This thesis describes work carried out to improve the operation of a 0.01 T open access low field MRI system. Two planar surface gradient coil sets and a new three channel gradient amplifier were designed, constructed and tested. Surface transmit coils and surface receive coils and a balanced preamplifier were developed to allow RF pulse generation and signal detection without compromising the single-sided access of the system. The properties of litz wire were investigated by simulation and testing, and it was shown and the established litz wire theory can also be applied to the design of cooled coils. Images were obtained to show the operation of the new gradient amplifier and to demonstrate the combined operation of a surface gradient coil, surface receive coil, surface transmit coil and the low noise preamplifier. A large surface receive coil was used to acquire in vivo images for the first time of surface regions of a volunteer's head. Images obtained with a cryogenic receive coil, showed an SNR improvement relative to a room temperature coil in agreement with theoretical predictions.

This thesis introduces several novel methods for the data-driven and ex- ploratory analysis of functional brain images. Functional magnetic resonance imaging (fMRI) has emerged as a safe and non-invasive way to image the hu- man brain in action. In pharmacological MRI (phMRI), a drug's effect on the brain is of interest, rather than the brain's response to a specific task as in fMRI. However, the sometimes prolonged response to a drug necessi- tates different methodologies than those for task related effects, with further methods development needed to deliver robust results so that phMRI may be of practical use during drug development. There are many confounding issues in analysing these data, including under-informed models of response, subject motion, scanner drift, and gross differences in brain volume. In this work, data from a phMRI experiment was analysed to examine the effect of a pharmacological dose of hydrocortisone; a glucocorticoid associ- ated with the body's response to stress, and used in a number of medical conditions. The key findings were that even without using a priori hypothe- ses about the site of action, hydrocortisone significantly reduces a phMRI signal associated with blood oxygenation in the dorsal hippocampi, which is confirmed by decreases in absolute perfusion measured using arterial spin labelling. Methods were developed for the detection and correction of artefacts, includ- ing intra-scan motion and scanner drift. Functional connectivity methods were examined, and methodological issues in comparing groups investigated, revealing that many previously observed differences may have been biased or even artefactual due to gross differences in brain volume. Temporal decom- position techniques were also explored for their use in brain imaging, with wavelet cluster analysis being developed into an interactive and iterative method, while an adaptive analysis method, empirical mode decomposition, is built upon to allow the analysis of many thousands of time courses.

This thesis describes research work undertaken in the field of image mining. More specifically, the research work is directed at image classification according to the nature of a particular Region Of Interest (ROI) that appears across a given image set. Four approaches are described in the context of the classification of medical images. The first is founded on the extraction of a ROI signature using the Hough transform, but using a polygonal approximation of the ROI boundary. The second approach is founded on a weighted subgraph mining technique whereby the ROI is represented using a quad-tree structure which allows the application of a weighted subgraph mining technique to identify feature vectors representing these ROIs; these can then be used as the foundation with which to build a classifier. The third uses an efficient mechanism for determining Zernike moments as a feature extractor, which are then translated into feature vectors to which a classification process can be applied. The fourth is founded on a time series analysis technique whereby the ROI is represented as a pseudo time series which can then be used as the foundation for a Case Based Reasoner. The presented evaluation is directed at MRI brain scan data where the classification is focused on the corpus callosum, a distinctive ROI in such data. For evaluation purposes three scenarios are considered: distinguishing between musicians and non-musicians, left handedness and right handedness, and epilepsy patient screening.

In this dissertation novel methods are proposed for analyzing EEG and fMRI. These include an advanced technique developed to integrate these, modalities, Majority of the proposed methods in this thesis are based on blind source separation (BSS) concept. Artifact removal is an essential task to prepare EEG signal recorded simultaneously with fMRI for further processing. This is tackled using two new approaches. In the first method, a hybrid independent component analysis (ICA) plus discrete Hermite transform (DHT) is developed. The second method employs a new cost function to perform source extraction based on joint short- and-long term prediction. The main objective of this work is to incorporate the prior information about the temporal structure and periodicity of ballistocardiogram (BCG) artifact into separation procedure. The main objective in fMRI analysis is detection of blood oxygenation level dependent (BOLD). General linear model (GLM) is a widely used technique for this purpose relying only on stimuli onset times and predefined haemodynamic response function (HRF). In this thesis, several BSS methods mainly based on non-negative matrix factorization (NMF) are developed for fMRI anal- ysis. The main superiority of these techniques over GLM is their model-free nature. However, we demonstrate that the performance of these techniques can be improved by exploiting some statistical and physiological prior information. This leads to an advanced approach that does not entirely rely on a predefined model (in contrast to GLM) while taking advantages of all existing information. An important step in our approach for EEG-fMRI fusion is through estimating the fMRI time course using the EEG signals. One approach is by detection of movement onset from brain event- related oscillations. Extracting these oscillations is challenging due to their non-phase-locked an- ture and inter-trial variability. In this research, a novel method based on linear prediction is proposed to extract rolandic beta rhythm from multi-channel EEG recording. This technique em- ploys a spatio-temporal constraint to effectively extract beta rhythms to study post-movement beta rebound. The results are used to construct a regressor for fMRI analysis in a combined EEG-fMRI paradigm. The last contribution in this thesis is development of a novel technique for EEG-fMRI fusion. This method combines the reconstructed time course using the extracted beta rhythm and fMRI using a partially constrained algorithm. PARAFAC2 is used for this purpose. The obtained results identify the voxels which are involved in post-movement beta rebound due to performing a motor task.

The aim of the work in this thesis is the development of functional magnetic resonance imaging (fMRI) techniques to investigate human brain plasticity and learning in the sensorimotor system. Patterns of cortical and sub-cortical activation in response to active, passive and Electrical Stimulation (ES)-induced Ankle dorsiflexion (ADP) movement were identified using fMRI. Electromyography (EMG)-guided fMRI analysis was shown to improve detection and reduce inter-session variance for active and ES tasks. A significantly greater number of voxels were found to be activated during active and ES-induced ADP compared to passive ADP. The contrast of active greater than ES-induced ADP showed increased activation in brain areas responsible for motor planning, execution and visuomotor co- ordination, conversely ES-induced activation was shown to result in greater activation in bilateral SII and insula than active ADP. Cortical responses to tactile stimulation at flutter (33 Hz) and vibration (250 Hz) frequencies were assessed using electrical (EEG) and haemodynamic (fMRI) measures. Each modality was first assessed individually, and the two modalities then combined using simultaneous EEG/fMRI. The evoked responses in the EEG data showed flutter and vibration evoked an overall similar bilateral activation in SI, similar between unilateral right and left hand stimulation. No significant laterality of the evoked response in SI due to 33 Hz tactile stimulation was found. Analysis of the fMRI data showed that unilateral tactile stimulation, at both 33 and 250 Hz, elicited bilateral activation in SI and SI!, with flutter eliciting a greater BOLD signal in contralateral SI. The SI mean laterality index was shown to be significantly higher for fMRI data than EEG data, indicative of contralateral activity. Simultaneous EEG/fMRI at 7 T resulted in very noisy EEG data, even when precautions were taken and post-processing artefact removal methods used. The effect of short term tactile training on a tactile discrimination task was demonstrated. Behavioural data showed alterations in responses with training. fMRI results showed that passive tactile letter discrimination activated multiple cortical areas, including the somatosensory network, fronto-parietal network, fusiform gyrus and cerebellum. Comparison between trained and untrained groups reveals a significant reduction in response in SI! and fronto-parietal areas to the tactile discrimination task suggestive of a decrease in the monitoring effort required to maintain performance with a shift to more automated performance,.

The work in this thesis focuses on the development and implementation of practical approaches to measure and characterise flow data using phase contrast magnetic resonance imaging (PC-MRI), and to use some of the body's natural physiological processes to modulate blood flow with the aim of producing additional probes to study pathologies. The use of low spatial resolution PC-MRI is investigated to allow high sampling rates and near-simultaneous measurement at multiple sites to be performed. The precision of this was validated and used to demonstrate changes in flow provoked by the use of glucose as a probe to alter abdominal blood flow. Waveform analysis to interrogate the phasic temporal flow waveform (PTFW) was also used to further characterise impact of the glucose on normal physiology. Through further experimentation, optimised sequences for the quantification of flow in the much more challenging environment of the right coronary artery (RCA) were implemented. New mathematical models were developed to perform waveform analysis on the RCA unique waveform shape flow data. A Gaussian model proved the most robust model successfully able to model the RCA PTFW with 84% flux and 93% velocity data modelled with over all total residual R2 = 0.68 ± 0.15 flux and R2 = 0.65 ± 0.14 velocity PC-MRI data respectively providing a good fit in all areas except the incisure (the recover period of the wave form after systole and before diastole) and the best overall fit across the entire PTFW. The physiological impact of blood flow during respiratory suspension at defined pressures, as a proxy for inter thoracic pressure, was explored. PC-MR blood flow, and PTFW measurements in the thoracic descending aorta and RCA were assessed for modulation.

No description available.

The results presented in this thesis are concerned with improvements to liquid-state Nuclear Magnetic Resonance (NMR) spectroscopic techniques and their application to small molecule (i.e. with molecular weight < 500) mixture systems, with the aim of separating signals from individual components. The results presented in this thesis provide an insight into NMR mixture analysis, which may ultimately have applications in the field of drug discovery and development.

No description available.

Abnormal angiogenesis is characterised by the alteration of physiological variables such as capillary permeability and fractional volume of the extravascular extracellular space. The aim of the work presented in this thesis was to investigate the feasibility of non-invasive measurement of these physiological variables through quantitative analysis of dynamic Gd-DTPA enhanced MRI (DEMRI) acquired using standard imaging hardware within a clinical setting. A method for quantitative analysis of DEMRI (QDEMRI) was developed and implemented on a standard personal computer platform using a set of programs written in the C programming language. The method includes pharmacokinetic modelling of Gd-DTPA kinetics based on the modification of existing approaches and moving-window algorithms for the measurement of black-box quantifiers of DEMRI. The measurements were performed in two angiogenesis dependent diseases: breast cancer and rheumatoid arthritis (RA). In a study involving QDEMRI analysis of 59 primary invasive breast carcinomas, a significant relationship between capillary permeability-related QDEMRI variables and tumour grade was found. In a randomised controlled study of early RA in metacarpophalangeal joints, a significant reduction in the QDEMRI variable which reflects fractional volume of the extravascular extracellular space was found three months after the start of therapy in 20 patients treated with methotrexate and intra-articular methylprednisolone injections, whereas it remained constant in a control group of 17 patients who were treated with slow-acting methotrexate only. A significant reduction in the permeability-related QDEMRI variable was detected in 17 patients treated with leflunomide four months after the start of treatment whilst it remained unchanged in 17 patients treated with methotrexate in a randomised controlled study of established RA of the knee joint. The results obtained in this work indicate that the proposed QDEMRI method can be used in its present form to monitor treatment-induced changes in angiogenesis dependent diseases. Further work is needed to render these measurements fully independent of DEMRI acquisition settings and allow the evaluation of individual lesions.