A. Rudkouskaya

This paper compares intensity-based and fluorescence lifetime-based FRET methods for in vivo small animal imaging, showing that lifetime-based FRET is more efficient and accurate for quantifying drug-target engagement.

This study demonstrates the use of a time-gated SPAD camera for in vitro and in vivo NIR fluorescence lifetime imaging, showing its effectiveness in monitoring molecular events in tumor xenografts.

This paper introduces a deep learning framework for fast hyperspectral fluorescence lifetime imaging, achieving rapid acquisition and processing without training data.

This paper develops a multiplexed imaging method to simultaneously assess tumor metabolism and receptor engagement in vivo, revealing an inverse relationship between metabolic activity and drug delivery efficacy.

This paper introduces FLI-Net, a deep learning model that enables ultra-fast, fit-free analysis of fluorescence lifetime imaging data across multiple spectral ranges and acquisition technologies.

The paper introduces a method using phasor analysis and NIR dyes to quantify fluorescence lifetime and stoichiometry in vitro and in vivo, demonstrating its application to FRET and pharmacokinetics studies.

This study develops MFLI-FRET to quantify receptor-ligand engagement in live animals, showing it correlates with intracellular ligand binding rather than ex vivo receptor expression.