Deep Compressive Macroscopic Fluorescence Lifetime Imaging

TL;DR

This paper introduces Net-FLICS, a deep learning CNN that rapidly and accurately reconstructs fluorescence lifetime images from compressive sensing data, significantly reducing processing time for potential real-time in vivo applications.

Contribution

The paper presents a novel CNN model, Net-FLICS, that improves the speed and quality of fluorescence lifetime image reconstruction from compressive sensing data.

Findings

Net-FLICS outperforms traditional methods in image quality.

Reconstruction time is nearly negligible with Net-FLICS.

Applicable to both simulated and experimental datasets.

Abstract

Compressive Macroscopic Fluorescence Lifetime Imaging (MFLI) is a novel technical implementation that enables monitoring multiple molecular interactions in macroscopic scale. Especially, we reported recently on the development of a hyperspectral wide-field time-resolved single-pixel imaging platform that facilitates whole-body in vivo lifetime imaging in less than 14 minutes. However, despite efficient data acquisition, the data processing of a Compressed Sensing (CS) based inversion plus lifetime fitting remain very time consuming. Herein, we propose to investigate the potential of deep learning for fast and accurate image formation. More precisely we developed a Convolutional Neural Network (CNN) called Net-FLICS (Network for Fluorescence Lifetime Imaging with Compressive Sensing) that reconstructs both intensity and lifetime images directly from raw CS measurements. Results show that better quality reconstruction can be obtained using Net-FLICS, for both simulation and experimental dataset, with almost negligible time compared to the traditional analytic methods. This first investigation suggests that Net-FLICS may be a powerful tool to enable CS-based lifetime imaging for real-time applications.