Improved 3D MR Image Acquisition and Processing in Congenital Heart Disease

Congenital heart disease (CHD) is the most common type of birth defect, affecting about 1% of the population. MRI is an essential tool in the assessment of CHD, including diagnosis, intervention planning and follow-up. Three-dimensional MRI can provide particularly rich visualization and information. However, it is often complicated by long scan times, cardiorespiratory motion, injection of contrast agents, and complex and time-consuming postprocessing. This thesis comprises four pieces of work that attempt to respond to some of these challenges. The first piece of work aims to enable fast acquisition of 3D time-resolved cardiac imaging during free breathing. Rapid imaging was achieved using an efficient spiral sequence and a sparse parallel imaging reconstruction. The feasibility of this approach was demonstrated on a population of 10 patients with CHD, and areas of improvement were identified. The second piece of work is an integrated software tool designed to simplify and accelerate the development of machine learning (ML) applications in MRI research. It also exploits the strengths of recently developed ML libraries for efficient MR image reconstruction and processing. The third piece of work aims to reduce contrast dose in contrast-enhanced MR angiography (MRA). This would reduce risks and costs associated with contrast agents. A deep learning-based contrast enhancement technique was developed and shown to improve image quality in real low-dose MRA in a population of 40 children and adults with CHD. The fourth and final piece of work aims to simplify the creation of computational models for hemodynamic assessment of the great arteries. A deep learning technique for 3D segmentation of the aorta and the pulmonary arteries was developed and shown to enable accurate calculation of clinically relevant biomarkers in a population of 10 patients with CHD

Dynamic Chemical Shift Imaging for Image-Guided Thermal Therapy

Magnetic resonance temperature imaging (MRTI) is recognized as a noninvasive means to provide temperature imaging for guidance in thermal therapies. The most common method of estimating temperature changes in the body using MR is by measuring the water proton resonant frequency (PRF) shift. Calculation of the complex phase difference (CPD) is the method of choice for measuring the PRF indirectly since it facilitates temperature mapping with high spatiotemporal resolution. Chemical shift imaging (CSI) techniques can provide the PRF directly with high sensitivity to temperature changes while minimizing artifacts commonly seen in CPD techniques. However, CSI techniques are currently limited by poor spatiotemporal resolution. This research intends to develop and validate a CSI-based MRTI technique with intentional spectral undersampling which allows relaxed parameters to improve spatiotemporal resolution. An algorithm based on autoregressive moving average (ARMA) modeling is developed and validated to help overcome limitations of Fourier-based analysis allowing highly accurate and precise PRF estimates. From the determined acquisition parameters and ARMA modeling, robust maps of temperature using the k-means algorithm are generated and validated in laser treatments in ex vivo tissue. The use of non-PRF based measurements provided by the technique is also investigated to aid in the validation of thermal damage predicted by an Arrhenius rate dose model

Low-rank and sparse reconstruction in dynamic magnetic resonance imaging via proximal splitting methods

Dynamic magnetic resonance imaging (MRI) consists of collecting multiple MR images in time, resulting in a spatio-temporal signal. However, MRI intrinsically suffers from long acquisition times due to various constraints. This limits the full potential of dynamic MR imaging, such as obtaining high spatial and temporal resolutions which are crucial to observe dynamic phenomena. This dissertation addresses the problem of the reconstruction of dynamic MR images from a limited amount of samples arising from a nuclear magnetic resonance experiment. The term limited can be explained by the approach taken in this thesis to speed up scan time, which is based on violating the Nyquist criterion by skipping measurements that would be normally acquired in a standard MRI procedure. The resulting problem can be classified in the general framework of linear ill-posed inverse problems. This thesis shows how low-dimensional signal models, specifically lowrank and sparsity, can help in the reconstruction of dynamic images from partial measurements. The use of these models are justified by significant developments in signal recovery techniques from partial data that have emerged in recent years in signal processing. The major contributions of this thesis are the development and characterisation of fast and efficient computational tools using convex low-rank and sparse constraints via proximal gradient methods, the development and characterisation of a novel joint reconstruction–separation method via the low-rank plus sparse matrix decomposition technique, and the development and characterisation of low-rank based recovery methods in the context of dynamic parallel MRI. Finally, an additional contribution of this thesis is to formulate the various MR image reconstruction problems in the context of convex optimisation to develop algorithms based on proximal splitting methods

Functional neuroimaging in subjects at high genetic risk of schizophrenia

Schizophrenia is an incapacitating psychiatric disorder characterized by hallucinations and delusions with a lifetime risk of around 1% worldwide. It is a highly heritable disorder which generally becomes manifest in early adult life. The established condition has been associated with structural and functional brain abnormalities, principally in prefrontal and temporal lobes, but it is unclear whether such abnormalities are related to inherited vulnerability, medication effects, or the presence of symptoms. Furthermore, the mechanisms by which the pre-morbid state switches into florid psychosis are unknown. The Edinburgh High Risk Study is designed to address these issues. The first phase (1994-1999) employed repeated clinical, neuropsychological assessments and structural imaging. In the current phase (1999-2004) functional magnetic resonance imaging (fMRI) has been added to the tests used previously.As part of the Edinburgh High Risk Study, this study used a covert verbal initiation fMRI task (the Hayling Sentence Completion Test) known to elicit frontal and temporal activation, to examine a large number of young participants at high risk of developing schizophrenia for genetic reasons, in comparison with a matched group of healthy controls. Subjects were scanned at baseline, and after approximately one year. At the time of the baseline scan none of the participants met criteria for any psychiatric disorder, however, a number of subjects reported isolated psychotic symptoms on direct questioning. Over the course of the entire study (1994-2004), 21 individuals developed schizophrenia according to standard diagnostic criteria. Four of these subjects made the transition over the course of the current study (1999-2004), i.e. subsequent to the baseline functional scanThere were three main aims of the current study (i) to use fMRI to identify the neural correlates of state and trait effects in high risk individuals, (ii) to determine ifit is possible to distinguish those who subsequently become ill from those who remain well using functional imaging, and (iii) to determine if patterns of brain activity change with the transition to illness, or vary with changes in symptomatic status of these individuals.Regarding the first aim, group differences of apparent genetic origin were found in prefrontal, thalamic, cerebellar regions, and differences in activation in those with symptoms were found in the parietal lobe. Functional connectivity analysis examining interactions between these regions also indicated similar abnormalities. These results may therefore reflect inherited deficits, and the earliest changes associated with the psychotic state, respectively. Although only a small number of subjects became ill over the course of the current study («=4), initial findings suggested abnormalities in medial prefrontal and medial temporal regions (with an indication of parietal lobe dysfunction) were able to distinguish those who later became ill versus those that remained well. Finally, there were also indications of changes in activation patterns over time in a subgroup of subjects with varying symptomatic status.To conclude, these results are consistent with previous findings in the Edinburgh High Risk Study - what is inherited by the high risk individuals is a state of heightened vulnerability manifesting, in the case of functional imaging, as abnormalities in activation and/or connectivity in preffontal-thalamiccerebellar and prefrontal-parietal regions. These finding also suggest that there are additional differences seen in those with psychotic symptoms, and to some extent in those who subsequently go on to develop the disorder. These results are not confounded by anti-psychotic medication since all subjects were anti-psychotic naive at the time of assessment. The lack of findings traditionally associated with the established illness (dorsolateral prefrontal cortex and lateral temporal lobe) indicate these may be specifically associated with the established state, or when performance differences become manifest. Overall therefore these findings reveal information regarding the pathophysiology of the state of vulnerability to the disorder and about the mechanisms involved in the development of schizophrenia or schizophrenic symptomatology

Quantitative MRI of the human brain: Magnetisation transfer and magnetic field mapping.

The ultimate goal of this thesis was to identify ways of combining the parametric maps produced by the use of multiple quantitative Magnetic Resonance Imaging (MRI) techniques. As a first stage towards this goal, this thesis focuses on magnetisation transfer (MT) imaging, and on the use of field mapping techniques to improve the reliability of other quantitative techniques such as functional MRI (fMRI) and diffusion tensor (DT) MRI. After summarising the basic principles of MRI, the MT phenomenon is described and a quantitative MT model is reviewed. A set of experiments is then described aiming at optimising the acquisition parameters for the measurement of the MT ratio (MTR). The interaction between T and MT is investigated, confirming that MTR acquisition protocols should be designed to minimize T -weighting. Next, the quantitative model of MT is used to optimise the white-to-grey matter contrast to noise ratio of a pulse sequence for MTR measurement, at both 1.5 T and 3.0 T. The following chapter is focused on the optimisation of quantitative MT for in vivo applications. First, the sensitivity to noise of the technique is investigated using simulations. Second, the implementation of a 3D pulse sequence for quantitative MT is described. The sequence is used at 1.5 T and at 3.0 T to collect data from healthy volunteers, providing normative values. Finally, the set of sampling points used to measure MT parameters is optimised using the Cramer-Rao lower bound, showing dramatic improvements in both precision and accuracy. Next, after a review of static field inhomogeneities and field mapping, the consequences of field inhomogeneities on quantitative MT are evaluated. The use of novel acquisition sequences for field mapping is investigated, the application of field-map based correction for fMRI and DT MRI data is considered, and its effects are discussed. Finally, an attempt to combine different parameters through multivariate analysis is presented, by using principal component analysis to identify patterns of association between MT parameters. Finally, an attempt to combine different tialysis is presented, by using principal componen ssociation between MT parameters