Enhancing Fluorescence Lifetime Parameter Estimation Accuracy with Differential Transformer Based Deep Learning Model Incorporating Pixelwise Instrument Response Function
Ismail Erbas, Vikas Pandey, Navid Ibtehaj Nizam, Nanxue Yuan, Amit Verma, Margarida Barosso, Xavier Intes
TL;DR
The paper tackles the challenge of robust fluorescence lifetime parameter estimation in macroscopic imaging where pixel-wise IRF and depth-of-field variations distort timing. It introduces MFliNet, a DIFF Transformer–based model that ingests both TPSF data and pixel-wise IRF to predict bi-exponential lifetime parameters $\tau_1$, $\tau_2$, and $A_R$. MFliNet achieves accuracy comparable to pixel-wise NLSF across varying IRFs while delivering orders of magnitude faster inference, demonstrated on tissue-mimicking phantoms and in-vivo HER2+ tumor models, and showing resilience to IRF offsets. This work advances real-time, high-precision macroscopic fluorescence lifetime imaging with potential for improved fluorescence-guided surgery and translational imaging applications.
Abstract
Fluorescence Lifetime Imaging (FLI) is a critical molecular imaging modality that provides unique information about the tissue microenvironment, which is invaluable for biomedical applications. FLI operates by acquiring and analyzing photon time-of-arrival histograms to extract quantitative parameters associated with temporal fluorescence decay. These histograms are influenced by the intrinsic properties of the fluorophore, instrument parameters, time-of-flight distributions associated with pixel-wise variations in the topographic and optical characteristics of the sample. Recent advancements in Deep Learning (DL) have enabled improved fluorescence lifetime parameter estimation. However, existing models are primarily designed for planar surface samples, limiting their applicability in translational scenarios involving complex surface profiles, such as \textit{in-vivo} whole-animal or imaged guided surgical applications. To address this limitation, we present MFliNet (Macroscopic FLI Network), a novel DL architecture that integrates the Instrument Response Function (IRF) as an additional input alongside experimental photon time-of-arrival histograms. Leveraging the capabilities of a Differential Transformer encoder-decoder architecture, MFliNet effectively focuses on critical input features, such as variations in photon time-of-arrival distributions. We evaluate MFliNet using rigorously designed tissue-mimicking phantoms and preclinical in-vivo cancer xenograft models. Our results demonstrate the model's robustness and suitability for complex macroscopic FLI applications, offering new opportunities for advanced biomedical imaging in diverse and challenging settings.