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Nizam, N. I.

Publications and source records attributed to Nizam, N. I..

2 recordsLinked to original sources

Depth-Resolved Macroscopic Fluorescence Lifetime Imaging via High-Spatial-Frequency Structured Illumination

SignificanceMacroscopic Fluorescence Lifetime Imaging (MFLI) is a powerful, non-invasive imaging modality that offers robust, physiologically relevant contrast largely independent of fluorophore concentration, excitation intensity, and tissue signal attenuation. However, accurately determining the depth of signal origin remains challenging, potentially leading to ambiguity in biological interpretation. Here, we present a novel optical correction method that effectively eliminates surface signal bias, such as that from skin in preclinical imaging, without the need for chemical clearance. This advancement supports the robust applicability of MFLI in translational research. AimEstablishment of a High Spatial Frequency-Fluorescence Lifetime Imaging (HSF-FLI) framework to selectively isolate subsurface fluorescence (deeper signals) from surface fluorescence, while preserving the accuracy of lifetime estimation. ApproachA modulation transfer function (MTF) that relates spatial frequency to penetration depth was derived using Monte Carlo eXtreme (MCX) simulations (for physics-based modeling) and validated with agar-based capillary phantoms on a time-gated ICCD-DMD system. Depth-independent fluorescence was decomposed into surface and subsurface components through structured three-phase sinusoidal illumination, and nonlinear least squares fitting was applied to recover lifetime or lifetime based parameters maps. HSF-FLI was demonstrated in vivo in mouse models bearing tumor xenogratfs and was cross validated with ex vivo measurements. ResultsWe extensively characterized the performance of High Spatial Frequency-Fluorescence Lifetime Imaging (HSF-FLI) through simulations and tissue-mimicking phantoms. The approach was further validated in vivo by assessing drug delivery in preclinical models using MFLI-FRET (Forster Resonance Energy Transfer). ConclusionBy coupling structured illumination with physics-based depth modeling, HSF-FLI delivers accurate, depth-selective lifetime readouts, setting the stage for robust and fast FLI implementation in translational studies.

bioengineering↗

A Novel Technique for Fluorescence Lifetime Tomography

Fluorescence lifetime imaging has emerged as a powerful tool for quantitatively assessing the molecular environment of live tissues in vivo. While fluorescence lifetime microscopy (FLIM) is a mature field, achieving effective 3D imaging in deep tissues has remained a significant challenge due to high scattering. In this study, we present a deep neural network-based approach, referred to as AUTO-FLI, which enables both 3D intensity and quantitative lifetime reconstructions at centimeters depth. This Deep Learning (DL)-based method incorporates an in silico framework to accurately generate fluorescence lifetime data for training and validation. The performance of this novel DL model is further validated with experimental data acquired on an anatomically accurate mouse-mimicking phantom. The results demonstrate that AUTO-FLI can provide precise 3D quantitative estimates of both intensity and lifetime distributions in highly scattering media. This method holds great promise for fluorescence lifetime-based molecular imaging at both the mesoscopic and macroscopic scales, with potential applications for pre-clinical translational research and fluorescence-guided surgery.

bioengineering↗