Evolutionary Computing Techniques to aid the Acquisition and Analysis of Nuclear Magnetic Resonance Data

Evolutionary computation, including genetic algorithms and genetic programming have taken the ideas of evolution in biology and applied some of the characteristics to problem solving. The survival of the fittest paradigm allows a population of candidate solutions to be modified by sexual and asexual reproduction and mutation to come closer to solving the problem in question without the necessity of having prior knowledge of what a good solution looks like. . The increasing importance of Nuclear Magnetic Resonance Spectroscopy in medicine has created a demand for automated data analysis for tissue classification and feature selection. The use of artificial intelligence techniques such as evolutionary computing can be used for such data analysis. This thesis applies the techniques of evolutionary computation to aid the collection and classification of Nuclear Magnetic Resonance spectroscopy data. The first section (chapters one and two) introduces Nuclear Magnetic Resonance spectroscopy and evolutionary computation and also contains a review of relevant literature. The second section focuses on classification. In the third chapter classification into two classes of brain tumours is undertaken. The fourth chapter expands this to classify tumours and tissues into more than two classes. Genetic Programming provided good solutions with relatively simple biochemical interpretation and was able to classify data into more than two classes at one time. The third section of the thesis concentrates on using evolutionary computation techniques to optimise data acquisition parameters directly from the Nuclear Magnetic Resonance hardware. Chapter five shows that Genetic Algorithms in particular are successful at suppressing signals from solvent while chapter six applies these techniques to find a way of enhancing the signals from metabolites important to the classification of brain tumours as found in chapter three. The final chapter draws conclusions as to the efficacy of evolutionary computation techniques applied to Nuclear Magnetic Resonance Spectroscopy. List of Abbreviations ADF Automatically Defined Function Al Artificial Intelligence Ala Alanine CH2 Creatine CH3 Creatine CT Computerised Tomography DMSO Dimethylsulphoxide EC Evolutionary Computation EP Evolutionary Programming ES Evolutionary Strategy FID Free Induction Delay FPGA Field Programmable Gate Arrays GA Genetic Algorithm GAOT Genetic Algorithm Optimisation Toolbox Gln Glutamate Glu Glutamine GP Genetic Programming 'H Proton, hydrogen atom with zero neutrons kNN k Nearest Neighbour where k is a number LDA Linear Discriminant Analysis MHz Megahertz MR Magnetic Resonance MRI Magnetic Resonance Imaging MRS Magnetic Resonance Spectroscopy MRSI Magnetic Resonance Spectroscopic Imaging NAA N-acetyl-aspartate NLM Non-Linear Mapping NMR Nuclear Magnetic Resonance NMV Net Magnetic Vector NN Neural Network PC Principal Component