Using GPU Acceleration and a Novel Artificial Neural Networks Approach for Ultra-Fast Fluorescence Lifetime Imaging Microscopy Analysis
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Using GPU Acceleration and a Novel Artificial Neural Networks Approach for Ultra-fast Fluorescence Lifetime Imaging Microscopy Analysis Gang Wu A Thesis Submitted for the Degree of Doctor of Philosophy School of Engineering and Informatics University of Sussex November 2017 To my beloved parents and wife. Declaration I hereby declare that except where specific reference is made to the work of others, the contents of this dissertation are original and have not been submitted in whole or in part for consideration for any other degree or qualification in this, or any other University. This dissertation is the result of my own work and includes nothing which is the outcome of work done in collaboration, except where specifically indicated in the text. Signature: Gang Wu November 2017 Acknowledgements PhD study has definitely been an unforgettable and precious journey of my life. It was the first time I ever left my country, and went to a new country with a very different culture. It was a privilege to witness two different cultures. This journey has not only strengthen my research ability, but also influenced my lifestyle, my value and my philosophy. I would like to thank my supervisors, Dr. David Day-Uei Li and Prof. Thomas Nowotny, for all their invaluable help and support throughout my PhD study. Without their help, I would not be able to get this far. I can always remember how they helped me with my research and papers. They encouraged me to think creatively and work on my own proposal. I would also like to thank all my friends and colleagues in the UK. Especially, Bill Parslow, Janice Britz, and the whole family, which is like my second family. I thank my parents from the bottom of my heart for their life long support. Also, a special thanks to my wife, who has given me so much happiness. The long geographic distance never separates us, and it has been proved that she is the girl I want to marry with. Last but not least, I would like to thank the University of Sussex, the China Scholarship Council, and the NVIDIA corporation for their support. Summary Fluorescence lifetime imaging microscopy (FLIM) which is capable of visualizing local molecular and physiological parameters in living cells, plays a significant role in biological sciences, chemistry, and medical research. In order to unveil dynamic cellular processes, it is necessary to develop high-speed FLIM technology. Thanks to the development of highly parallel time-to-digital convertor (TDC) arrays, especially when integrated with single-photon avalanche diodes (SPADs), the acquisition rate of high- resolution fluorescence lifetime imaging has been dramatically improved. On the other hand, these technological advances and advanced data acquisition systems have generated massive data, which significantly increases the difficulty of FLIM analysis. Traditional FLIM systems rely on time-consuming iterative algorithms to retrieve the FLIM parameters. Therefore, lifetime analysis has become a bottleneck for high-speed FLIM applications, let alone real-time or video-rate FLIM systems. Although some simple algorithms have been proposed, most of them are only able to resolve a simple FLIM decay model. On the other hand, existing FLIM systems based on CPU processing do not make use of available parallel acceleration. In order to tackle the existing problems, my study focused on introducing the state-of-art general purpose graphics processing units (GPUs) to the FLIM analysis, and building a data processing system based on both CPU and GPUs. With a large amount of parallel cores, the GPUs are able to significantly speed up lifetime analysis compared to CPU- only processing. In addition to transform the existing algorithms into GPU computing, I have developed a new high-speed and GPU friendly algorithm based on an artificial neural network (ANN). The proposed GPU-ANN-FLIM method has dramatically improved the efficiency of FLIM analysis, which is at least 1000-folder faster than some traditional algorithms, meaning that it has great potential to fuel current revolutions in high-speed high-resolution FLIM applications. Contents Contents ........................................................................................................................... vi List of Figures .................................................................................................................. ix List of Tables.................................................................................................................. xvi Nomenclature ................................................................................................................ xvii Publications and presentations ........................................................................................ xx Chapter 1 Introduction ............................................................................................... 21 1.1 Overview .......................................................................................................... 21 1.2 Background and objectives............................................................................... 21 1.3 Contribution ...................................................................................................... 24 1.4 Structure of the thesis ....................................................................................... 25 Chapter 2 Fluorescence lifetime imaging microscopy and applications ................... 27 2.1 Overview .......................................................................................................... 27 2.2 Principle of FLIM ............................................................................................. 27 2.2.1 Fluorescence .............................................................................................. 27 2.2.2 Fluorescence lifetime and quantum yields ................................................ 29 2.2.3 Fluorescence lifetime imaging .................................................................. 31 2.3 Time-domain FLIM by TCSPC ....................................................................... 33 2.3.1 TCSPC ...................................................................................................... 33 2.3.2 Instrument response function .................................................................... 35 2.3.3 Standard algorithms .................................................................................. 36 2.4 FLIM applications ............................................................................................ 46 2.5 Summary .......................................................................................................... 48 Chapter 3 Graphics processing units and FLIM analysis .......................................... 49 3.1 Overview .......................................................................................................... 49 3.2 Parallel Computing ........................................................................................... 49 3.2.1 Brief history of Parallel Computing .......................................................... 50 3.3 Graphics processing units ................................................................................. 51 3.3.1 GPUs as programmable parallel processors .............................................. 51 3.3.2 GPU architecture ....................................................................................... 55 Contents vii 3.4 CUDA overview ............................................................................................... 57 3.4.1 CUDA architecture.................................................................................... 58 3.4.2 Programming model .................................................................................. 59 3.5 GPU applications .............................................................................................. 68 3.6 GPU acceleration of standard FLIM algorithms .............................................. 68 3.6.1 Thread-based computing for non-iterative algorithms .............................. 69 3.6.2 Block-based computing for iterative algorithms ....................................... 70 3.7 Optimization of GPU Programming ................................................................. 73 3.7.1 Kernel Configuration ................................................................................ 73 3.7.2 Memory Optimization ............................................................................... 75 3.7.3 Programming Strategies ............................................................................ 78 3.8 Evaluation of standard algorithms .................................................................... 80 3.9 Summary .......................................................................................................... 86 Chapter 4 Artificial neural network and FLIM analysis ............................................ 87 4.1 Overview .......................................................................................................... 87 4.2 Artificial neural networks ................................................................................. 88 4.2.1 A biological neuron ................................................................................... 89 4.2.2 Mathematical model of an artificial neuron .............................................. 90 4.2.3 Feedforward neural network ..................................................................... 92 4.2.4 ANN