FLIM quality metric visualization as a means to validate consistency across large-area non-homogeneous FLIM datasets

Robust and interpretable analysis of fluorescence lifetime imaging microscopy (FLIM) data requires careful assessment of data across biological samples. Due to limitations in sample availability, difference in protein expression, photobleaching, or acquisition time, FLIM datasets are often susceptible to signal variability. This is only exacerbated with large field-of-view FLIM data, such as examining metabolic fluxes across whole tissue slices due to morphology changes. We adapt the FLIM F-value (or figure-of-merit) within our analysis as a statistical metric to capture the confidence in lifetime by comparing variance across fitted parameters, analogous to typical image SNR. In this study, we apply pixelwise and regional analysis of F-values across large-area FLIM datasets to identify image regions with similar confidence levels. Visualization of F-value distribution enables detection of acquisition outliers or poor-quality regions within a large mosaic collection, which can be flagged for reacquisition or removal. This approach enhances the statistical power of downstream biological interpretation by ensuring that only data with quantifiable and stable lifetime information are retained. To our knowledge, this is the first application of F-value mapping as a dataset-wide quality control measure in FLIM.