Computational Methods for Low-Cost Fluorescence Lifetime Imaging Microscopy (FLIM)

This project aims to develop a new computational algorithm to enable fast imaging with low-cost single-photon sensors for fluorescence Lifetime Imaging microscopy (FLIM) using statistical and/or learning based approaches.
Recent technological innovations, eg detection and acquisition hardware, have pushed sparse-photon imaging to the fore in a variety of applications including FLIM microscopy for bioimaging and 3D Lidar imaging for autonomous vehicles or consumer electronics. Despite recent advances, current systems can still be optimized to better exploit the noisy or incomplete measurements especially when acquiring a small number of pixels using cheap sensors (i.e., when only capturing 8x8 pixels to build an image).
In this project, we will optimize the processing of FLIM single-photon data to enable high-resolution imaging from sub-sampled acquired data (data acquired with a low resolution cost-effective sensor [1]). A focus will be on the combination of statistical Bayesian models [2-3], optimization algorithms and state-of-the-art deep learning methods to solve these challenging inverse problems. In particular, the candidate will investigate one or a combination of the following aspects: the design of efficient networks (eg using unrolling [3], or plug-and-play approaches), and using new representations (eg using neural fields approaches or generative modelling with VAE, normalizing flows, etc). Initial study will be conducted on simulated data to characterize performance limitations, followed by tests on real FLIM data acquired with a new system.
The candidate will closely collaborate with world-leading computational imaging groups in Heriot-Watt University (Quantum Optics and Computational Imaging), Edinburgh University and the wider community via the EPSRC Quantum Technology Hub in Quantum Imaging (Quantic). There is a possibility to pursue research in a PhD program with available funding for successful candidates.

Please contact supervisors for more details regarding the project.
More information regarding the groups can be accessed in:
https://sites.google.com/site/abderrahimhalimi/home
https://quantum-optics.site.hw.ac.uk/

Software Needs and Skills:
Statistical signal and Image processing, Bayesian methods, deep learning, optimization.

Python, Matlab.

[1] A. Ruget, M. Tyler, G. Mora-Martín, S. Scholes, F. Zhu, I. Gyongy, B. Hearn, S. McLaughlin, A. Halimi, J. Leach, "Pixels2Pose: Super-Resolution Time-of-Flight Imaging for 3D Pose Estimation", Science Advances, 2022
[2] A. Halimi, A. Maccarone, R. Lamb, G. Buller, S. McLaughlin, "Robust and Guided Bayesian Reconstruction of Single-Photon 3D Lidar Data: Application to Multispectral and Underwater Imaging," IEEE-TCI, 2021.
[3] J. Koo, A. Halimi, S. McLaughlin, "A Bayesian Based Deep Unrolling Algorithm for Single-Photon Lidar Systems", IEEE-JSTSP, 2022.