Multivariate Analysis of MR Images in Temporal Lobe Epilepsy

Epilepsy stands aside from other neurological diseases because clinical patterns of progression are unknown: The etiology of each epilepsy case is unique and so it is the individual prognosis. Temporal lobe epilepsy (TLE) is the most frequent type of focal epilepsy and the surgical excision of the hippocampus and the surrounding tissue is an accepted treatment in refractory cases, specially when seizures become frequent increasingly affecting the performance of daily tasks and significantly decreasing the quality of life of the patient. The sensitivity of clinical imaging is poor for patients with no hippocampal involvement and invasive procedures such as the Wada test and intracranial EEG are required to detect and lateralize epileptogenic tissue. This thesis develops imaging processing techniques using quantitative relaxometry and diffusion tensor imaging with the aiming to provide a less invasive alternative when detectability is low. Chapter 2 develops the concept of individual feature maps on regions of interest. A laterality score on these maps correctly distinguished left TLE from right TLE in 12 out of 15 patients. Chapter 3 explores machine learning models to detect TLE, obtaining perfect classification for left patients, and 88.9% accuracy for right TLE patients. Chapter 4 focuses on temporal lobe asymmetry developing a voxel-based method for assessing asymmetry and verifying its applicability to individual predictions (92% accuracy) and group-wise statistical analyses. Informative ROI and voxel-based informative features are described for each experiment, demonstrating the relative importance of mean diffusivity over other MR imaging alternatives in identification and lateralization of TLE patients. Finally, the conclusion chapter discuss contributions, main limitations and outlining options for future research

Combining global and local information for the segmentation of MR images of the brain

Magnetic resonance imaging can provide high resolution volumetric images of the brain with exceptional soft tissue contrast. These factors allow the complex structure of the brain to be clearly visualised. This has lead to the development of quantitative methods to analyse neuroanatomical structures. In turn, this has promoted the use of computational methods to automate and improve these techniques. This thesis investigates methods to accurately segment MRI images of the brain. The use of global and local image information is considered, where global information includes image intensity distributions, means and variances and local information is based on the relationship between spatially neighbouring voxels. Methods are explored that aim to improve the classification and segmentation of MR images of the brain by combining these elements. Some common artefacts exist in MR brain images that can be seriously detrimental to image analysis methods. Methods to correct for these artifacts are assessed by exploring their effect, first with some well established classification methods and then with methods that combine global information with local information in the form of a Markov random field model. Another characteristic of MR images is the partial volume effect that occurs where signals from different tissues become mixed over the finite volume of a voxel. This effect is demonstrated and quantified using a simulation. Analysis methods that address these issues are tested on simulated and real MR images. They are also applied to study the structure of the temporal lobes in a group of patients with temporal lobe epilepsy. The results emphasise the benefits and limitations of applying these methods to a problem of this nature. The work in this thesis demonstrates the advantages of using global and local information together in the segmentation of MR brain images and proposes a generalised framework that allows this information to be combined in a flexible way

Quantifying structural changes in the ageing brain from magnetic resonance imaging

Understanding the ageing process is of increasing importance to an ageing society and one aspect of this is investigating what role the brain has in this process. Cognitive ability declines as we age and it is one of the most distressing aspects of getting older. Brain tissue deterioration is a significant contributor to lower cognitive ability in late life but the underlying biological mechanisms in the brain are not yet fully understood. One reason for this is the difficulty in obtaining accurate measures of potential ageing-related brain biomarkers. The chapters in this thesis explore the difficulties of quantifying brain changes in the ageing brain from Magnetic Resonance Imaging (MRI), and how the changes identified are related to cognition in later life. The data was acquired as part of the second wave of the longitudinal Lothian Birth Cohort 1936 study in which 866 people aged 73 years, returned for cognitive and medical assessment. At this stage of the study 702 underwent MR imaging resulting in 627 complete datasets across all testing. The entire data, a randomly chosen subset of 150 and 416 freely available data were used to investigate global and regional measurement methods in older brains and how the resultant measurements related to cognitive performance. Furthermore the presence of early life cognitive data in the form of a general intelligence test sat at age 11, served as an indicator of cognitive ability prior to the potential influence of the ageing process. The chapters concerning global measures at first establish, that a measure of intracranial volume (ICV) serves as both a way of correcting for individual differences in brain size between participants and as a proxy premorbid measure of brain size. The analysis, utilising freely available cross-sectional MRI data (http://www.oasis-brains.org) revealed that ICV differed very little between 18-28 year olds and 84-96 year olds where as total brain tissue volume (TBV) differed by 14.1% between the two groups, which was more than twice the standard deviation across the entire age range (18-96 years). Second a validated, reliable method for measuring ICV was investigated using 150 people randomly chosen from the LBC1936 study. Automated and semi-automated methods were validated against reference measurements the results of which showed that common ageing features make automated and semi-automated methods that do not have an additional manual editing step, ineffective at producing accurate ICV measurements. This analysis also highlighted the need to employ additional spatial overlap assessment to volumetric comparison of measurement methods to reduce the effect of false-positives and false-negatives skewing apparent discrepancies between methods. Using the information gained here ICV and TBV from the entire LBC1936 cohort were analysed in a structural equation model, alongside cognitive ability measures at both age 11 and age 73. We found that TBV was a stronger predictor of later life cognitive ability, after accounting for early life ability, but that a modest association remained between ICV and late life cognition. This suggests that early life factors pay a role in how well we age, though the relationship is complex. The regional measures chapters look at two brain regions commonly associated with ageing, the hippocampus and the frontal lobes. Measuring either of these brain regions in large samples of healthy older adults is challenging for many reasons. The hippocampus is small and as with all brain regions shows greater variation in older age, this makes employing automated methods that have the advantage of being fast and reproducible difficult. Following the results of our systematic review of automated methods for measuring the hippocampus, the two most commonly used and available automated methods were validated against reference standard measurements. The results indicated that although automated methods present an attractive alternative to laborious manual measurements they still require manual editing to produce accurate measurements in older adults. The modified strategy employed across the LBC1936 was to use an automated method and then manually edit the output; these segmentations were used to investigate the potential of multimodal image analysis in clarifying associations between the hippocampus and cognitive ability in old age. The analysis focused on associations between longitudinal relaxation time (T1), magnetization transfer ratio (MTR), fractional anisotropy (FA) and mean diffusivity (MD) in the hippocampus and general factors of fluid intelligence, cognitive processing speed and memory. The findings show that multi-modal MRI assessments were more sensitive than volumetric measurements at detecting associations with cognitive measures. The difficulty with producing a relevant frontal lobe measure was made apparent when the result of a large systematic review looking at the manual protocols used revealed 19 methods and 15 different landmarks had been employed. This resulted in an analysis that took the 5 most common boundaries reported and applied them to 10 randomly selected participants from the LBC1936. The results showed significant differences between the resultant volumes, with the smallest measurement when using the genu as the posterior marker representing only 35% of the measurement acquired using the central sulcus. The results from the studies presented in this thesis strongly highlight the need to develop age specific methods when using brain MRI to study ageing. Furthermore the implications of using unstandardised protocols, making assumptions about a methods performance based on validation in younger samples and the need to account for early life factors in this area of research have been made clearer. Studies building on these findings will be beneficial in elucidating the role of the brain in ageing

Ex vivo relaxation rates and magnetic susceptibility changes of corpus callosum in aging rats

Department of Biomedical EngineeringMyelin, the main component of white matter (WM), is a lipid-protein membrane structure that surrounds axon compactly in the nervous systems of vertebrates. Myelin exists in the form of a multi-lamellar sheath consisting of repeating units of the myelin bilayers and most of myelinated axons are distributed in WM. The main role of myelin is the electrical insulator for neurons, which increases the speed and efficiency of signal conduction. Since speed of action potential transmission is necessary to promote various neuronal functions, the measurements of myelin content are important for studies of normal development and neurodegenerative diseases. In the evaluation of myelin, magnetic resonance imaging (MRI) is widely used as a noninvasive imaging technique that provides detailed anatomical images with various contrast mechanisms. Due to the limitation in MRI resolution and the size of myelinated axons (~ 1 ??m), myelin cannot be directly resolved by MRI. Also, the T2 value of non-aqueous protons of myelin (50 ??s < T2 < 1 ms) is too short to measure the signal in conventional MRI, making direct imaging difficult. Therefore, most MRI techniques currently used for myelin imaging are mainly based on indirect estimation of myelin. Currently, various MRI techniques for indirectly examining the myelin content are being studied with their respective strengths and weaknesses. However, there is still no method that is considered gold standard in the field of myelin MRI. Studies comparing and analyzing the effects of volumetric parameters on myelin through various validation methods are still lacking. Therefore, the purpose of this study is to quantify volumetric changes in myelin, such as myelin volume fraction (MVF), in the corpus callosum (CC) of post-mortem aging rat brains through MRI-based measurements and histological/theoretical validations. In the first section, the relationship with MVF was established through the MRI-derived values: longitudinal relaxation rate R1 and the magnetic susceptibility values obtained through quantitative susceptibility mapping (QSM). The absolute MVF values were determined by transmission electron microscopy (TEM) as a gold standard measure for comparison with the values obtained by the aforementioned MRI techniques. Also, QSM simulations were performed based on the TEM-derived structures to theoretically evaluate and understand the MR signal properties. Correlations of MVF versus MRI-derived values (R1 and magnetic susceptibility) showed a strong linear relationship. In addition, QSM simulation results established a linearly proportional relationship between simulated magnetic susceptibility and MVF. Statistically significant linear correlations between MRI-derived values and MVF demonstrated that variable myelin content in WM (i.e., CC) could be quantified across different stages of aging. These results further support that both MRI techniques (R1 and QSM) provide an efficient means to study the brain aging process with accurate volumetric quantification of myelin content in the WM. In the second section, multiple spin echo sequence-based MRI-R2 values were measured to confirm that myelin volume information could be detected even when the short-T2 component (myelin water signal) was not detected due to the fixation effect. TEM-based quantification of MVF and corresponding Monte-Carlo simulation to estimate relaxation rates (R2,IE) due to diffusion in the presence of inhomogeneous magnetic field perturbation in intra- and extra-cellular (IE) spaces were respectively performed. A significant correlation between mean MRI-R2 and MVF values was observed, and the estimated R2,IE values of Monte-Carlo simulations in IE water signals were also positively correlated with MVF values. However, the magnitude of R2,IE values were much smaller than that those observed for MRI-R2 values, indicating that R2-related changes in MVF are likely dominated by the myelin water content. Such comparisons between independent parameters from MRI, TEM, and simulations support the suggestion that myelin water signals were indistinguishably mixed to exhibit mono-exponential R2, and still reflect the volumetric information of myelin. In conclusion, it was confirmed that the proposed MRI-based measurements (R1, R2 and QSM) can be usefully used for the quantification of myelin volume in the post-mortem rat CC regions based on histological/theoretical validations (TEM and simulation).clos

Preclinical MRI of the Kidney

This Open Access volume provides readers with an open access protocol collection and wide-ranging recommendations for preclinical renal MRI used in translational research. The chapters in this book are interdisciplinary in nature and bridge the gaps between physics, physiology, and medicine. They are designed to enhance training in renal MRI sciences and improve the reproducibility of renal imaging research. Chapters provide guidance for exploring, using and developing small animal renal MRI in your laboratory as a unique tool for advanced in vivo phenotyping, diagnostic imaging, and research into potential new therapies. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Cutting-edge and thorough, Preclinical MRI of the Kidney: Methods and Protocols is a valuable resource and will be of importance to anyone interested in the preclinical aspect of renal and cardiorenal diseases in the fields of physiology, nephrology, radiology, and cardiology. This publication is based upon work from COST Action PARENCHIMA, supported by European Cooperation in Science and Technology (COST). COST (www.cost.eu) is a funding agency for research and innovation networks. COST Actions help connect research initiatives across Europe and enable scientists to grow their ideas by sharing them with their peers. This boosts their research, career and innovation. PARENCHIMA (renalmri.org) is a community-driven Action in the COST program of the European Union, which unites more than 200 experts in renal MRI from 30 countries with the aim to improve the reproducibility and standardization of renal MRI biomarkers

Preclinical MRI of the kidney : methods and protocols

This Open Access volume provides readers with an open access protocol collection and wide-ranging recommendations for preclinical renal MRI used in translational research. The chapters in this book are interdisciplinary in nature and bridge the gaps between physics, physiology, and medicine. They are designed to enhance training in renal MRI sciences and improve the reproducibility of renal imaging research. Chapters provide guidance for exploring, using and developing small animal renal MRI in your laboratory as a unique tool for advanced in vivo phenotyping, diagnostic imaging, and research into potential new therapies. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Cutting-edge and thorough, Preclinical MRI of the Kidney: Methods and Protocols is a valuable resource and will be of importance to anyone interested in the preclinical aspect of renal and cardiorenal diseases in the fields of physiology, nephrology, radiology, and cardiology. This publication is based upon work from COST Action PARENCHIMA, supported by European Cooperation in Science and Technology (COST). COST (www.cost.eu) is a funding agency for research and innovation networks. COST Actions help connect research initiatives across Europe and enable scientists to grow their ideas by sharing them with their peers. This boosts their research, career and innovation. PARENCHIMA (renalmri.org) is a community-driven Action in the COST program of the European Union, which unites more than 200 experts in renal MRI from 30 countries with the aim to improve the reproducibility and standardization of renal MRI biomarkers

B1+-mapping and B1+ inhomogeneity correction at high field

Magnetic resonance images acquired at the highest strength of the main magnetic field B0 are of interest since they highly benefit from an increased signal to noise ratio. At ultra high field strengths (B0 > 7 Tesla) images with more contrast and higher resolution can thus be obtained, opening new insights into the understanding of organ structures and disease evolutions. One of the main challenges of ultra high field MR imaging is that the wavelength of MR radiations starts to be shorter than the typical organs of interest. At such wavelength, the transmit magnetic field B1+ used to manipulate the magnetization in MR imaging is subject to constructive and destructive interferences and becomes position dependent. This inhomogeneity in the B1+ field leads to signal and contrast variations in the anatomical images which are prone to misinterpretation. This thesis is about measuring and correcting the inhomogeneous B1+ field at 7 Tesla. To be able to correct the B1+ inhomogeneity, it is necessary to measure it first. An appropriate B1+-mapping sequence should provide accurate measurements in a wide range of B1+ values in a short amount of time since the acquisition of the B1+ distribution can be considered as an adjustment step. The SA2RAGE sequence was developed according to these criteria, allowing a typical three-dimensional B1+ map to be acquired in less than 2min. The next challenge was to correct the B1+ inhomogeneity observed across the brain at 7 Tesla. To obtain results of high quality, RF pulses were designed to generate the desired magnetization profile. It was already known that kT-point pulses designed in the small tip angle (STA) approximation provided substantial B1+ inhomogeneity correction. In this thesis, a methodology expressing the Bloch equations in a linear form was developed for the design of kT-point pulses beyond the STA regime. Excitation, inversion and refocusing pulses were designed and significant improvements were observed in the associated magnetization profiles when compared to the results found in the STA regime. The last part of the thesis was dedicated to the design of kT-points for a turbo spin echo (TSE) sequence in order to remove the effect of the B1+ inhomogeneity on T2-weighted imaging at 7 Tesla. In the first approach, a kT-point pulse was designed in the STA regime to make the excitation profile as homogeneous as possible. It was demonstrated that a symmetric kT-point pulse designed in the STA regime still generates an homogeneous excitation profile for flip angles as high as 120°. A unique symmetric kT-point pulse was designed in the STA regime and used to replace all the original hard pulses of a TSE sequence (static design). By adding parallel transmission, anatomical images largely devoid of artifacts resulting from the common B1+ inhomogeneity at 7 Tesla were acquired. To be able to acquire T2-weighted images with signal and contrast homogeneity by using a more efficient TSE sequence protocol, a specific kT-point waveform was optimized for each pulse of the TSE sequence (dynamic design). It was demonstrated that, although at a cost of an increase of the specific absorption rate, the dynamic outperforms the static kT-point design in terms of signal and contrast homogeneity obtained in the acquired T2-weighted images. The use of dynamic kT-points to obtain such a quality in T2-weighted imaging is thus promising for clinical applications at ultra high field

Diffusion MRI analysis:robust and efficient microstructure modeling

Diffusion MRI (dMRI) allows for investigating the structure of the human brain. This is useful for both scientific brain research as well as medical diagnosis. Since the raw dMRI data is not directly interpretable by humans, we use mathematical models to convert the raw dMRI data into something interpretable. These models can be computed using multiple different computational methods, each having a different trade-off in accuracy, robustness and efficiency. In this thesis we studied multiple different computational models for their usability and efficiency for dMRI modeling. In the end we provide the reader with methodological recommendations for dMRI modeling and provide a high performance GPU enabled dMRI computing platform containing all recommendations