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In Vivo Diffusion Magnetic Resonance Imaging Of The White Matter Microstructure From Dictionaries Generated By Monte Carlo Simulations
Download In Vivo Diffusion Magnetic Resonance Imaging Of The White Matter Microstructure From Dictionaries Generated By Monte Carlo Simulations full books in PDF, epub, and Kindle. Read online In Vivo Diffusion Magnetic Resonance Imaging Of The White Matter Microstructure From Dictionaries Generated By Monte Carlo Simulations ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
Book Synopsis In Vivo Diffusion Magnetic Resonance Imaging of the White Matter Microstructure from Dictionaries Generated by Monte Carlo Simulations by : Gaëtan Olivier D. Rensonnet
Download or read book In Vivo Diffusion Magnetic Resonance Imaging of the White Matter Microstructure from Dictionaries Generated by Monte Carlo Simulations written by Gaëtan Olivier D. Rensonnet and published by . This book was released on 2019 with total page 143 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mots-clés de l'auteur: diffusion-weighted magnetic resonance imaging ; white matter ; brain ; microstructure ; Monte Carlo simulations ; fingerprinting ; dictionaries.
Book Synopsis Modeling and Simulation of the Diffusion MRI Signal from Human Brain White Matter to Decode Its Microstructure and Produce an Anatomic Atlas at High Fields (3T) by : Kévin Ginsburger
Download or read book Modeling and Simulation of the Diffusion MRI Signal from Human Brain White Matter to Decode Its Microstructure and Produce an Anatomic Atlas at High Fields (3T) written by Kévin Ginsburger and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Diffusion Magnetic Resonance Imaging of water in the brain has proven very useful to establish a cartography of brain connections. It is the only in vivo modality to study anatomical connectivity. A few years ago, it has been shown that diffusion MRI is also a unique tool to perform virtual biopsy of cerebral tissues. However, most of current analytical models (AxCaliber, ActiveAx, CHARMED) employed for the estimation of white matter microstructure rely upon a basic modeling of white matter, with axons represented by simple cylinders and extra-axonal diffusion assumed to be Gaussian. First, a more physically plausible analytical model of the human brain white matter accounting for the time-dependence of the diffusion process in the extra-axonal space was developed for Oscillating Gradient Spin Echo (OGSE) sequence signals. A decoding tool enabling to solve the inverse problem of estimating the parameters of the white matter microstructure from the OGSE-weighted diffusion MRI signal was designed using a robust optimization scheme for parameter estimation. Second, a Big Data approach was designed to further improve the brain microstructure decoding. All the simulation tools necessary to construct computational models of brain tissues were developed in the frame of this thesis. An algorithm creating realistic white matter tissue numerical phantoms based on a spherical meshing of cell shapes was designed, enabling to generate a massive amount of virtual voxels in a computationally efficient way thanks to a GPU-based implementation. An ultra-fast simulation tool of the water molecules diffusion process in those virtual voxels was designed, enabling to generate synthetic diffusion MRI signal for each virtual voxel. A dictionary of virtual voxels containing a huge set of geometrical configurations present in white matter was built. This dictionary contained virtual voxels with varying degrees of axonal beading, a swelling of the axonal membrane which occurs after strokes and other pathologies. The set of synthetic signals and associated geometrical configurations of the corresponding voxels was used as a training data set for a machine learning algorithm designed to decode white matter microstructure from the diffusion MRI signal and estimate the degree of axonal beading. This decoder showed encouraging regression results on unknown simulated data, showing the potential of the presented approach to characterize the microstructure of healthy and injured brain tissues in vivo. The microstructure decoding tools developed during this thesis will in particular be used to characterize white matter tissue microstructural parameters (axonal density, mean axonal diameter, glial density, mean glial cells diameter, microvascular density ) in short and long bundles. The simulation tools developed in the frame of this thesis will enable the construction of a probabilistic atlas of the white matter bundles microstructural parameters, using a mean propagator based diffeomorphic registration tool also designed in the frame of this thesis to register each individual.
Book Synopsis Monte Carlo Simulation of Diffusion Magnetic Resonance Imaging by : Ming Miao
Download or read book Monte Carlo Simulation of Diffusion Magnetic Resonance Imaging written by Ming Miao and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The goal of this thesis is to describe, implement and analyse Monte Carlo (MC) algorithms for simulating the mechanism of diffusion magnetic resonance imaging (dMRI). As the inverse problem of mapping the sub-voxel micro-structure remains challenging, MC methods provide an important numerical approach for creating ground-truth data. The main idea of such simulations is first generating a large sample of independent random trajectories in a prescribed geometry and then synthesizing the imaging signals according to given imaging sequences. The thesis starts by providing a concise introduction of the mathematical background for understanding dMRI. It then proceeds to describe the workflow and implementation of the most basic Monte Carlo method with experiments performed on simple geometries. A theoretical framework for error analysis is introduced, which to the best of the author's knowledge, has been absent in the literature. In an effort to mitigate the costly nature of MC algorithms, the geometrically adaptive fast random walk algorithm (GAFRW) is implemented, first invented by D.Grebenkov. Additional mathematical justification is provided in the appendix should the reader find details in the original paper by Grebenkov lacking. The result suggests that the GAFRW algorithm only provides moderate accuracy improvement over the crude MC method in the geometry modeled after white matter fibers. Overall, both approaches are shown to be flexible for a variety of geometries and pulse sequences.
Book Synopsis Advanced analysis of diffusion MRI data by : Xuan Gu
Download or read book Advanced analysis of diffusion MRI data written by Xuan Gu and published by Linköping University Electronic Press. This book was released on 2019-11-19 with total page 93 pages. Available in PDF, EPUB and Kindle. Book excerpt: Diffusion magnetic resonance imaging (diffusion MRI) is a non-invasive imaging modality which can measure diffusion of water molecules, by making the MRI acquisition sensitive to diffusion. Diffusion MRI provides unique possibilities to study structural connectivity of the human brain, e.g. how the white matter connects different parts of the brain. Diffusion MRI enables a range of tools that permit qualitative and quantitative assessments of many neurological disorders, such as stroke and Parkinson. This thesis introduces novel methods for diffusion MRI data analysis. Prior to estimating a diffusion model in each location (voxel) of the brain, the diffusion data needs to be preprocessed to correct for geometric distortions and head motion. A deep learning approach to synthesize diffusion scalar maps from a T1-weighted MR image is proposed, and it is shown that the distortion-free synthesized images can be used for distortion correction. An evaluation, involving both simulated data and real data, of six methods for susceptibility distortion correction is also presented in this thesis. A common problem in diffusion MRI is to estimate the uncertainty of a diffusion model. An empirical evaluation of tractography, a technique that permits reconstruction of white matter pathways in the human brain, is presented in this thesis. The evaluation is based on analyzing 32 diffusion datasets from a single healthy subject, to study how reliable tractography is. In most cases only a single dataset is available for each subject. This thesis presents methods based on frequentistic (bootstrap) as well as Bayesian inference, which can provide uncertainty estimates when only a single dataset is available. These uncertainty measures can then, for example, be used in a group analysis to downweight subjects with a higher uncertainty.
Book Synopsis In Vivo Diffusion Magnetic Resonance Imaging to Evaluate Microstructure of the Human Brain by : 李鴻禧
Download or read book In Vivo Diffusion Magnetic Resonance Imaging to Evaluate Microstructure of the Human Brain written by 李鴻禧 and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis A Spatio-temporal Model for White Matter Tractography in Diffusion Tensor Imaging by : Juna Goo
Download or read book A Spatio-temporal Model for White Matter Tractography in Diffusion Tensor Imaging written by Juna Goo and published by . This book was released on 2020 with total page 118 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation focuses on the theoretical and applied aspects of a spatio-temporal modeling for the reconstruction of in-vivo fiber tracts in white matter when a single brain is scanned with magnetic resonance imaging (MRI) on several occasions. The objective of this research is twofold: one is how to estimate the spatial trajectory of a nerve fiber bundle at a given time point in the presence of measurement noise and the other is how to incorporate a progressive deterioration of brain connectivity into a hypothesis test. This dissertation leverages the spatio-temporal behavior of water diffusion in a region of the brain where the estimation of fiber trajectories is made from smoothing the time-varying diffusion tensor field via the Nadaraya-Watson type kernel regression estimator to its eigenvector field. The estimated fiber pathway takes the form of confidence ellipsoids given the estimates of mean and covariance functions. Furthermore, this dissertation proposes a hypothesis test in which the null hypothesis states that true fiber trajectories remain the same over a certain time interval. This null hypothesis indicates no substantial pathological changes of fiber pathways in that region of the brain during the observed time period. The proposed test statistic is shown to follow the limiting chi-square distribution under the null hypothesis. The power of the test is illustrated via Monte Carlo simulations. Lastly, this dissertation demonstrates the test can also be applied to a real longitudinal DTI study of a single brain repeatedly measured across time.
Book Synopsis Advancing White Matter Tractometry of the Brain Using Diffusion MRI and Machine Learning by : Bramsh Qamar Chandio
Download or read book Advancing White Matter Tractometry of the Brain Using Diffusion MRI and Machine Learning written by Bramsh Qamar Chandio and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The human brain contains billions of axons that bundle together in tracts and fasciculi. These can be reconstructed in vivo by collecting diffusion MRI data and deploying tractography algorithms. The outputs of tractography algorithms are called tractograms. These tractograms are represented digitally using streamlines, which are representations of 3D curves traversing the brain. Diffusion MRI and tractography provide crucial information about brain connectivity and microstructural changes due to underlying conditions such as Alzheimer's, Parkinson's, and Schizophrenia disease. However, often generated whole-brain tractograms have millions of streamlines with many false positives and anatomically implausible streamlines. Therefore, tractograms require novel processing pipelines that can reduce such issues and provide anatomically relevant outcomes. For example, a) bundle segmentation methods extract anatomically relevant streamlines and white matter tracts/bundles from the whole-brain tractograms. b) bundle registration methods are used to create common spaces across subjects, and c) statistical methods can then be applied to study microstructural changes in groups and populations along the length of the bundles. This process of quantifying microstructural changes due to a disease or condition along the length of the digitally reconstructed white matter tracts is called tractometry.In this dissertation, we introduced new methods to advance tractometry using machine learning and functional data analysis approaches. For the problem of bundle segmentation and streamline filtering, we introduced the auto-calibrated RecoBundles method that precisely extracts bundles from tractograms with only one reference exemplar. We also developed an unsupervised method, FiberNeat, that filters out spurious streamlines from bundles in latent space. To solve the registration problem, a novel method, BundleWarp, was created for the nonlinear registration of white matter bundles where users can control the amount of deformations with a single free regularization parameter (Lambda). In the category of tractometry methods, we created a publicly available advanced tractometry pipeline called BUndle ANalytics (BUAN). BUAN provides a completely automatic, end-to-end streamline-based solution that connects bundle segmentation, registration, analysis of bundle anatomy, and bundle shape analysis. BUAN reports the exact locations of population differences along the length of the tracts. BUAN also includes metrics and methods for quality assurance of extracted white matter tracts in large populations. Furthermore, in BUAN 2.0, instead of treating points on the streamlines as independent observations in statistical analysis, we proposed using functional data analysis (FDA) methods where each streamline is considered a function. This dissertation moves beyond the standard processing of brain images to a tractography-based analysis of the brain tissue microstructure and connectivity by introducing robust, fast, and simple-to-use algorithms. Results are shown on Parkinson's disease data from Parkinson's Progression Markers Initiative (PPMI) and Alzheimer's disease from Alzheimer's Disease Neuroimaging Initiative phase 3 (ADNI3) datasets. The methods developed as part of this dissertation are made publicly available through DIPY.org.
Book Synopsis Quantification of White Matter Pathologies by a Novel Diffusion MRI Technique by : Chunyu Song
Download or read book Quantification of White Matter Pathologies by a Novel Diffusion MRI Technique written by Chunyu Song and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The diffusion of water molecules in biological tissues in vivo or ex vivo is not free, impeded by the presence of obstacles of the environment, e.g., macromolecules, fibers and membranes. Diffusion magnetic resonance imaging (MRI) leverages the effect of water diffusion pattern through diffusion-weighting. It has gained wide application in imaging brain white matter tracts owning to the highly anisotropic diffusion of water molecules in these structures. Diffusion MRI detects white matter tract abnormalities in the absence of T2W lesions. Thus, it has been widely employed to assess disease progression of multiple sclerosis (MS). Current diffusion MRI images water signals from both intra- and extra-axonal compartments representing a weighted average of the diffusion effects of the two compartments. Thus, current diffusion MRI could not differentiate complexity of diffusion MRI signals in the presence of vasogenic edema, cell infiltration, and nerve damages. Therefore, to accurately reflect development of MS, it is necessary to develop a noninvasive neuroimaging method to detect nerve injury without the confounding effects from the surrounding extra-fiber pathologies.In this thesis work, we propose a new diffusion MRI model improving diffusion basis spectrum imaging (DBSI) by inclusion of an intra-axonal compartment (DBSI-IA) to eliminate the overwhelming impacts of extra-axonal compartment in the presence of inflammation and tissue loss. DBSI-IA maintains the advantage of DBSI in resolving crossing fibers using low-b-value diffusion weighting while quantifying diffusivities of intra-/extra-axonal compartment water. Thus, DBSI-IA may be used to quantify axonal injury (via intra-axonal axial diffusivity), axon loss (via intra-axonal volume), demyelination (via extra-axonal radial diffusivity), edema and inflammation (via isotropic diffusion spectrum) with high precision. Through the multiple-tensor modelling of diffusion MRI signals, DBSI-IA has shown the potential to detect axonal injury in MS missed by conventional diffusion tensor imaging (DTI) and DBSI.We first examined the validity of DBSI-IA using Monte-Carlo simulation (Chapter 4), observing the impact of extra-axonal water diffusion on DBSI derived axonal injury metrics including axial diffusivity and fiber fraction. DBSI-IA derived intra-axonal diffusivity and intra-axonal volume fraction is immune to the effect of extra-axonal water compartment. To further validate the suitability of DBSI-IA for analyzing diffusion MRI data, we applied this new model to analyze a data set from previously published in vivo DBSI of optic nerves from EAE mice (Chapter 5). We found that DBSI-IA derived pathological metrics closely correlated with immunohistochemistry identified optic nerve pathologies. DBSI-IA was then applied to previously published DBSI data from MS patients (Chapter 6). We successfully assessed normal appearing corpus callosum axonal injury in MS patients that have been missed by both DTI and DBSI.In conclusion, DBSI-IA derived axonal integrity metrics, such as the intra-axonal fiber fraction and intra-axonal axial diffusivities (AD), can accurately reflect axonal injury missed by DTI or DBSI. DBSI-IA retains the sensitivity of DBSI to demyelination and cell infiltration in MS. The application of DBSI-IA provided a new perspective in developing a more effective diffusion MRI model by separating the extra- and intra-axonal water compartments.
Book Synopsis Quantitative White Matter Metrics by : Luis Lacerda
Download or read book Quantitative White Matter Metrics written by Luis Lacerda and published by . This book was released on 2016 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Diffusion imaging is a non-invasive imaging method which has been successfully applied to study white matter. Most clinical approaches, based on Diffusion Tensor Imaging (DTI), are limited by the simple model of the underlying tissue imposed, failing to reconstruct the diffusion propagator, which fully encodes the displacement of water molecules. To do so, more comprehensive sampling schemes such as Diffusion Spectrum Imaging (OSI) have been developed. In this thesis, I have investigated the effect of different tissue configurations, sampling and processing steps m the performance of OSI. I identified specific configurations where OSI is unable to characterise diffusion without artefacts, namely aliasing caused by fast diffusion components. Furthermore, processing of the diffusion orientation distribution function (ODF) in these environments can lead to generation of spurious fibres in tractography reconstructions. 'To overcome this, I have applied a novel step in the processing pipeline of OSI, namely a different way of computing the ODF, which consists of restricting the range of integration to probabilities based on the physical displacement of "axonlike" diffusivities. Alternatively, it is possible to use a mathematical representation of the acquired signal, of which the Simple Harmonic Oscillator based Reconstruction and Estimation (SHORE) and Mean Apparent Propagator Magnetic Resonance Imaging (MAP-MRI) are examples. I have here used these methods and further provided optimised acquisitions based on standard propagator metrics. Finally, I have introduced new metrics that use microstructural information available at the different displacement scales, and can facilitate exploration of brain organisation even when no a-priori biophysical model is available.
Book Synopsis Brain Microstructure Mapping Using Quantitative and Diffsusion MRI by : Alice Lebois
Download or read book Brain Microstructure Mapping Using Quantitative and Diffsusion MRI written by Alice Lebois and published by . This book was released on 2014 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis is focused on the human brain microstructure mapping using quantitative and diffusion MRI. The T1/T2 quantitative imaging relies on sequences dedicated to the mapping of T1 and T2 relaxation times. Their variations within the tissue are linked to the presence of different water compartments defined by a specific organization of the tissue at the cell scale. Measuring these parameters can help, therefore, to better characterize the brain microstructure. The dMRI, on the other hand, explores the brownian motion of water molecules in the brain tissue, where the water molecules' movement is constrained by natural barriers, such as cell membranes. Thus, the information on their displacement carried by the dMRI signal gives access to the underlying cytoarchitecture. Combination of these two modalities is, therefore, a promising way to probe the brain tissue microstructure. The main goal of the present thesis is to set up the methodology to study the microstructure of the white matter of the human brain in vivo. The first part includes the acquisition of a unique MRI database of 79 healthy subjects (the Archi/CONNECT), which includes anatomical high resolution data, relaxometry data, diffusion-weighted data at high spatio-angular resolution and functional data. This database has allowed us to build the first atlas of the anatomical connectivity of the healthy brain through the automatic segmentation of the major white matter bundles, providing an appropriate anatomical reference for the white matter to study individually the quantitative parameters along each fascicle, characterizing its microstructure organization. Emphasis was placed on the construction of the first atlas of the T1/T2 profiles along the major white matter pathways. The profiles of the T1 and T2 relaxation times were then correlated to the quantitative profiles computed from the diffusion MRI data (fractional anisotropy, radial and longitudinal diffusivities, apparent diffusion coefficient), in order to better understand their relations and to explain the observed variability along the fascicles and the interhemispheric asymmetries. The second part was focused on the brain tissue modeling at the cell scale to extract the quantitative parameters characterizing the geometry of the cellular membranes, such as the axonal diameter and the axonal density. A diffusion MRI sequence was developed on the 3 Teslas and 7 Teslas Siemens clinical systems of NeuroSpin which is able to apply any kind of gradient waveforms to fall within an approach where the gradient waveform results from an optimization under the hypothesis of a geometrical tissue model, hardware and time constraints induced by clinical applications. This sequence was applied in the study of fourteen healthy subjects in order to build the first quantitative atlas of the axonal diameter and the local axonal density at 7T. We also proposed a new geometrical model to model the axon, dividing the axonal compartment, usually modelled using a simple cylinder, into two compartments: one being near the membranes with low diffusivity and one farer from the membranes, less restricted and with higher diffusivity. We conducted a theoretical study showing that under clinical conditions, this new model allows, in part, to overcome the bias induced by the simple cylindrical model leading to a systematic overestimation of the smallest diameters. Finally, in the aim of going further in the physiopathology of the autism, we added to the current 3T imaging protocol the dMRI sequence developed in the framework of this thesis in order to map the axonal diameter and density. This study is ongoing and should validate shortly the contribution of these new quantitative measures of the microstructure in the comprehension of the atrophies of the corpus callosum, initially observed using less specific diffusion parameters such as the generalized fractional anisotropy. There will be other clinical applications in the future.
Book Synopsis Information Processing in Medical Imaging by : Albert C. S. Chung
Download or read book Information Processing in Medical Imaging written by Albert C. S. Chung and published by Springer. This book was released on 2019-05-22 with total page 884 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 26th International Conference on Information Processing in Medical Imaging, IPMI 2019, held at the Hong Kong University of Science and Technology, Hong Kong, China, in June 2019. The 69 full papers presented in this volume were carefully reviewed and selected from 229 submissions. They were organized in topical sections on deep learning and segmentation; classification and inference; reconstruction; disease modeling; shape, registration; learning motion; functional imaging; and white matter imaging. The book also includes a number of post papers.
Book Synopsis Geometric Models of Brain White Matter for Microstructure Imaging with Diffusion MRI. by : E. Panagiotaki
Download or read book Geometric Models of Brain White Matter for Microstructure Imaging with Diffusion MRI. written by E. Panagiotaki and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The research presented in this thesis models the diffusion-weighted MRI signal within brain white matter tissue. We are interested in deriving descriptive microstructure indices such as white matter axon diameter and density from the observed diffusion MRI signal. The motivation is to obtain non-invasive reliable biomarkers for early diagnosis and prognosis of brain development and disease. We use both analytic and numerical models to investigate which properties of the tissue and aspects of the diffusion process affect the diffusion signal we measure. First we develop a numerical method to approximate the tissue structure as closely as possible. We construct three-dimensional meshes, from a stack of confocal microscopy images using the marching cubes algorithm. The experiment demonstrates the technique using a biological phantom (asparagus). We devise an MRI protocol to acquire data from the sample. We use the mesh models as substrates in Monte-Carlo simulations to generate synthetic MRI measurements. To test the feasibility of the method we compare simulated measurements from the three-dimensional mesh with scanner measurements from the same sample and simulated measurements from an extruded mesh and much simpler parametric models. The results show that the three-dimensional mesh model matches the data better than the extruded mesh and the parametric models revealing the sensitivity of the diffusion signal to the microstructure. The second study constructs a taxonomy of analytic multi-compartment models of white matter by combining intra- and extra-axonal compartments from simple models. We devise an imaging protocol that allows diffusion sensitisation parallel and perpendicular to tissue fibres. We use the protocol to acquire data from two fixed rat brains, which allows us to fit, study and evaluate the models. We conclude that models which incorporate non-zero axon radius describe the measurements most accurately. The key observation is a departure of signals in the parallel direction from the two-compartment models, suggesting restriction, most likely from glial cells or binding of water molecules to the membranes. The addition of the third compartment can capture this departure and explain the data. The final study investigates the estimates using in vivo brain diffusion measurements. We adjust the imaging protocol to allow an in vivo MRI acquisition of a rat brain and compare and assess the taxonomy of models. We then select the models that best explain the in vivo data and compare the estimates with those from the ex vivo measurements to identify any discrepancies. The results support the addition of the third compartment model as per the ex vivo findings, however the ranking of the models favours the zero radius intra-axonal compartments.
Book Synopsis Automated in Vivo Dissection of White Matter Structures from Diffusion Magnetic Resonance Imaging by : Demian Wassermann
Download or read book Automated in Vivo Dissection of White Matter Structures from Diffusion Magnetic Resonance Imaging written by Demian Wassermann and published by . This book was released on 2010 with total page 259 pages. Available in PDF, EPUB and Kindle. Book excerpt: The brain is organized in networks that are made up of tracks connecting different regions. These networks are important for the development of brain functions such as language. Lesions and cognitive disorders are sometimes better explained by disconnection mechanisms between cerebral regions than by damage of those regions. Despite several decades of tracing these networks in the brain, our knowledge of cerebral connections has progressed very little since the beginning of the last century. Recently, we have seen a spectacular development of magnetic resonance imaging (MRI) techniques for the study of the living human brain. One technique for exploring white matter (WM) tissue characteristics and pathway in vivo is diffusion MRI (dMRI). Particulary, dMRI tractography facilitates the tracing the WM tracts in vivo. dMRI is a promising technique to explore the anatomical basis of human cognition and its disorders. The motivation of this thesis is the in vivo dissection of the WM. This procedure isolates the WM tracts that play a role in a particular function or disorder of the brain so they can be analysed. Manually performing this task requires a great knowledge of brain anatomy and several hours of work. Hence, the development of a technique to automatically perform the identification of WM structures is of utmost importance. This thesis has several contributions : we develop means for the automatic dissection of WM tracts from dMRI, this is based on a mathematical framework for the WM and its tracts ; using these tools, we develop techniques to analyse the spinal chord and to find group differences in the WM particulary between healthy and schizophrenic subjects.
Book Synopsis In Vivo Quantification of Complex Neurite Configurations Using Magnetic Resonance Imaging by : Maira Tariq
Download or read book In Vivo Quantification of Complex Neurite Configurations Using Magnetic Resonance Imaging written by Maira Tariq and published by . This book was released on 2018 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Diffusion MRI for Well-posed and Optimal White Matter Microstructure Characterisation by : Santiago Coelho
Download or read book Diffusion MRI for Well-posed and Optimal White Matter Microstructure Characterisation written by Santiago Coelho and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Advanced Diffusion Encoding Methods in MRI by : Daniel Topgaard
Download or read book Advanced Diffusion Encoding Methods in MRI written by Daniel Topgaard and published by Royal Society of Chemistry. This book was released on 2020-08-17 with total page 455 pages. Available in PDF, EPUB and Kindle. Book excerpt: The medical MRI community is by far the largest user of diffusion NMR techniques and this book captures the current surge of methods and provides a primary source to aid adoption in this field. There is a trend to adapting the more advanced diffusion encoding sequences developed by NMR researchers within the fields of porous media, chemical engineering, and colloid science to medical research. Recently published papers indicate great potential for improved diagnosis of the numerous pathological conditions associated with changes of tissue microstructure that are invisible to conventional diffusion MRI. This book disseminates these recent developments to the wider community of MRI researchers and clinicians. The chapters cover the theoretical basis, hardware and pulse sequences, data analysis and validation, and recent applications aimed at promoting further growth in the field. This is a fast moving field and chapters are written by key MRI scientists that have contributed to the successful translation of the advanced diffusion NMR methods to the context of medical MRI, from global locations.
Book Synopsis Multi-parametric Quantification of White Matter Microstructure in the Human Brain by : Sonya Bells
Download or read book Multi-parametric Quantification of White Matter Microstructure in the Human Brain written by Sonya Bells and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: To date the majority of MRI studies of white matter (WM) microstructure have used diffusion tensor MRI (DT-MRI), comparing groups on a voxel-by-voxel basis. There are limitations to this approach. Firstly, the analysis approach treats each voxel independently, ignoring the fact that adjacent voxels may come from the same tract (or may come from completely separate tracts). Secondly, DT-MRI is sensitive to both interesting properties of WM (e.g., myelination, axon density), and less interesting properties (e.g., intra-voxel orientational dispersion). In contrast, other imaging approaches, based on different contrast mechanisms, can provide increased specificity and therefore sensitivity to differences in one particular attribute of tissue microstructure (e.g., myelin content or axonal density). Both quantitative magnetization transfer (qMT) imaging and multicomponent relaxometry provide proxy estimates of myelin content while the combined hindered and restricted model of diffusion (CHARMED) provides a proxy estimate of axon density. We present a novel imaging method called tractometry, which permits simultaneous quantitative assessment of these different microstructural attributes along specific pathways. Crucially, the metrics were only weakly correlated, suggesting that tractometry provides complementary WM microstructural information to DT-MRI. In developing the tractometry pipeline, we also performed a detailed examination of the qMT pipeline, identifying and reducing sources of variance to provide optimized results. We also identify a number of issues with the current state-of-the art, including the stability of tract based spatial statistics (TBSS). We show that conducting a structure-function correlation TBSS study may lead to vastly different conclusions, based simply on the participants recruited into the study. We also address microstructural asymmetry, including the degree of partial-volume effects (PVEs) from free water, which impact on WM metrics. The observed spatial heterogeneity of PVEs can potentially confound interpretation in studies where contralateral hemispheres are used as internal controls, and could either exacerbate or possibly negate tissue differences.
