Search bioRxiv⌕ Search

Biology subjects

Erbas, I.

Publications and source records attributed to Erbas, I..

2 recordsLinked to original sources

Real-Time Wide-Field Fluorescence Lifetime Imaging via Single-Snapshot Acquisition for Biomedical Applications

Fluorescence lifetime imaging (FLI) is a powerful tool for investigating molecular processes, microenvironmental parameters, and molecular interactions across tissue to (sub-)cellular levels. Despite its established value in numerous biomedical applications, conventional FLI techniques are hindered by long acquisition times. This limitation restricts their use in real-time scenarios, such as monitoring fast biological processes, studying live organisms, and in environments that require rapid imaging and immediate inference, such as clinical image-guided interventions. Here, we present a novel FLI approach that combines a large-format time-gated SPAD array with dual-gate acquisition capability, alongside a rapid lifetime determination algorithm. This integration allows for real-time fluorescence lifetime estimation through single-snapshot acquisitions, eliminating the need for traditional, time-consuming time-resolved data collection. We demonstrate the scalability and versatility of this method by achieving real-time FLI across challenging biomedical applications, ranging from capturing fast neural dynamics at the microscopic scale, performing multimodal 3D volumetric FLI of tumor organoids at the mesoscopic scale, to macroscale FLI in both direct and highly scattering regimes. Furthermore, we validate its utility in fluorescence lifetime-guided surgical procedures using tissue-mimicking phantoms. Overall, this new methodology significantly enhances the temporal and spatial capabilities of FLI, opening the door to the assessment of fast dynamic biomedical signals. It also enables the seamless integration of FLI into clinical workflows, particularly in applications like fluorescence-guided surgery.

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↗