
- 5
- Issue:
- 1
- ISSN:
- 2399-3642
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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