Enhancing Fluorescence Lifetime Parameter Estimation Accuracy with Differential Transformer Based Deep Learning Model Incorporating Pixelwise Instrument Response Function

TL;DR

This paper introduces MFliNet, a deep learning model using a Differential Transformer architecture that incorporates the Instrument Response Function to improve fluorescence lifetime parameter estimation in complex, real-world biomedical imaging scenarios.

Contribution

The paper presents a novel DL architecture, MFliNet, that integrates IRF as input and is effective for macroscopic fluorescence lifetime imaging in complex tissue environments.

Findings

MFliNet accurately estimates fluorescence lifetime parameters in tissue-mimicking phantoms.

The model demonstrates robustness in preclinical in-vivo cancer models.

It outperforms existing models limited to planar samples.

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.