A Novel Autoencoder Design for Bi-exponential Fluorescence Lifetime Imaging

Abstract

Fluorescence Lifetime Imaging Microscopy is a highly sensitive analytical technique used to monitor real-time metabolic events and cellular biochemistry by measuring the decay time of fluorophores like nicotinamide adenine dinucleotide (NADH). Applying this technique to live cells requires low laser power and short acquisition times, resulting in low-photon datasets with significant Poisson noise. Traditional analysis methods struggle to fit double exponential decays at low photon counts and are computationally expensive. To address these limitations, this thesis introduces a Locally Linear Embedding Bi-Exponential Autoencoder built with 1D ConvMixer blocks to rapidly and accurately extract bi-exponential decay parameters. The convolutional neural network compresses a 256-bin temporal decay histogram into a concise three-dimensional latent space representing the fast lifetime, slow life-time, and bound fraction. To train the network, a MATLAB-based synthetic data pipeline was developed. This pipeline merged realistic cellular morphology from the Human Protein Atlas with physically accurate simulated multi-exponential decays and simulated noise. The model was subsequently evaluated against various simulated perturbations, demonstrating high resilience to flat background noise and triexponential decay signals, though it exhibited sensitivity to temporal misalignment and extreme photon starvation. Finally, the autoencoder was tested on experimental in vitro squamous cell carcinoma datasets and compared to the industry standard double exponential fitting. The model achieved excellent agreement with traditional methods for the short life-time parameter, especially at higher photon counts. For the long lifetime and bound fraction, while accurately capturing the slope of the values, there is a systematic difference in the model prediction and least squared fit. The results confirm that this deep learning architecture could provide a robust and computationally efficient alternative to traditional curve fitting. Future work would go into establishing a true ground truth comparison with traditional methods, and testing different model configurations to improve systematic differences.