Dissertations / Theses: 'MRI methods'

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Mougin, Olivier. "Quantitative methods in high field MRI." Thesis, University of Nottingham, 2010. http://eprints.nottingham.ac.uk/11608/.

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The increased signal-to-noise ratio available at high magnetic ﬁeld makes possible the acquisition of clinically useful MR images either at higher resolution or for quantitative methods. The work in this thesis is focused on the development of quantitative imaging methods used to overcome difﬁculties due to high ﬁeld MRI systems (> 3T). The protocols developed and presented here have been tested on various studies aiming at discriminating tissues based on their NMR properties. The quantities of interest in this thesis are the longitudinal relaxation time T1, as well as the magnetization transfer process, particularly the chemical exchange phenomenon involving amide protons which is highlighted particularly well at 7T under speciﬁc conditions. Both quantities (T1 and amide proton transfer) are related to the underlying structure of the tissues in-vivo, especially inside the white matter of the brain. While a standard weighted image at high resolution can provide indices of the extent of the pathology, a robust measure of the NMR properties of brain tissues can detect earlier abnormalities. A method based on a 3D Turbo FLASH readout and measuring reliably the T1 in-vivo for clinical studies at 7T is ﬁrst presented. The other major part of this thesis presents magnetization transfer and chemical exchange phenomena. First a quantitative method is investigated at 7T, leading to a new model for exchange as well as contrast optimization possibility for imaging. Results using those methods are presented and applied in clinical setting, the main focus being to image reliably the brain of both healthy subjects and Multiple Sclerosis patients to look at myelin structures.

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Morra, Jonathan Harold. "Learning methods for brain MRI segmentation." Diss., Restricted to subscribing institutions, 2009. http://proquest.umi.com/pqdweb?did=1905693471&sid=1&Fmt=2&clientId=1564&RQT=309&VName=PQD.

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Groves, Adrian R. "Bayesian learning methods for modelling functional MRI." Thesis, University of Oxford, 2009. http://ora.ox.ac.uk/objects/uuid:fe46e696-a1a6-4a9d-9dfe-861b05b1ed33.

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Bayesian learning methods are the basis of many powerful analysis techniques in neuroimaging, permitting probabilistic inference on hierarchical, generative models of data. This thesis primarily develops Bayesian analysis techniques for magnetic resonance imaging (MRI), which is a noninvasive neuroimaging tool for probing function, perfusion, and structure in the human brain. The first part of this work fits nonlinear biophysical models to multimodal functional MRI data within a variational Bayes framework. Simultaneously-acquired multimodal data contains mixtures of different signals and therefore may have common noise sources, and a method for automatically modelling this correlation is developed. A Gaussian process prior is also used to allow spatial regularization while simultaneously applying informative priors on model parameters, restricting biophysically-interpretable parameters to reasonable values. The second part introduces a novel data fusion framework for multivariate data analysis which finds a joint decomposition of data across several modalities using a shared loading matrix. Each modality has its own generative model, including separate spatial maps, noise models and sparsity priors. This flexible approach can perform supervised learning by using target variables as a modality. By inferring the data decomposition and multivariate decoding simultaneously, the decoding targets indirectly influence the component shapes and help to preserve useful components. The same framework is used for unsupervised learning by placing independent component analysis (ICA) priors on the spatial maps. Linked ICA is a novel approach developed to jointly decompose multimodal data, and is applied to combined structural and diffusion images across groups of subjects. This allows some of the benefits of tensor ICA and spatially-concatenated ICA to be combined, and allows model comparison between different configurations. This joint decomposition framework is particularly flexible because of its separate generative models for each modality and could potentially improve modelling of functional MRI, magnetoencephalography, and other functional neuroimaging modalities.

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Ivarsson, Magnus. "Evaluation of 3D MRI Image Registration Methods." Thesis, Linköpings universitet, Datorseende, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-139075.

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Image registration is the process of geometrically deforming a template image into a reference image. This technique is important and widely used within thefield of medical IT. The purpose could be to detect image variations, pathologicaldevelopment or in the company AMRA’s case, to quantify fat tissue in variousparts of the human body.From an MRI (Magnetic Resonance Imaging) scan, a water and fat tissue image isobtained. Currently, AMRA is using the Morphon algorithm to register and segment the water image in order to quantify fat and muscle tissue. During the firstpart of this master thesis, two alternative registration methods were evaluated.The first algorithm was Free Form Deformation which is a non-linear parametricbased method. The second algorithm was a non-parametric optical flow basedmethod known as the Demon algorithm. During the second part of the thesis,the Demon algorithm was used to evaluate the effect of using the fat images forregistrations.

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Malik, Shaihan. "Data Driven Reconstruction Methods for Dynamic Undersampled MRI." Thesis, Imperial College London, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.486758.

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Dynamic undersampling of MRI data can be used in order to accelerate image acquisition by exploiting the inherent information redundancy existing in sequences of dynamic images. Regions within the field of view (FOV) are forced to share temporal bandwidth, this leads to more efficient encoding so long as regions requiring a large bandwidth are not forced to share. It is noted that existing image reconstruction techniques (for example k-t SENSE) can cause temporal blurring whilst attempting to filter noise from reconstructed images. A new reconstruction technique named x-f choice is proposed, with the aim of reducing this effect. Image reconstruction techniques for dynamic undersampled data in general require some estimate of the expected temporal variation. Existing methods use low resolution images as a pragmatic solution, it is shown that errors can result from this. In this project methods for extracting this information from undersampled data have been investigated. The focus has been on identifying temporally correlated signals within the undersampled data, so that information lost by undersampling may be estimated from elsewhere without the need for extra data. X-f choice in conjunction with analysis of temporal correlations has been used to successfully reconstructed DCE-MRA data acquired in vivo without the need for any extra information at reduction factors of up to 9. It is shown that temporal correlations may be used in order to improve image reconstruction quality in a variety of cases including cardiac imaging, using both x-f choice and the existing reconstruction technique k-t SENSE.

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Niazy, Rami. "Simultaneous electroencephalography and functional MRI : methods and applications." Thesis, University of Oxford, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.483692.

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Rababa`h, Qasim. "Perfusion MRI of gliomas - comparison of analysis methods." Thesis, Örebro universitet, Institutionen för hälsovetenskap och medicin, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-37302.

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Pietsch, Maximilian Rainer. "Advanced diffusion MRI analysis methods for neonatal imaging." Thesis, King's College London (University of London), 2018. https://kclpure.kcl.ac.uk/portal/en/theses/advanced-diffusion-mri-analysis-methods-for-neonatal-imaging(3d1a8dc2-070c-4651-9a42-5171d6ebbab1).html.

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Developmental processes taking place during the third trimester and the neonatal period lay the foundation for a functioning human brain. In the course of these months, neuronal migration, cellular organisation, cortical development and myelination shape the form and function of our arguably most complex and outstanding organ. Diffusion weighted MRI (dMRI) has been extensively used to study the rapid changes in microstructural properties of white and grey matter non-invasively and provides contrast that is complementary to other imaging modalities [Yoshida et al., 2013]. The sensitivity to processes on the cellular level has made diffusion imaging a tool for studying white matter development and the early detection of injury [Hüppi, Dubois, 2006]. Linking the measured signal to changes in the cellular composition and organisation of brain tissue poses data processing challenges unique to the pediatric population. In particular, movement during the acquisition corrupts diffusion images beyond repair and requires manual data cleaning. We developed a neural network classifier that can perform this task automatically, allowing large-scale automated processing and analysis of diffusion data. Also, inferring cellular tissue properties from the signal is difficult as the brain simul-taneously undergoes a number processes that could alter the contrast in various ways. In simulations, I investigate the validity of often implicitly assumed relations between quantities derived from Diffusion Tensor Imaging (DTI) and myelination in the context of changing tissue compartment volume fractions, showing that the interpretation of DTI parameters is flawed in the absence of a-priori knowledge about tissue microstructure. In recent years, progress in acquisition and reconstruction techniques have facilitated acquiring quantitatively and qualitatively richer diffusion images. Currently, High Angu-lar Resolution Diffusion Imaging (HARDI) and higher order diffusion models are uniquely positioned to capture and characterise developmental and maturation processes. The De-veloping Human Connectome Project (dHCP) is a group effort to advance the field of pediatric MRI and has made possible much of the work in this thesis. The HARDI data acquired as part of the dHCP captures microstructural properties of the developing brain with an unprecedented quality and information content. Characterising tissue properties requires a model that allows inferring processes on the cellular level from HARDI data. To build this model, it is necessary to incorporate domain knowledge about physical and biological properties of brain tissue. Even for adult populations, where brain tissue properties are comparatively static, developing higher order diffusion models that provide microstructure-specific markers is an open research question [Novikov, Kiselev, Jespersen, 2018]. For these reasons, this thesis investigates the use of data-driven techniques for the study of brain development, which do not require explicit a priori models of tissue microstructure, but rather attempt to decompose the observed signal into interpretable components. In chapter 8, we develop tools to produce an unbiased group template of tissue prop-erties at term, using a method that makes few assumptions about the microstructual properties of neonatal brain tissue. However, rapid brain maturation entails changes in tissue properties that require taking the temporal component into account. This term-time template is extended to the longitudinal domain in chapter 9, capturing tissue maturation patterns from 33 to 44 weeks gestational age in the dHCP cohort. Together, these developments pave the way for detailed investigations into the devel-opment of the human brain. These techniques will form the basis for more advanced analyses, and will hopefully provide useful insights not available using existing methods. Parts of this thesis and work related to experiments performed in this thesis have been presented at conferences under the titles "Effect of demyelination on diffusion ten-sor indices: A Monte Carlo simulation study" [Pietsch, Tournier, 2015], "Multi-contrast diffeomorphic non-linear registration of orientation density functions" [Pietsch et al., 2017a], "Transfer learning and convolutional neural net fusion for motion artefact de-tection" [Kelly et al., 2017], "Multi-shell neonatal brain HARDI template" [Pietsch et al., 2017b], and "Longitudinal multi-component HARDI atlas of neonatal white matter" [Pietsch et al., 2018]. A manuscript with the title "A framework for multi-component analysis of diffusion MRI data over the neonatal period" based on chapter 9 is currently under review in NeuroImage.

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Sawiak, Stephen John. "Computational methods for mouse brain phenotyping using MRI." Thesis, University of Cambridge, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.611550.

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Campbell-Washburn, A. E. "Development of MRI methods for experimental disease models." Thesis, University College London (University of London), 2012. http://discovery.ucl.ac.uk/1378548/.

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Magnetic resonance imaging (MRI) is a powerful technique for the in vivo study of experimental disease models. The application of MRI to animal models requires the development of specialized methods which can provide insight into anatomy, function, physiology and specific pathology. This thesis research focused around the development of MRI methods for imaging the mouse heart and other body organs. In this work, single slice arterial spin labelling (ASL) was implemented and optimized for the in vivo measurement of perfusion in the mouse heart. A fast ECG-gated Look-Locker sequence was used for T1 mapping with data logger recordings for the assessment of respiration corruption and additional prospective gating. A variability and repeatability study was performed to assess the applicability of the technique in vivo. This technique was then extended to have multi-slice capabilities through the implementation of a multi-slice cardiac T1 mapping sequence. In order to apply the multi-slice sequence in vivo, a new method of perfusion quantification was developed to compensate for the input function of the blood magnetization. Amyloidosis is a severe condition where amyloidotic fibrils of mis-folded proteins accumulate in the extracellular space. With the development of new therapies, there is an urgent need for sensitive imaging markers for the monitoring of amyloidosis. In this research, the extracellular volume fraction, as measured using equilibrium contrast MRI with primed infusions of gadolinium, was assessed as a marker for the detection of amyloidosis and for the monitoring of amyloid depletion during therapy. Finally, in order to remove spike noise in MRI data sets, a post-processing algorithm was implemented and validated for the removal of RF spikes in k-space Overall, this thesis research presents methodological developments of cardiac and body MRI for the in vivo study of experimental models of disease.

11

Segerdahl, Tony. "MRI Safety, Test Methods and Construction of a Database." Thesis, Stockholm University, Medical Radiation Physics (together with KI), 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-6968.

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Magnetic Resonance Imaging, MRI, is a diagnostic tool in progress which has been available at major hospitals since the mid eighties. Today almost all hospitals world wide may depict the human body with their own MRI scanner. MRI is dependent on a uniform magnetic field inside the scanner tunnel and Radio frequent (RF) waves used for excitation of the magnetic dipole moments in the body. These properties along with the magnetic field surrounding the scanner are associated with dangerous effects - when interacting with medical implants made of metals. These dangerous effects are twisting forces or torques, heating and translational forces respectively. A database containing information about known implants behaviour regarding these effects among with earlier documentation and information concerning MRI patient safety at Karolinska hospital, Huddinge was constructed.

Also a phantom used for heating effect measurements was constructed and heating effect measurements were performed at a SPC4129 locking titanium Peritoneal Dialysis (PD) catheter adapter and a Deep Brain Stimulator (DBS) in order to test the phantom and confirm the theory about RF induced heating on medical implants. Evidence for heating effects caused by the implants was found.

A torque measurement apparatus was constructed and measurements were performed. All measurements where performed in order to investigate the functionality of the apparatus and also the theory behind dangerous magnetically induced torques (twisting movements). Substantial torque were measured on the ferromagnetic device used for the test.

The heating phantom and torque measurement apparatus is slightly modified models of those proposed by ASTM (American Society for Testing and Materials).

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Payne, Nicholas Roy. "Quadrupolar relaxation-based methods in fast field-cycling MRI." Thesis, University of Aberdeen, 2019. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=240235.

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Quadrupolar relaxation-based methods in Fast Field-Cycling MRI Nicholas R. Payne Aberdeen Biomedical Imaging Centre, University of Aberdeen, AB25 2ZD, Scotland, UK Fast Field-Cycling MRI (FFC-MRI) can access contrast based on the dependence of a sample's relaxation rate to the applied magnetic field strength. This technique can provide signal based on interactions with local quadrupolar nuclei through cross-relaxation, however, there are other so-called Nuclear Quadrupole Double Resonance (NQDR) techniques described in the literature. This work has been centred on efforts to apply these techniques to FFC-MRI and otherwise explore how interactions between protons and quadrupolar nuclei can be further exploited. Through this work two irradiation systems were designed and built for manual tuning, in the first instance, and automatic tuning. The latter was able to quickly retune to coil, however, it was limited in RF power handling capability. A second strand of work was concerned with the reduction in receiver deadtime required to detect signal from solid-state samples such as those previously used in NQDR experiments in the literature. However, circuitry designed to dampen coil ringing by temporarily reducing the resonator's Q-factor following a pulse, along with a novel method utilising field-cycling were not able to reduce the deadtime enough to detect signal from relevant samples. This, coupled with a lack of evidence of NQDR effects in gel-like samples, proved the ultimate stumbling block for NQDR in FFC-MRI. Success was seen in a third strand of work in which simulations were used to design custom experiments which could be used to provide large increases to the signal-to-noise ratio in some experiments. The simulated environment also allowed for fast testing and development of new post-process algorithms which could more accurately calculate relaxation rates. The work concluded that NQDR is unlikely to be useful in FFC-MRI due to the constraints on both the sample and the technique. However the information from quadrupolar nuclei can be improved with better post-processing and tailored pulse sequence parameters.

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Malone, Ian Brian. "Registration based methods for MRI derived PET attenuation correction." Thesis, University of Cambridge, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.612407.

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Lenglet, Christophe. "Geometric and variational methods for diffusion tensor MRI processing." Nice, 2006. http://www.theses.fr/2006NICE4083.

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Cette thèse est consacrée au développement d'outils de traitement pour l'Imagerie par Résonance Magnétique du Tenseur de Diffusion (IRM-TD). Cette technique d'IRM récente est d'une grande importance pour comprendre le fonctionnement du cerveau ou pour améliorer le diagnostic de pathologies neurologiques. Nous proposons des méthodes de traitement basées sur la géométrie Riemannienne, les équations aux dérivées partielles et les techniques de propagation de front. La première partie de ce travail est théorique. Après des rappels sur le système nerveux humain, l'IRM et la géométrie différentielle, nous étudions l'espace des lois normales multivariées. L'introduction d'une structure Riemannienne sur cet espace nous permet de définir des statistiques et des schémas numériques intrinsèques qui sont à la base des algorithmes proposés dans la seconde partie. Les propriétés de cet espace sont importantes pour l'IRM-TD car les tenseurs de diffusion sont les matrices de covariance de lois normales modélisant la diffusion des molécules d'eau en chaque voxel du milieu imagé. La seconde partie est méthodologique. Nous y introduisons des approches originales pour l'estimation et la régularisation d'IRM-TD. Puis nous montrons comment évaluer le degré de connectivité entre aires corticales et introduisons un modèle statistique d'évolution de surface permettant de segmenter ces images. Finalement, nous proposons une méthode de recalage non-rigide. La dernière partie de cette thèse est consacrée à l'analyse des connexions entre le cortex cérébral et les noyaux gris centraux, impliquées dans des tâches motrices, et à l'étude du réseau anatomo-fonctionnel du cortex visuel humain
This thesis deals with the development of new processing tools for Diffusion Tensor Magnetic Resonance Imaging (DT-MRI). This recent MRI technique is of utmost importance to acquire a better understanding of the brain mechanisms and to improve the diagnosis of neurological disorders. We introduce new algorithms relying on Riemannian geometry, partial differential equations and front propagation techniques. The first part of this work is theoretical. After a few reminders about the human nervous system, MRI and differential geometry, we study the space of multivariate normal distributions. The introduction of a Riemannian structure on that space allows us to define statistics and intrinsic numerical schemes that will constitute the core of the algorithms proposed in the second part. The properties of that space are important for DT-MRI since diffusion tensors are the covariance matrices of normal laws modeling the diffusion of water molecules at each voxel of the acquired volume. The second part of this thesis is methodological. We start with the introduction of original approaches for the estimation and regularization of DT-MRI. We then show how to evaluate the degree of connectivity between cortical areas. Next, we introduce a statistical surface evolution framework for the segmentation of those images. Finally, we propose a non-rigid registration method. The last part of this thesis is dedicated to the application of our tools to two important neuroscience problems: the analysis of the connections between the cerebral cortex and the basal ganglia, implicated in motor tasks, and the study of the anatomo-functional network of the human visual cortex

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Buchanan, Colin Richard. "Structural brain networks from diffusion MRI : methods and application." Thesis, University of Edinburgh, 2015. http://hdl.handle.net/1842/14183.

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Structural brain networks can be constructed at a macroscopic scale using diffusion magnetic resonance imaging (dMRI) and whole-brain tractography. Under this approach, grey matter regions, such as Brodmann areas, form the nodes of a network and tractography is used to construct a set of white matter fibre tracts which form the connections. Graph-theoretic measures may then be used to characterise patterns of connectivity. In this study, we measured the test-retest properties of such networks by varying several factors affecting network construction using ten healthy volunteers who underwent a dMRI protocol at 1.5 T on two separate occasions. High resolution T1-weighted brains were parcellated into regions-of-interest and network connections were identified using dMRI and two alternative tractography algorithms, two alternative seeding strategies, constraints on anatomical plausibility and three alternative network weightings. Test-retest performance was found to improve when: 1) seeding from white matter, rather than grey; and 2) using probabilistic tractography, rather than deterministic. In terms of network weighting, a measure of streamline density produced better test-retest performance than tract-averaged diffusion anisotropy, although it remains unclear which is most representative of the underlying axonal connections. These findings were then used to inform network construction for two further cohorts: a casecontrol analysis of 30 patients with amyotrophic lateral sclerosis (ALS) compared with 30 age-matched healthy controls; and a cross-sectional analysis of 80 healthy volunteers aged 25– 64 years. In both cases, networks were constructed using a weighting reflecting tract-averaged fractional anisotropy (FA). A mass-univariate statistical technique called network-based statistics, identified an impaired motor-frontal-subcortical subnetwork (10 nodes and 12 bidirectional connections), consistent with upper motor neuron pathology, in the ALS group compared with the controls. Reduced FA for three of the impaired network connections, which involved fibres of the cortico-spinal tract, were significantly correlated with the rate of disease progression. Cross-sectional analysis of the 80 healthy volunteers was intended to provide supporting evidence for the widely reported age-related decline in white matter integrity. However, no meaningful relationships were found between increasing age and impaired connectivity based on global, lobar and nodal network properties – findings which were confirmed with a conventional voxel-based analysis of the dMRI data. In conclusion, whilst current acquisition protocols and methods can produce networks capable of characterising the genuine between-subject differences in connectivity, it is challenging to measure subtle white matter changes, for example, due to normal ageing. We conclude that future work should be undertaken to address these concerns.

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Suever, Jonathan D. "MRI methods for predicting response to cardiac resynchronization therapy." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/50224.

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Cardiac Resynchronization Therapy (CRT) is a treatment option for heart failure patients with ventricular dyssynchrony. CRT corrects for dyssynchrony by electrically stimulating the septal and lateral walls of the left ventricle (LV), forcing synchronous con- traction and improving cardiac output. Current selection criteria for CRT rely upon the QRS duration, measured from a surface electrocardiogram, as a marker of electrical dyssynchrony. Unfortunately, 30-40% of patients undergoing CRT fail to benefit from the treatment. A multitude of studies have shown that presence of mechanical dyssynchrony in the LV is an important factor in determining if a patient will benefit from CRT. Furthermore, recent evidence suggests that patient response can be improved by placing the LV pacing lead in the most dyssynchronous or latest contracting segment. The overall goal of this project was to develop methods that allow for accurate assessment and display of regional mechanical dyssynchrony throughout the LV and at the site of the LV pacing lead. To accomplish this goal, we developed a method for quantifying regional dyssynchrony from standard short-axis cine magnetic resonance (MR) images. To assess the effects of LV lead placement, we developed a registration method that allows us to project the LV lead location from dual-plane fluoroscopy onto MR measurements of cardiac function. By applying these techniques in patients undergoing CRT, we were able to investigate the relationship between regional dyssynchrony, LV pacing lead location, and CRT response.

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Cui, Chen. "MRI fat-water separation using graph search based methods." Diss., University of Iowa, 2017. https://ir.uiowa.edu/etd/5740.

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The separation of water and fat from multi-echo images is a classic problem in magnetic resonance imaging (MRI) with a wide range of important clinical applications. For example, removal of fat signal can provide better visualization of other signal of interest in MRI scans. In other cases, the fat distribution map can be of great importance in diagnosis. Although many methods have been proposed over the past three decades, robust fat water separation remains a challenge as radiological technology and clinical expectation continue to grow. The problem presents three key difficulties: a) the presence of B0 field inhomogeneities, often large in the state-of-the-art research and clinical settings, which makes the problem non-linear and ill-posed; b) the ambiguity of signal modeling in locations with only one metabolite (either fat or water), which can manifest as spurious fat water swaps in the separation; c) the computational expenditure in fat water separation as the size of the data is increasing along with evolving MRI hardware, which hampers the clinical applicability of the fat water separation. The main focus of this thesis is to develop novel graph based algorithms to estimate the B0 field inhomogeneity maps and separate fat water signals with global accuracy and computational efficiency. We propose a new smoothness constrained framework for the GlObally Optimal Surface Estimation (GOOSE), in which the spatial smoothness of the B0 field is modeled as a finite constraint between adjacent voxels in a uniformly discretized graph. We further develop a new non-equidistant graph model that enables a Rapid GlObally Optimal Surface Estimation (R-GOOSE) in a subset of the fully discretized graph in GOOSE. Extensions of the above frameworks are also developed to achieve high computational efficiency for processing large 3D datasets. Global convergence of the optimization formulation is proven in all frameworks. The developed methods have also been extensively compared to the existing state-of-the-art fat water separation methods on a variety of datasets with consistent performance of high accuracy and efficiency.

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Saleh, Muhammad G. "Methods and adaptations required to perform small-animal MRI scanning using a large bore clinical MRI." Master's thesis, University of Cape Town, 2012. http://hdl.handle.net/11427/22098.

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Small-animal imaging has been widely implemented to study succession of disease, therapeutic treatments and the effects of environmental insults. The gold standard noninvasive technique for following progression of heart failure in small-animal models is magnetic resonance imaging (MRI). The aim of this project was to adapt a clinical MRI system to perform small-animal cardiac MRI. The first part of the thesis describes the adaptations required, which included design and construction of a small-animal radiofrequency (RF) coil, physical support (cradle), a core body temperature regulation system, and optimization of pulse sequences. The system was validated using a phantom and in-vivo in 5 healthy rats. The signal-to-noise ratio (SNR) in the phantom was 91% higher using the small-animal coil compared to the standard head coil. SNRs of 7 ± 2 and 18.9 ± 0.6 were achieved in myocardium and blood, respectively, in healthy rats and MR left ventricular mass (LVM) was highly correlated with (r=0.87) with post-mortem mass. In the second part of the study, left ventricular remodeling (LVR) was investigated in a nonreperfused model of myocardial infarction (MI) in 5 sham and 7 infarcted rats. Rats were scanned at 2 and 4 weeks post surgery to allow for global and regional functional and structural analyses of the heart. Images were of sufficient quality to enable semi-automatic segmentation using Segment. Significant increase in end-systolic volume (ESV) was observed in MI rats at 2 weeks post surgery. At 4 weeks post surgery, end-diastolic volume (EDV) and ESV of MI rats were significantly higher than in sham rats. Ejection fraction (EF) of MI rats dropped significantly at 2 weeks and a further significant drop was observed at 4 weeks indicating contractile dysfunction. Wall thickness (WTh) analyses in MI rats at 4 weeks revealed significant reduction in end-diastolic (ED) wall thickness in the anterior region due to necrosis of myocytes. In the posterior region, WTh was significantly higher due to LV hypertrophy. At end-systole (ES), the MI rats revealed significant decrease in WTh in the anterior and lateral regions. MI rats suffered reduction in systolic wall thickening in all regions of the heart, indicating global contractile dysfunction.

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Poggiali, Davide. "Postprocessing Neuroimaging methods in MRI and PET/MRI with applications to Multiple Sclerosis and other Neurological diseases." Doctoral thesis, Università degli studi di Padova, 2016. http://hdl.handle.net/11577/3421919.

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Many non-invasive imaging instruments have been developed in the last 40 years, allowing to obtain images of the interior human body while the patient is still alive. In the contest of Neurology studies, imaging system as CT, MRI, SPECT or PET allows to obtain biomarkers useful to quantitatively distinguish between healthy and unhealthy subjects, evaluate the staging of a Neurological illness in a patient, evaluate the efficacy of a treatment, explore the causes of the illness. In this work MRI and PET imaging system introduced from scratch, going from reconstruction from raw data to state-of-the art post-processing techniques and the computation of more popular biomarkers. After these introduction, three original work using the recent PET/MRI imaging system are presented, with a particular focus on the methods. These three studies involve patients with Multiple Sclerosis, Alzheimer's Disease and Brain Tumor.
Negli ultimi 40 anni sono stati sviluppati diversi strumenti di imaging non-invasivi, in modo da ottenere immagini dell'interno del corpo umano mentre il paziente è ancora in vita. Nel contesto neurologico, sistemi di imaging come TAC, RM, SPECT e PET permettono di ottenere biomarcatori utili a distinguere quantitativamente soggetti sani da pazienti con malattie neurologiche, valutare lo stato di avanzamento di una malattia in un paziente, valutare l'efficacia di un trattamento, esplorare le cause della malattia. Nel presente lavoro si presentano i sistemi di acquisione di immagini RM e PET fin dalle fondamenta, partendo dai metodi di ricostruzione dell'immagine dai dati grezzi, allo stato dell'arte dei metodi di post-processing, fino al calcolo dei biomarcatori più diffusi. Dopo tale introduzione saranno presentati tre lavori originali di imaging PET/MRI, con una particolare attenzione ai metodi. Questi tre lavori riguardano pazienti con Sclerosi Multipla, Morbo di Alzheimer e Tumori Cerebrali.

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Callaghan, Martina. "PadeÌ methods for image reconstruction and feature extraction in MRI." Thesis, Imperial College London, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.416865.

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Mills, Parker H. "Computational Methods for Enhancing Sensitivity to MRI Cell-Tracking Agents." Research Showcase @ CMU, 2011. http://repository.cmu.edu/dissertations/67.

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New methods for programming cells to perform desired functions in vivo promise to enable new diagnostic tools and therapies. To develop, confidently deploy, and routinely use these emerging cellular therapies, it is necessary to have the ability to non-invasively detect and monitor transplanted cells, or those that have been genetically-modified in situ. MRI offers non-invasive high-resolution imaging within deep tissues without the use of ionizing radiation. In order for cells of interest to appear in MR images, they are labeled with iron oxide contrast agent (CA), genetic instructions to produce their own CA, or with fluorine-based tracer agents. Regardless of the type of label used, it is a challenge to achieve sufficient MR image signal and contrast in order to differentiate labeled cells from background tissue or image noise, especially when they reside in inhomogeneous tissue, or when scan time is limited. Improvements in sensitivity to labeled cells are needed for their ready detection and quantification. Cells labeled with iron oxide CA appear in conventional proton (1H) MR images as hypo-intense spots or regions within the organ or anatomy being imaged. To facilitate cell-tracking that employs iron-oxides, we present three methods: The first, called the Two-Compartment T2 Contrast Model (T2CM), is a model for predicting the relationship between iron oxide CA concentration and expected image contrast. The second method, called Phase Slope Magnitude Imaging (PSM), highlights arbitrary distributions of iron oxide CA in tissue. The third method, called Phase Map Cross-Correlation Detection and Quantification (PDQ), detects isolated magnetic dipoles that indicate the presence of an iron oxide-labeled cell or cell cluster. PDQ then measures the magnetic moment of each dipole and registers its location for the purpose of cell-tracking and 3D visualization. Cells labeled with fluorine-based tracer agents appear in fluorine (19F) MR images as hyper- intense spots or regions against a background of only image noise. The background is devoid of anatomical features, since tissue fluorine concentration is insignificant relative to that within labeled cells -- distinguishing fluorine tracer from anatomical tissue features is not an issue. However, fluorine-based tracer agents often have a sparse spatial distribution and produce low levels of MRI signal, so it is often difficult to distinguish labeled cells from background image noise, especially when scan times are limited during in vivo experiments. To facilitate cell-tracking that employs fluorine-based tracers, we implemented and evaluated compressed sensing acquisition and reconstruction. This method generates 3D images with higher signal-to-noise ratios than conventional methods, allowing for 3D fluorine acquisitions with higher resolutions or shortened scan times. Overall these methods for enhancing sensitivity to cells labeled with iron oxide CAs and fluorine tracer agents will help enable MRI as a platform for detecting and tracking cells in living subjects. Improved MRI cell monitoring will help researchers understand how normal and diseased cells behave and migrate inside living systems, and will help to determine the efficacy of new cellular therapies.

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Azeredo, Gomes Teixeira Rui Pedro. "Optimized variable flip angle methods for single pool MRI relaxometry." Thesis, King's College London (University of London), 2017. https://kclpure.kcl.ac.uk/portal/en/theses/optimized-variable-flip-angle-methods-for-single-pool-mri-relaxometry(f9a620f9-d722-482b-ae07-78a80f783eac).html.

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Magnetic Resonance Imaging (MRI) is routinely used as a highly, soft-tissue sen-sitive qualitative modality. Thus, although widely used as a first line of investiga-tion for both radiological diagnosis and treatment monitoring of neurological dis-ease, almost all assessments are based on images presented in arbitrary units. With this in mind, there is a growing interest in quantitative MRI as a potential route to less subjective diagnosis and to allow cross site comparison studies. Key MR parameters are the proton density M0 and relaxation times T1 and T2 which are strongly associated with tissue integrity. This absolute tissue specific measurements, are expected to overcome inter-site bias in multi-centre studies as opposed to conventional M0, T1 and T2 weighted images whose use is still controversial. Unfortunately, gold standard methods for estimating relaxation times are two dimensional acquisitions based on spin-echo processes which require long acqui-sition times. On the contrary, many gradient echo techniques, such as Variable Flip Angle (VFA), Driven Equilibrium Single Pulse Observation of T1/2 (DESPOT), Muti-Parametric Mapping(MPM), etc, have been developed to infer tissue MR properties in clinically feasible times. However, a consensus regarding the accu-racy of each method has still to be found. One possible source for the reported discrepancies between methods, is the fact that, in biological samples, a process called Magnetization Transfer (MT) is known to influence the observed relaxom-etry measurements. To characterize tissue more fully, so called multiple-pool models have been suggested. Current clinical protocols for quantitative imaging generally fail to take MT correctly into account, and therefore produce variable results that undermine their utility as secure diagnostic methods. Quantitative MT protocols can more precisely characterise tissue, but require more data to be col-lected so are not regarded as clinically feasible. The work here presented, built on single-compartment DESPOT relaxome-try approach and sought to increase its precision of by two means: (i) a joint system relaxometry (JSR) approach that estimates parameters in a single step using all available data; and (ii) optimizing acquisition parameters by deploying a robust design tool based on the Cr ́amer-Rao lower bound (CRLB). Once this was achieved, the absolute accuracy of gradient echo methods was explored by exploring the influence of magnetization transfer effects on single-pool assump-tions. It was then hypothesised that robust relaxometry methods can be achieved by ensuring Constant Saturation of Magnetization Transfer (CSMT) effects. This was demonstrated both numerically and experimentally.

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Wu, Wenchuan. "Acquisition and reconstruction methods for hybrid 2D/3D diffusion MRI." Thesis, University of Oxford, 2018. http://ora.ox.ac.uk/objects/uuid:b6de8931-3910-4342-acdb-4eac49263b2c.

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Diffusion magnetic resonance imaging (MRI) has been increasingly used in neuroscience studies, particularly for mapping white matter tracts in the brain. Typically, diffusion MRI is acquired using a two-dimensional (2D) single-shot echo planar imaging sequence, which allows rapid acquisition and reduced sensitivity to subject motion. However, conventional 2D diffusion MRI faces many limitations, such as long TR (repetition time) which lead to low SNR (signal-to-noise ratio) efficiency, and long scan times with advanced diffusion protocols which usually require a large number of diffusion directions and/or b values. These limitations become more acute at high spatial resolution. Three-dimensional (3D) methods have also been developed for diffusion MRI, but they are not widely used for acquiring data in vivo due to the challenges in subject motion. Hybrid 2D/3D methods, including 3D multi-slab acquisition and simultaneous multi-slice acquisition, have been recently proposed to address the limitations of conventional 2D and 3D methods. However, 3D multi-slab acquisition faces the problem of slab boundary artefacts, which could decrease the image quality and propagate into diffusion quantifications. Simultaneous multi-slice acquisition would suffer from significant noise amplification when in-plane under-sampling is also applied. The work in this thesis seeks to develop acquisition and reconstruction methods to improve hybrid 2D/3D diffusion MRI. A new method is proposed to correct slab boundary artefacts in 3D multi-slab imaging, which jointly estimates the slab profile and underlying image using a nonlinear reconstruction. Correction results demonstrate superior performance compared with previously proposed methods. A k-q acquisition and reconstruction approach is developed to accelerate diffusion MRI based on Gaussian process methods. Here, we target improvements to simultaneous multi-slice imaging, demonstrating high acceleration factors in combination with in-plane under-sampling. Combinations of hybrid 2D/3D acquisitions with the ultra-high field of 7T are also investigated, which could enable high resolution diffusion MRI without substantially compromising SNR. The methods developed in this work are expected to improve the data quality and scan efficiency of diffusion MRI.

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Padormo, Francesco. "Advanced methods for mapping the radiofrequency magnetic fields in MRI." Thesis, Imperial College London, 2013. http://hdl.handle.net/10044/1/14630.

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As MRI systems have increased in static magnetic field strength, the radiofrequency (RF) fields that are used for magnetisation excitation and signal reception have become significantly less uniform. This can lead to image artifacts and errors when performing quantitative MRI. A further complication arises if the RF fields vary substantially in time. In the first part of this investigation temporal variations caused by respiration were explored on a 3T scanner. It was found that fractional changes in transmit field amplitude between inhalation and expiration ranged from 1% to 14% in the region of the liver in a small group of normal subjects. This observation motivated the development of a pulse sequence and reconstruction method to allow dynamic observation of the transmit field throughout the respiratory cycle. However, the proposed method was unsuccessful due to the inherently time-consuming nature of transmit field mapping sequences. This prompted the development of a novel data reconstruction method to allow the acceleration of transmit field mapping sequences. The proposed technique posed the RF field reconstruction as a nonlinear least-squares optimisation problem, exploiting the fact that the fields vary smoothly. It was shown that this approach was superior to standard reconstruction approaches. The final component of this thesis presents a unified approach to RF field calibration. The proposed method uses all measured data to estimate both transmit and receive sensitivities, whilst simultaneously insisting that they are smooth functions of space. The resulting maps are robust to both noise and imperfections in regions of low signal.

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Ahluwalia, Vishwadeep. "Optimization of Functional MRI methods for olfactory interventional studies at 3T." VCU Scholars Compass, 2009. http://scholarscompass.vcu.edu/etd/1953.

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Functional MRI technique is vital in investigating the effect of an intervention on cortical activation in normal and patient population. In many such investigations, block stimulation paradigms are still the preferred method of inducing brain activation during functional imaging sessions because of the high BOLD response, ease in implementation and subject compliance especially in patient population. However, effect of an intervention can be validly interpreted only after reproducibility of a detectable BOLD response evoked by the stimulation paradigm is first verified in the absence of the intervention. Detecting a large BOLD response that is also reproducible is a difficult task particularly in olfactory Functional MRI studies due to the factors such as (a) susceptibility-induced signal loss in olfactory related brain areas and (b) desensitization to odors due to prolonged odor stimulation, which is typical when block paradigms are used. Therefore, when block paradigms are used in olfactory interventional Functional MRI studies, the effect of the intervention may not be easily interpretable due to the factors mentioned above. The first task of this thesis was to select a block stimulation paradigm that would produce a large and reproducible BOLD response. It was hypothesized that a BOLD response of this nature could be produced if within-block and across-session desensitization could be minimized and further, that desensitization could be minimized by reducing the amount of odor by pulsing the odor stimulus within a block instead of providing a continuous odor throughout the block duration. Once the best paradigm was selected, the second task of the thesis was to select the best model for use in general linear model (GLM) analysis of the functional data, so that robust activation is detected in olfactory related brain areas. Finally, the third task was to apply the paradigm and model that were selected as the best among the ones tested in this thesis, to an olfactory interventional Functional MRI study investigating the effect of food (bananas) eaten to satiety on the brain activation to the odor related to that food. The methods used in this thesis to ensure valid interpretation of an interventional effect, can serve as a template for the experimental design of future interventional Functional MRI studies.

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Huang, Chuan. "Novel Methods for T2 Estimation Using Highly Undersampled Radial MRI Data." Diss., The University of Arizona, 2011. http://hdl.handle.net/10150/203531.

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The work presented in this dissertation involves the development of parametric magnetic resonance imaging (MRI) techniques that can be used in a clinical set up. In the first chapter an introduction of basic magnetic resonance physics is given. The introduction covers the source to tissue magnetization, the origin of the detectable signal, the relaxation mechanisms, and the imaging principles. In the second chapter T₂ estimation - the main parametric MRI technique addressed in this work - is introduced and the problem associated with T₂ estimation from highly undersampled fast spin-echo (FSE) data is presented. In Chapter 3, a novel model-based algorithm with linearization by principal component analysis (REPCOM) is described. Based on simulations, physical phantom and in vivo data, the proposed algorithm is shown to produce accurate and stable T₂ estimates. In Chapter 4, the concept of indirect echoes associated with the acquisition of FSE data is introduced. Indirect echo correction using the extended phase graph approach is then studied for standard sampled data. A novel reconstruction algorithm (SERENADE) is presented for the reconstruction of decay curves with indirect echoes from highly undersampled data. The technique is evaluated using simulations, physical phantom and in vivo data; decay curves with indirect echoes are shown to be accurately recovered by this technique. Chapter 5 is dedicated to correcting the partial volume effect (PVE) in T₂ estimation. For the case of small lesions within a background tissue, PVE affects T₂ estimation which in turn affects lesion classification. A novel joint fitting algorithm is proposed and compared to conventional fitting algorithms using fully sampled spin-echo (SE) images. It is shown that the proposed algorithm is more accurate, robust, and insensitive to region of interest drawing than the conventional fitting algorithms. Because the acquisition of fully sampled SE images is long, the technique is combined with a thick refocusing slice approach in order to be able to use undersampled FSE data and reduce the acquisition time to a breath hold (~ 20 s). The final chapter summarizes the results presented in the dissertations and discusses areas for future work.

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Silvestri, Erica. "Simultaneous PET/MRI for Connectivity Mapping: Quantitative Methods in Clinical Setting." Doctoral thesis, Università degli studi di Padova, 2018. http://hdl.handle.net/11577/3426715.

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In recent years, the study of brain connectivity has received growing interest from neuroscience field, from a point of view both of analysis of pathological condition and of a healthy brain. Hybrid PET/MRI scanners are promising tools to study this complex phenomenon. This thesis presents a general framework for the acquisition and analysis of simultaneous multi-modal PET/MRI imaging data to study brain connectivity in a clinical setting. Several aspects are faced ranging from the planning of an acquisition protocol consistent with clinical constraint to the off-line PET image reconstruction, from the selection and implementation of methods for quantifying the acquired data to the development of methodologies to combine the complementary information obtained with the two modalities. The developed analysis framework was applied to two different studies, a first conducted on patients affected by Parkinson’s Disease and dementia, and a second one on high grade gliomas, as proof of concept evaluation that the pipeline can be extended in clinical settings.

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Yang, Zheyi. "Numerical methods to estimate brain micro-structure from diffusion MRI data." Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAE016.

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L'imagerie par résonance magnétique de diffusion (IRM de diffusion) est une modalité d'imagerie non invasive couramment utilisée pour mesurer les propriétés micro-structurales des tissus biologiques au dessous de la résolution spatiale, en mesurant indirectement le déplacement de diffusion des molécules d'eau. En raison de la complexité géométrique du cerveau et du mécanisme complexe de l'IRM de diffusion, il est difficile de relier directement les signaux reçus à des paramètres biophysiques significatifs, tels que le diamètre des axones ou la densité. Ces dernières années, plusieurs modèles biophysiques ont été introduits pour répondre à ce problème de la faible interprétabilité. Ces modèles représentent les signaux d'IRM de diffusion comme un mélange de signaux analytiques sous certaines hypothèses, par exemple des membranes imperméables, de différentes géométries simples et non connectées, par exemple des sphères et des bâtonnets. Par la suite, ils visent à extraire les paramètres de ces géométries simples, qui sont corrélés avec des paramètres biophysiques, en inversant la formulation analytique. Cependant, la validité de ces hypothèses reste indéterminée dans les expériences réelles. L'objectif de cette thèse est d'améliorer la fiabilité et l'efficacité de l'estimation de la microstructure par deux moyens. Tout d'abord, pour faciliter l'étude quantitative de la domaine de validité des modèles biophysiques et de l'effet de la déformation géométrique et de la perméabilité de la membrane cellulaire par simulation, nous avons proposé deux modèles réduits dérivés de l'équation de Bloch-Torrey, respectivement. Dans le cas de membranes perméables, une nouvelle approche de simulation utilisant une base propre de Laplace imperméable est proposée. Quant à la déformation géométrique, nous utilisons une expansion asymptotique par rapport aux angles de déformation pour approximer le signal. Ces deux modèles réduits permettent de faire les calculs efficaces des signaux pour diverses valeurs de déformation/perméabilité. Des simulations numériques montrent que ces deux modèles peuvent rapidement calculer les signaux avec un niveau d'erreur raisonnable par rapport aux méthodes existantes. Plusieurs études ont été menées sur les effets de la perméabilité et de la déformation sur les signaux ou sur le coefficient de diffusion efficace (ADC en anglais), en utilisant les modèles proposés. Deuxièmement, au lieu d'inverser un modèle de géométries simplifiées, nous présentons une nouvelle approche pour associer la taille des somas dans la matière grise par des biomarqueurs intermédiaires. Des simulations numériques identifient une corrélation entre le diamètre/densité des somas et le point d'inflexion des signaux moyennés sur la direction à des amplitudes élevées (b>2500s/mm^2), offrant des perspectives pour l'estimation de la microstructure. Nous adaptons un réseau neuronal entièrement connecté en utilisant ces biomarqueurs et comparé aux modèles biophysiques, cette approche offre des résultats comparables sur les données synthétiques et in vivo et une estimation rapide car aucune inversion n'est impliquée
Diffusion magnetic resonance imaging (diffusion MRI) is a widely used non-invasive imaging modality to probe the micro-structural properties of biological tissues below the spatial resolution, by indirectly measuring the diffusion displacement of water molecules. Due to the geometrical complexity of the brain and intricate diffusion MRI mechanism, it is challenging to directly link the received signals to meaningful biophysical parameters, such as axon radii or volume fraction.In recent years, several biophysical models have been introduced to address the issue of weak interpretability. These models represent the diffusion MRI signals as a mixture of analytical signals under certain assumptions, e.g. impermeable membranes, of various disconnected simple geometries, such as spheres and sticks. Subsequently, they aim to extract the parameters of these geometries, which correlate with biophysical parameters, by inverting the analytical expression.However, the validity of these assumptions remains undetermined in actual experiments.The objective of this thesis is to improve the microstructure estimation reliability and efficiency from two perspectives. First, to facilitate the quantitative study of the valid range of biophysical models and the effect of geometrical deformation and cell membrane permeability via simulation, we proposed two reduced models derived from the Bloch-Torrey equation, respectively. For the case of the presence of permeable membranes, a new simulation approach using impermeable Laplace eigenbasis is proposed. As for the geometrical deformation, we use an asymptotic expansion with respect to the deformation angles to approximate the signal. These two reduced models enable efficient computation of signals for various values of deformation/permeability. Numerical simulations reveal that these two models can fast compute the signals within a reasonable error level compared to existing methods. Several studies have been conducted about the effects of permeability and deformation on the signals or the apparent diffusion coefficient (ADC), using the proposed models.Second, instead of inverting a simplified geometries model, we present a novel approach to associate soma size in gray matter by intermediary biomarkers. Numerical simulations identify a correlation between the volume-weighted soma radius/volume fraction and the inflection point of direction-averaged signals at high b-values (b>2500s/mm^2), offering insights for microstructure estimation. We fit a fully connected neural network using these biomarkers and compared to biophysical models, this approach offers comparable results on both synthetic and in vivo data and fast estimation since no inversion is involved

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Merola, Alberto. "Development of MRI methods to map cerebral metabolic oxygen consumption in humans." Thesis, Cardiff University, 2016. http://orca.cf.ac.uk/91542/.

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The quantification of brain activity has been one of the main goals of neuroimaging since the earliest applications. In functional magnetic resonance imaging (fMRI) such an aim has been pursued indirectly by studying changes of the blood oxygenation dependent signal triggered by alterations in blood flow following changes in energy metabolism. Such approach is limited because of the complex relationship between the vascular and neural systems in brain tissue. Therefore methods have been proposed to assess oxygen metabolism, which directly underlies energy supply to brain tissue and therefore brain activity. Investigating existing and novel MRI methods, the thesis aims to improve the assessment of oxygen metabolism for a fully quantitative measurement of this biomarker. A simulation study has been carried out to optimise one of the mathematical (fMRI calibration) models used to relate the measured signal to the underlying physiology. As a result we are able to define a new model, less complex and more accurate for estimation of oxygen extraction fraction. Following this, an estimation approach recently developed in our centre is applied to carbon dioxide and oxygen calibrated fMRI data in an experimental setting firstly for a repeatability study and then for a drug study looking at the acute effects of caffeine on brain metabolism and haemodynamics. The precision of the novel approach shows values consistent with previous methods, but with much higher spatial resolution. Exploiting this, acute caffeine effects are characterized with a voxel-wise level of detail, showing results consistent with literature electrophysiological findings. Finally, an innovative method for estimating oxygen extraction fraction, based on velocity spectral imaging and estimation of transverse relaxation time, is introduced and tested at a proof-of-concept level. The performance and limits are examined through simulation and experimentation, suggesting that it might be a viable alternative to the calibration techniques previously introduced.

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Chao, Hui Ph D. Massachusetts Institute of Technology. "Multi-echo methods for fast MRI and MRS of ³¹P containing compounds." Thesis, Massachusetts Institute of Technology, 1997. http://hdl.handle.net/1721.1/43335.

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Schwarz, Jolanda M. [Verfasser]. "Advanced Image Reconstruction Methods for Ultra-High Field MRI / Jolanda M. Schwarz." Bonn : Universitäts- und Landesbibliothek Bonn, 2020. http://d-nb.info/1218474947/34.

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Mott, Lisa. "Efficient statistical methods for inference and model selection in diffusion-weighted MRI models." Thesis, University of Nottingham, 2016. http://eprints.nottingham.ac.uk/31173/.

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Diffusion-Weighted Magnetic Resonance Imaging (DW-MRI) on the brain is a revolutionary method that provides in-vivo access to tissue macrostructure non-invasively (Basser et al., 1994). Recently, DW-MRI has been shown to have great potential in characterising brain microstructure, such as diameter and size distribution of neuronal fibres, features that were available so far only postmortem or through animal studies (Zhang et al., 2011). Using a process known as Tractography the existence of brain connections can be estimated using a set of DW images (Basser et al., 2000). The main aim of this thesis is to develop efficient methods for studying Tractography within a Bayesian framework. In order to characterise the white matter in the brain we focus on the widely used partial volume model (Behrens et al., 2003). We describe methods that are both time and computationally efficient for estimating the parameters of the partial volume model, before reparametrising the model, so that parameter estimation is viable in some special cases. The partial volume model allows for multiple fibre orientations so we develop methodology to choose between the number of white matter fibres in a voxel. We then take into account the uncertainty in the number of fibre orientations and provide a Fully Probabilistic Tractography method as an alternative to existing Tractography algorithms. Finally we look into the Global Tractography model (Jbabdi et al., 2007) and develop efficient methods for inferring connections between brain regions by investigating methods based on Thermodynamic Integration.

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Denolin, Vincent. "Sources of contrast and acquisition methods in functional MRI of the human brain." Doctoral thesis, Universite Libre de Bruxelles, 2002. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/211408.

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L'Imagerie fonctionnelle par Résonance Magnétique (IRMf) a connu un développement important depuis sa découverte au début des années 1990. Basée le plus souvent sur l'effet BOLD (Blood Oxygenation Level Dependent), cette technique permet d'obtenir de façon totalement non-invasive des cartes d'activation cérébrale, avec de meilleures résolutions spatiale et temporelle que les méthodes préexistantes telles que la tomographie par émission de positrons (TEP). Facilement praticable au moyen des imageurs par RMN disponible dans les hôpitaux, elle a mené à de nombreuses applications dans le domaine des neurosciences et de l'étude des pathologies cérébrales.

Il est maintenant bien établi que l'effet BOLD est dû à une augmentation de l'oxygénation du sang veineux dans les régions du cerveau où se produit l'activation neuronale, impliquant une diminution de la différence de susceptibilité magnétique entre le sang et les tissus environnants (la déoxyhémoglobine étant paramagnétique et l'oxyhémoglobine diamagnétique), et par conséquent un augmentation du signal si la méthode d'acquisition est sensible aux inhomogénéités de champ magnétique. Cependant, il reste encore de nombreuses inconnues quant aux mécanismes liant les variations d'oxygénation, de flux et de volume sanguin à l'augmentation de signal observée, et la dépendance du phénomène en des paramètres tels que l'intensité du champ, la résolution spatiale, et le type de séquence de RMN utilisée. La première partie de la thèse est donc consacrée à l'étude de l'effet BOLD, dans le cas particulier des contributions dues aux veines de drainage dans les séquences de type écho de gradient rendues sensibles au mouvement par l'ajout de gradients de champ. Le modèle développé montre que, contrairement au comportement suggéré par de précédentes publications, l'effet de ces gradients n'est pas une diminution monotone de la différence de signal lorsque l'intensité des gradients augmente. D'importantes oscillations sont produites par l'effet de phase dû au déplacement des spins du sang dans les gradients additionnels, et par la variation de cette phase suite à l'augmentation du flux sanguin. La validation expérimentale du modèle est réalisée au moyen de la séquence PRESTO (Principles of Echo-Shifting combined with a Train of Observations), c'est-à-dire une séquence en écho de gradient où des gradients supplémentaires permettent d'augmenter la sensibilité aux inhomogénéités de champ, et donc à l'effet BOLD. Un accord qualitatif avec la théorie est établi en montrant que la variation de signal observée peut augmenter lorsqu'on intensifie les gradients additionnels.

Un autre source de débat continuel dans le domaine de l'IRMf réside dans l'optimalisation des méthodes d'acquisition, au point de vue notamment de leur sensibilité à l'effet BOLD, leurs résolutions spatiale et temporelle, leur sensibilité à divers artefacts tels que la perte de signal dans les zones présentant des inhomogénéités de champ à grande échelle, et la contamination des cartes d'activation par les contributions des grosses veines, qui peuvent être distantes du lieu d'activation réel. Les séquences en écho de spin sont connues pour être moins sensibles à ces deux derniers problèmes, c'est pourquoi la deuxième partie de la thèse est consacrée à une nouvelle technique permettant de donner une pondération T2 plutôt que T2* aux images. Le principe de base de la méthode n'est pas neuf, puisqu'il s'agit de la « Préparation T2 » (T2prep), qui consiste à atténuer l'aimantation longitudinale différemment selon la valeur du temps de relaxation T2, mais il n’avait jamais été appliqué à l’IRMf. Ses avantages par rapport à d’autres méthodes hybrides T2 et T2* sont principalement le gain en résolution temporelle et en dissipation d’énergie électromagnétique dans les tissus. Le contraste généré par ces séquences est étudié au moyen de solutions stationnaires des équations de Bloch. Des prédictions sont faites quant au contraste BOLD, sur base de ces solutions stationnaires et d’une description simplifiée de l’effet BOLD en termes de variations de T2 et T2*. Une méthode est proposée pour rendre le signal constant au travers du train d’impulsions en faisant varier l’angle de bascule d’une impulsion à l’autre, ce qui permet de diminuer le flou dans les images. Des expériences in vitro montrent un accord quantitatif excellent avec les prédictions théoriques quant à l’intensité des signaux mesurés, aussi bien dans le cas de l’angle constant que pour la série d’angles variables. Des expériences d’activation du cortex visuel démontrent la faisabilité de l’IRMf au moyen de séquences T2prep, et confirment les prédictions théoriques quant à la variation de signal causée par l’activation.

La troisième partie de la thèse constitue la suite logique des deux premières, puisqu’elle est consacrée à une extension du principe de déplacement d’écho (echo-shifting) aux séquences en écho de spin à l’état stationnaire, ce qui permet d’obtenir une pondération T2 et T2* importante tout en maintenant un temps de répétition court, et donc une bonne résolution temporelle. Une analyse théorique approfondie de la formation du signal dans de telles séquences est présentée. Elle est basée en partie sur la technique de résolution des équations de Bloch utilisée dans la deuxième partie, qui consiste à calculer l’aimantation d’état stationnaire en fonction des angles de précession dans le plan transverse, puis à intégrer sur les isochromats pour obtenir le signal résultant d’un voxel (volume element). Le problème est aussi envisagé sous l’angle des « trajectoires de cohérence », c’est-à-dire la subdivision du signal en composantes plus ou moins déphasées, par l’effet combiné des impulsions RF, des gradients appliqués et des inhomogénéités du champ magnétique principal. Cette approche permet d’interpréter l’intensité du signal dans les séquences à écho déplacé comme le résultat d’interférences destructives entre diverses composantes physiquement interprétables. Elle permet de comprendre comment la variation de la phase de l’impulsion d’excitation (RF-spoiling) élimine ces interférences. Des expériences in vitro montrent un accord quantitatif excellent avec les calculs théoriques, et la faisabilité de la méthode in vivo est établie. Il n’est pas encore possible de conclure quant à l’applicabilité de la nouvelle méthode dans le cadre de l’IRMf, mais l’approche théorique proposée a en tout cas permis de revoir en profondeur les mécanismes de formation du signal pour l’ensemble des méthodes à écho déplacé, puisque le cas de l’écho de gradient s’avère complètement similaire au cas de l’écho de spin.

La thèse évolue donc progressivement de la modélisation de l’effet BOLD vers la conception de séquences, permettant ainsi d’aborder deux aspects fondamentaux de la physique de l’IRMf.

Doctorat en sciences appliquées
info:eu-repo/semantics/nonPublished

34

Kwan, Remi K.-S. "An extensible MRI simulator for quantitative evaluation of image-processing and classification methods." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape11/PQDD_0003/MQ44019.pdf.

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Kwan, Remi K. S. "An extensible MRI simulator for quantitative evaluation of image-processing and classification methods /." Thesis, McGill University, 1997. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=20210.

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The increased use of computer-aided image analysis techniques, in medical imaging has lead to a greater need for objective and quantitative methods of evaluating these techniques. However, validation of in vivo Magnetic Resonance Imaging (MRI) studies is complicated by a lack of 'absolute truth' or reference data.
This thesis describes an extensible MRI simulator that efficiently generates realistic 3D images of human brain anatomy, using a 3D brain phantom with known anatomical structures and tissue properties. An object-oriented design is presented that allows simulator models to be adapted to specific studies. Models for discrete-event Bloch equation simulation of Nuclear Magnetic Resonance (NMR) signal production, scan parameter dependent noise, and partial volume are given.
The simulator is validated using doped-gelatin phantom experiments, and its application to common medical imaging tasks, such as, image registration, classification, nonuniform intensity correction, and fMRI analysis is shown.

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Morgado, Correia Marta. "Development of methods for the acquisition and analysis of diffusion weighted MRI data." Thesis, University of Cambridge, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.611789.

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Warnert, Esther. "Development and application of perfusion MRI methods : innovating the measurement of cerebrovascular physiology." Thesis, Cardiff University, 2014. http://orca.cf.ac.uk/70026/.

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A healthy cerebrovasculature is important in maintaining constant delivery of blood carrying oxygen and nutrients required for normal cerebral function. There is a considerable body of research into the regulation of the cerebrovasculature and cerebral blood flow, which has improved diagnosis, treatment and prevention of cerebrovascular pathology. However, a severe confound in research concerning the cerebrovasculature is the limited amount of in vivo human data available that assesses function and physiology of key cerebral structures: the brainstem and the cerebral arteries themselves. The work presented in this thesis aims to provide non-invasive imaging tools that can be used to further the understanding of cerebrovascular function and physiology. Herein, innovations of arterial spin labelling, an established magnetic resonance imaging technique to measure perfusion, are presented that address the challenges of measuring brainstem cerebrovascular physiology and facilitate a novel measurement of cerebral arterial compliance, a marker of cerebrovascular health. Regulation of the cerebrovasculature by the sympathetic nervous system is used throughout this thesis as an example process that can be investigated with arterial spin labelling based imaging methods, because the brainstem is an important sympathetic control centre and changes in sympathetic outflow directly affect the cerebral arteries. Firstly, arterial spin labelling is adapted to measure cerebral blood flow in the brainstem. The arterial spin labelling method tailored for the brainstem is then applied in the investigation of cerebral blood flow in the presence of poly-cystic ovary syndrome, a pathology recently associated with elevated sympathetic outflow. Secondly, an arterial spin labelling based approach to measure cerebral arterial compliance is introduced. Lastly, this thesis shows that a combination of the optimised brainstem perfusion imaging method and the novel arterial compliance measurement is able to investigate clinically relevant processes, including cerebral autoregulation and Cushing’s mechanism.

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Veronese, Elisa. "Methods for segmentation and characterization of multiple sclerosis cortical lesions from MRI data." Doctoral thesis, Università degli studi di Padova, 2012. http://hdl.handle.net/11577/3422439.

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This thesis deals with the automatic analysis of magnetic resonance images of the brain, acquired from people affected by multiple sclerosis. In particular, the primary aim of the analysis is to obtain a quantitative measure of the cortical lesion burden due to the specific disease. Besides, we propose a technique for the characterization of the different lesion types, based on their inflammatory activity. Multiple sclerosis is a chronic, inflammatory disease of the central nervous system, that causes a progressive demyelination of several areas of the brain and of the spinal cord. As far as diseases’ frequency is concerned, multiple sclerosis represents the second neurologic disease in the young adult, and it is even the first inflammatory chronic disease. Besides, it can also be considered as a social burden, with heavy direct and indirect costs: multiple sclerosis prevents people from working as much as they could without the disease, and can lead to the impossibility to live autonomously. Last but not least, the cost of treatment and care can be very high. Although the causes are still partly unclear, a lot has been achieved in the understanding of the different phases of the inflammatory process characterizing multiple sclerosis. Today it is possible to early diagnose the disease, thus allowing to limit symptoms by early therapies. The lesions caused by multiple sclerosis can be visualized in vivo thanks to magnetic resonance (MR) imaging. In particular in the latest decades several MR sequences have been designed in order to highlight white matter lesions. When studying gray matter lesions, instead, the available MR sequences are less numerous. This is partly due to the fact that the gray matter involvement in multiple sclerosis is a relatively recent finding. It is important to verify both the evolution and the appearance of new lesions: in this way it is possible to monitor the disease progression, which is particularly tricky in the case of multiple sclerosis. This disease is characterized by acute relapses alternated with remitting periods of variable length. For this reason patients are periodically examined acquiring MR images. The subsequent diagnosis made by the physician is based on the MR results. So, it is fundamental for the neurologist to be well trained in order to be able to properly evaluate different magnetic resonance sequences. Besides, he/she has to pay close attention not only to detect new lesions, but also to recognize those lesions that were already present in the previous examinations, and that might have changed their shape, their dimension or they activity. This process requires time and attention, but unfortunately, being human-based, it is strongly error prone. Unquestionably, the diagnose cannot prescind from the neurologist’s evaluation. Nonetheless, the advent of techniques for the automatic analysis of magnetic resonance images and for the detection of multiple sclerosis lesions would represent a valid support for the physicians, who could provide accurate evaluations in faster timing. In this thesis several MR techniques currently used for cortical lesions visualization will be described. Then a segmentation algorithm will be proposed, in order to find the region corresponding to gray matter. On this region a second algorithm will be presented, that detect multiple sclerosis cortical lesions. Finally, a first attempt to characterize cortical lesions based on their inflammatory activity will be described.
Questa tesi tratta l’analisi automatica di immagini di risonanza magnetica cerebrale in soggetti affetti da sclerosi multipla. In particolare, l’analisi è volta sia a una stima quantitativa del carico di lesioni corticali presenti a causa del decorso della malattia, sia a una caratterizzazione del tipo di lesioni presenti basata sul loro grado di infiammazione. La sclerosi multipla è una malattia infiammatoria a decorso cronico che colpisce il sistema nervoso centrale, provocandone una progressiva distruzione della mielina in più aree. Per frequenza, nel giovane adulto è la seconda malattia neurologica e la prima di tipo infiammatorio cronico. Inoltre, la sclerosi multipla può essere considerata anche come malattia sociale, con un’elevata ricaduta economica, sia diretta che indiretta: la diminuzione o la perdita dell’autonomia porta alla progressiva impossibilità di svolgere una qualsiasi attività lavorativa fino all’incapacità di condurre una vita indipendente. A questo si aggiungano il costo delle cure e dell’assistenza necessarie. Benché le cause siano ancora in parte sconosciute, molto è stato fatto nel chiarire le diverse fasi del processo infiammatorio che caratterizza tale patologia, permettendo così di arrivare a una diagnosi e a un trattamento precoce che consentono di ridurre gli effetti della malattia. Le lesioni causate dalla sclerosi multipla risultano visibili grazie a particolari tecniche di acquisizione di immagini basate sulla risonanza magnetica. In particolare negli ultimi decenni si sono studiate e messe a punto diverse sequenze di risonanza ottimizzate per la visualizzazione delle lesioni in materia bianca. Il quadro delle tecniche a disposizione qualora si vogliano studiare lesioni in materia grigia risulta invece meno completo, soprattutto a causa del fatto che la scoperta di un coinvolgimento della materia grigia nella malattia è molto più recente. La verifica dell’evoluzione e della comparsa di nuove lesioni è importante dal momento che consente di monitorare il progredire di una malattia caratterizzata da fasi acute intervallate a periodi di quiescenza più o meno lunghi. Per questo motivo i soggetti affetti da sclerosi multipla vengono periodicamente sottoposti a esami di risonanza magnetica. Ogni successiva valutazione da parte del medico neurologo dipenderà da quanto evidenziato dalle immagini acquisite. In questo senso è fondamentale che il medico sia ben allenato nella valutazione di immagini di risonanza, e che ponga particolare attenzione non solo nell’individuare la comparsa di nuove lesioni, ma anche nel riconoscere la presenza di lesioni già presenti in esami precedenti, che possono essere progredite nella forma, nelle dimensioni e nel grado di attività. La lettura di un esame di risonanza magnetica richiede tempo e attenzione, ed è inevitabilmente soggetta all’errore umano che caratterizza qualsiasi valutazione manuale. Per questo, benché sia impensabile prescindere dalla valutazione del medico, una tecnica di analisi automatica di immagini di risonanza magnetica cerebrale che sia in grado di evidenziare la presenza di lesioni da sclerosi multipla può rappresentare un valido aiuto alla refertazione, sia in termini di tempo che di accuratezza. In questa tesi si descriveranno le tecniche di risonanza magnetica a disposizione per una miglior visualizzazione delle lesioni corticali. Su queste si procederà alla segmentazione del tessuto di interesse, ossia del volume di materia grigia. In seguito verrà descritta la tecnica proposta per il riconoscimento delle regioni patologiche corticali. Infine sarà descritto un primo tentativo di caratterizzazione delle diverse lesioni corticali, basato sulla valutazione del grado di attività di ciascuna lesione.

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Randtke, Edward Alexander. "Development and Evaluation of Exchange Rate Measurement Methods." Diss., The University of Arizona, 2013. http://hdl.handle.net/10150/314652.

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Exchange rate determination allows precise modeling of chemical systems, and allows one to infer properties relevant to tumor biology such as enzyme activity and pH. Current exchange rate determination methods found via Contrast Enhanced Saturation Transfer agents are not effective for fast exchanging protons and use non-linear models. A comparison of their effectiveness has not been performed. In this thesis, I compare the effectiveness of current exchange rate measurement methods. I also develop exchange rate measurement methods that are effective for fast exchanging CEST agents and use linear models instead of non-linear models. In chapter 1 I review current exchange rate measurement methods. In chapter 2 I compare several of the current methods of exchange rate measurement, along with several techniques we develop. In chapter 3 I linearize the Quantifying Exchange through Saturation Transfer (QUEST) measurement method analogously to the Omega Plot method, and compare its effectiveness to the QUEST method. In chapter 4, I compare the effectiveness of current exchange rate theories (Transition State Theory and Landau-Zener theory) in the moderate coupling regime, and propose our own combined Eyring-Landau-Zener theory for this intermediate regime. In chapter 5 I discuss future directions for method development and experiments involving exchange rate determination.

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Schwaderlapp, Niels Leonard [Verfasser], and J. G. [Akademischer Betreuer] Korvink. "Preclinical MRI in Neurological Diseases - Development of MRI Methods for Non-Invasive Investigation of Experimental Epilepsy / Niels Leonard Schwaderlapp ; Betreuer: J. G. Korvink." Karlsruhe : KIT-Bibliothek, 2020. http://d-nb.info/1205001972/34.

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Dickie, David Alexander. "Methods to assess changes in human brain structure across the lifecourse." Thesis, University of Edinburgh, 2014. http://hdl.handle.net/1842/10027.

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Human brain structure can be measured across the lifecourse (“in vivo”) with magnetic resonance imaging (MRI). MRI data are often used to create “atlases” and statistical models of brain structure across the lifecourse. These methods may define how brain structure changes through life and support diagnoses of increasingly common, yet still fatal, age-related neurodegenerative diseases. As diseases such as Alzheimer’s (AD) cast an ever growing shadow over our ageing population, it is vitally important to robustly define changes which are normal for age and those which are pathological. This work therefore assessed existing MR brain image data, atlases, and statistical models. These assessments led me to propose novel methods for accurately defining the distributions and boundaries of normal ageing and pathological brain structure. A systematic review found that there were fewer than 100 appropriately tested normal subjects aged ≥60 years openly available worldwide. These subjects did not have the range of MRI sequences required to effectively characterise the features of brain ageing. The majority of brain image atlases identified in this review were found to contain data from few or no subjects aged ≥60 years and were in a limited range of MRI sequences. All of these atlases were created with parametric (mean-based) statistics that require the assumptions of equal variance and Gaussian distributions. When these assumptions are not met, mean-based atlases and models may not well represent the distributions and boundaries of brain structure. I tested these assumptions and found that they were not met in whole brain, subregional, and voxel-based models of ~580 subjects from across the lifecourse (0- 90 years). I then implemented novel whole brain, subregional, and voxel-based statistics, e.g. percentile rank atlases and nonparametric effect size estimates. The equivalent parametric statistics led to errors in classification and inflated effects by up to 45% in normal ageing-AD comparisons. I conclude that more MR brain image data, age appropriate atlases, and nonparametric statistical models are needed to define the true limits of normal brain structure. Accurate definition of these limits will ultimately improve diagnoses, treatment, and outcome of neurodegenerative disease.

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Zhao, Nan. "Accelerated T1 and T2 Parameter Mapping and Data Denoising Methods for 3D Quantitative MRI." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1613748540796138.

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Clark, Daniel James. "Chemical Exchange Saturation Transfer and Quantitative MRI Methods: Applications for Osteoarthritis and Cartilage Injury." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1431016691.

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Stecker, Ian. "Optimizing Quantitative Methods in Murine Pulmonary Imaging with UTE 1H MR." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1592135581719325.

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Andersson, Jonathan. "Methods for automatic analysis of glucose uptake in adipose tissue using quantitative PET/MRI data." Thesis, Uppsala universitet, Enheten för radiologi, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-233200.

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Brown adipose tissue (BAT) is the main tissue involved in non-shivering heat production. A greater understanding of BAT could possibly lead to new ways of prevention and treatment of obesity and type 2 diabetes. The increasing prevalence of these conditions and the problems they cause society and individuals make the study of the subject important. An ongoing study performed at the Turku University Hospital uses images acquired using PET/MRI with 18F-FDG as the tracer. Scans are performed on sedentary and athlete subjects during normal room temperature and during cold stimulation. Sedentary subjects then undergo scanning during cold stimulation again after a six weeks long exercise training intervention. This degree project used images from this study. The objective of this degree project was to examine methods to automatically and objectively quantify parameters relevant for activation of BAT in combined PET/MRI data. A secondary goal was to create images showing glucose uptake changes in subjects from images taken at different times. Parameters were quantified in adipose tissue directly without registration (image matching), and for neck scans also after registration. Results for the first three subjects who have completed the study are presented. Larger registration errors were encountered near moving organs and in regions with less information. The creation of images showing changes in glucose uptake seem to be working well for the neck scans, and somewhat well for other sub-volumes. These images can be useful for identification of BAT. Examples of these images are shown in the report.

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Neto, Henriques Rafael. "Advanced methods for diffusion MRI data analysis and their application to the healthy ageing brain." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/281993.

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Diffusion of water molecules in biological tissues depends on several microstructural properties. Therefore, diffusion Magnetic Resonance Imaging (dMRI) is a useful tool to infer and study microstructural brain changes in the context of human development, ageing and neuropathology. In this thesis, the state-of-the-art of advanced dMRI techniques is explored and strategies to overcome or reduce its pitfalls are developed and validated. Firstly, it is shown that PCA denoising and Gibbs artefact suppression algorithms provide an optimal compromise between increased precision of diffusion measures and the loss of tissue's diffusion non-Gaussian information. Secondly, the spatial information provided by the diffusion kurtosis imaging (DKI) technique is explored and used to resolve crossing fibres and generalize diffusion measures to cases not limited to well-aligned white matter fibres. Thirdly, as an alternative to diffusion microstructural modelling techniques such as the neurite orientation dispersion and density imaging (NODDI), it is shown that spherical deconvolution techniques can be used to characterize fibre crossing and dispersion simultaneously. Fourthly, free water volume fraction estimates provided by the free water diffusion tensor imaging (fwDTI) are shown to be useful to detect and remove voxels corrupted by cerebrospinal fluid (CSF) partial volume effects. Finally, dMRI techniques are applied to the diffusion data from the large collaborative Cambridge Centre for Ageing and Neuroscience (CamCAN) study. From these data, the inference provided by diffusion anisotropy measures on maturation and degeneration processes is shown to be biased by age-related changes of fibre organization. Inconsistencies of previous NODDI ageing studies are also revealed to be associated with the different age ranges covered. The CamCAN data is also processed using a novel non-Gaussian diffusion characterization technique which is invariant to different fibre configurations. Results show that this technique can provide indices specific to axonal water fraction which can be linked to age-related fibre density changes.

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Cheng, Wei-Hung. "MRI-Based Images Segmentation for GPU Accelerated Fuzzy Methods on Graphics Processing Units by CUDA." Kent State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=kent154349822159698.

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Berman, Benjamin Paul. "Accelerated Radial Magnetic Resonance Imaging: New Applications and Methods." Diss., The University of Arizona, 2015. http://hdl.handle.net/10150/594390.

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Magnetic resonance imaging is a widely used medical imaging technique, and accelerated data acquisition is critical for clinical utility. In this thesis, new techniques that incorporate radial acquisition, compressed sensing and sparse regularization for improved rapid imaging are presented. Sufficiently accelerated imaging methods can lead to new applications. Here we demonstrate a solution to lung imaging during forced expiration using accelerated MRI. A technique for dynamic 3D imaging of the lungs from highly undersampled data is developed and tested on six subjects. This method takes advantage of image sparsity, both spatially and temporally, including the use of reference frames called bookends. Sparsity, with respect to total variation, and residual from the bookends, enables reconstruction from an extremely limited amount of data. Dynamic 3D images can be captured at an unprecedented sub-150 ms temporal resolution, using only three (or less) acquired radial lines per slice per time point. Lung volume calculations based on image segmentation are compared to those from simultaneously acquired spirometer measurements. Additionally, accelerated imaging methods can be used to improve upon widely used applications; we also present a technique for improved T₂-mapping. A novel model-based compressed sensing method is extended to include a sparse regularization that is learned from the principal component coefficients. The principal components are determined by a range of T₂ decay curves, and the coefficients of the principal components are reconstructed. These coefficient maps share coherent spatial structures, and a spatial patch--based dictionary is a learned for a sparse constraint. This transformation is learned from the coefficients themselves. The proposed reconstruction is suited for non-Cartesian, multi-channel data. The dictionary constraint leads to parameter maps with less noise and less aliasing for high amounts of acceleration.

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Kale, Hikmet Emre. "Segmentation Of Human Facial Muscles On Ct And Mri Data Using Level Set And Bayesian Methods." Master's thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12613352/index.pdf.

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Medical image segmentation is a challenging problem, and is studied widely. In this thesis, the main goal is to develop automatic segmentation techniques of human mimic muscles and to compare them with ground truth data in order to determine the method that provides best segmentation results. The segmentation methods are based on Bayesian with Markov Random Field (MRF) and Level Set (Active Contour) models. Proposed segmentation methods are multi step processes including preprocess, main muscle segmentation step and post process, and are applied on three types of data: Magnetic Resonance Imaging (MRI) data, Computerized Tomography (CT) data and unified data, in which case, information coming from both modalities are utilized. The methods are applied both in three dimensions (3D) and two dimensions (2D) data cases. A simulation data and two patient data are utilized for tests. The patient data results are compared statistically with ground truth data which was labeled by an expert radiologist.

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Kofler, Andreas [Verfasser]. "Deep learning-based methods for image reconstruction in cardiac CT and cardiac cine MRI / Andreas Kofler." Berlin : Medizinische Fakultät Charité - Universitätsmedizin Berlin, 2021. http://d-nb.info/1241541132/34.

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Dissertations / Theses on the topic 'MRI methods'

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