Welcome to PsychiatryAI.com: [PubMed] - Psychiatry AI Latest

Spatial resolution enhancement in photon-starved STED imaging using deep learning-based fluorescence lifetime analysis

Evidence

Nanoscale. 2023 May 9. doi: 10.1039/d3nr00305a. Online ahead of print.

ABSTRACT

As a super-resolution imaging method, stimulated emission depletion (STED) microscopy has unraveled fine intracellular structures and provided insights into nanoscale organizations in cells. Although image resolution can be further enhanced by continuously increasing the STED-beam power, the resulting photodamage and phototoxicity are major issues for real-world applications of STED microscopy. Here we demonstrate that, with 50% less STED-beam power, the STED image resolution can be improved up to 1.45-fold using the separation of photons by a lifetime tuning (SPLIT) scheme combined with a deep learning-based phasor analysis algorithm termed flimGANE (fluorescence lifetime imaging based on a generative adversarial network). This work offers a new approach for STED imaging in situations where only a limited photon budget is available.

PMID:37159237 | DOI:10.1039/d3nr00305a

Document this CPD Copy URL Button

Google

Google Keep Add to Google Keep

LinkedIn Share Share on Linkedin Share on Linkedin

Estimated reading time: 3 minute(s)

Latest: Psychiatryai.com #RAISR4D

Real-Time Evidence Search [Psychiatry]

Spatial resolution enhancement in photon-starved STED imaging using deep learning-based fluorescence lifetime analysis

🌐 90 Days

Evidence Blueprint

Spatial resolution enhancement in photon-starved STED imaging using deep learning-based fluorescence lifetime analysis

QR Code

☊ AI-Driven Related Evidence Nodes

(recent articles with at least 5 words in title)

Save Evidence Blueprint

Save as PDF

Spatial resolution enhancement in photon-starved STED imaging using deep learning-based fluorescence lifetime analysis

🌐 365 Days

close chatgpt icon
ChatGPT

Enter your request.