Denoising of fluorescence lifetime imaging data via principal component analysis.

ABSTRACT: Fluorescence Lifetime Imaging Microscopy (FLIM) quantifies autofluorescence lifetime to assess cellular metabolism, therapeutic efficacy, and disease progression. These dynamic and heterogeneous processes complicate signal analysis. Fit-free analysis methods such as phasor analysis are increasingly used due to limitations of fit-based approaches. However, incorporating photon-counting shot noise often leads to moderate-to-high uncertainty in detecting subtle changes. Common noise-reduction strategies can introduce errors and cause data loss. We developed noise-corrected principal component analysis (NC-PCA), which selectively identifies and removes noise to isolate the signal of interest. We validated NC-PCA by analyzing FLIM images of patient-derived colorectal cancer organoids treated wi

SUBMITTER: Soltani S 

PROVIDER: S-EPMC12623853 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature