Thesis
Fast fluorescence lifetime imaging and sensing via deep learning
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- Rights statement
- Awarding institution
- University of Strathclyde
- Date of award
- 2023
- Thesis identifier
- T16653
- Person Identifier (Local)
- 201877447
- Qualification Level
- Qualification Name
- Department, School or Faculty
- Abstract
- Fluorescence lifetime imaging microscopy (FLIM) has become a valuable tool in diverse disciplines. This thesis presents deep learning (DL) approaches to addressing two major challenges in FLIM: slow and complex data analysis and the high photon budget for precisely quantifying the fluorescence lifetimes. DL's ability to extract high-dimensional features from data has revolutionized optical and biomedical imaging analysis. This thesis contributes several novel DL FLIM algorithms that significantly expand FLIM's scope.Firstly, a hardware-friendly pixel-wise DL algorithm is proposed for fast FLIM data analysis. The algorithm has a simple architecture yet can effectively resolve multi-exponential decay models. The calculation speed and accuracy outperform conventional methods significantly.Secondly, a DL algorithm is proposed to improve FLIM image spatial resolution, obtaining high-resolution (HR) fluorescence lifetime images from low-resolution (LR) images. A computational framework is developed to generate large-scale semi-synthetic FLIM datasets to address the challenge of the lack of sufficient high-quality FLIM datasets. This algorithm offers a practical approach to obtaining HR FLIM images quickly for FLIM systems.Thirdly, a DL algorithm is developed to analyze FLIM images with only a few photons per pixel, named Few-Photon Fluorescence Lifetime Imaging (FPFLI) algorithm. FPFLI uses spatial correlation and intensity information to robustly estimate the fluorescence lifetime images, pushing this photon budget to a record-low level of only a few photons per pixel.Finally, a time-resolved flow cytometry (TRFC) system is developed by integrating an advanced CMOS single-photon avalanche diode (SPAD) array and a DL processor. The SPAD array, using a parallel light detection scheme, shows an excellent photon-counting throughput. A quantized convolutional neural network (QCNN) algorithm is designed and implemented on a field-programmable gate array as an embedded processor. The processor resolves fluorescence lifetimes against disturbing noise, showing unparalleled high accuracy, fast analysis speed, and low power consumption.
- Advisor / supervisor
- Li, David Day-Uei
- Chen, Yu
- Resource Type
- DOI
- Date Created
- 2022
- Funder
Beziehungen
Objekte
Miniaturansicht | Titel | Hochladedatum | Sichtbarkeit | Aktionen |
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PDF of thesis T16653 | 2023-07-19 | Öffentlich | Herunterladen |