Author: Hamza KebiriPublisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description
Thèse. Biologie. Médecine. 2023
Deep Learning Methods for Diffusion MRI in Early Development of the Human Brain
Author: Hamza KebiriPublisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description
Thèse. Biologie. Médecine. 2023
Advanced analysis of diffusion MRI data
Author: Xuan GuPublisher: Linköping University Electronic Press
ISBN: 9175190036
Category :
Languages : en
Pages : 93
Book Description
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.
Introduction to Diffusion Tensor Imaging
Author: Susumu MoriPublisher: Academic Press
ISBN: 0123984076
Category : Medical
Languages : en
Pages : 141
Book Description
The concepts behind diffusion tensor imaging (DTI) are commonly difficult to grasp, even for magnetic resonance physicists. To make matters worse, a many more complex higher-order methods have been proposed over the last few years to overcome the now well-known deficiencies of DTI. In Introduction to Diffusion Tensor Imaging: And Higher Order Models, these concepts are explained through extensive use of illustrations rather than equations to help readers gain a more intuitive understanding of the inner workings of these techniques. Emphasis is placed on the interpretation of DTI images and tractography results, the design of experiments, and the types of application studies that can be undertaken. Diffusion MRI is a very active field of research, and theories and techniques are constantly evolving. To make sense of this constantly shifting landscape, there is a need for a textbook that explains the concepts behind how these techniques work in a way that is easy and intuitive to understand—Introduction to Diffusion Tensor Imaging fills this gap. Extensive use of illustrations to explain the concepts of diffusion tensor imaging and related methods Easy to understand, even without a background in physics Includes sections on image interpretation, experimental design, and applications Up-to-date information on more recent higher-order models, which are increasingly being used for clinical applications
Microstructure Imaging in the Human Brain with Advanced Diffusion MRI and Machine Learning
Author: Noémi G. GyőriComputational Diffusion MRI
Author: Andrea FusterPublisher: Springer
ISBN: 3319285882
Category : Mathematics
Languages : en
Pages : 236
Book Description
These Proceedings of the 2015 MICCAI Workshop “Computational Diffusion MRI” offer a snapshot of the current state of the art on a broad range of topics within the highly active and growing field of diffusion MRI. The topics vary from fundamental theoretical work on mathematical modeling, to the development and evaluation of robust algorithms, new computational methods applied to diffusion magnetic resonance imaging data, and applications in neuroscientific studies and clinical practice. Over the last decade interest in diffusion MRI has exploded. The technique provides unique insights into the microstructure of living tissue and enables in-vivo connectivity mapping of the brain. Computational techniques are key to the continued success and development of diffusion MRI and to its widespread transfer into clinical practice. New processing methods are essential for addressing issues at each stage of the diffusion MRI pipeline: acquisition, reconstruction, modeling and model fitting, image processing, fiber tracking, connectivity mapping, visualization, group studies and inference. This volume, which includes both careful mathematical derivations and a wealth of rich, full-color visualizations and biologically or clinically relevant results, offers a valuable starting point for anyone interested in learning about computational diffusion MRI and mathematical methods for mapping brain connectivity, as well as new perspectives and insights on current research challenges for those currently working in the field. It will be of interest to researchers and practitioners in the fields of computer science, MR physics, and applied mathematics.
Machine learning methods for human brain imaging
Author: Fatos Tunay Yarman VuralPublisher: Frontiers Media SA
ISBN: 2832519105
Category : Science
Languages : en
Pages : 160
Book Description
Computational Diffusion MRI
Author: Andrea FusterPublisher: Springer
ISBN: 3319541307
Category : Mathematics
Languages : en
Pages : 214
Book Description
This volume offers a valuable starting point for anyone interested in learning computational diffusion MRI and mathematical methods for brain connectivity, while also sharing new perspectives and insights on the latest research challenges for those currently working in the field. Over the last decade, interest in diffusion MRI has virtually exploded. The technique provides unique insights into the microstructure of living tissue and enables in-vivo connectivity mapping of the brain. Computational techniques are key to the continued success and development of diffusion MRI and to its widespread transfer into the clinic, while new processing methods are essential to addressing issues at each stage of the diffusion MRI pipeline: acquisition, reconstruction, modeling and model fitting, image processing, fiber tracking, connectivity mapping, visualization, group studies and inference. These papers from the 2016 MICCAI Workshop “Computational Diffusion MRI” – which was intended to provide a snapshot of the latest developments within the highly active and growing field of diffusion MR – cover a wide range of topics, from fundamental theoretical work on mathematical modeling, to the development and evaluation of robust algorithms and applications in neuroscientific studies and clinical practice. The contributions include rigorous mathematical derivations, a wealth of rich, full-color visualizations, and biologically or clinically relevant results. As such, they will be of interest to researchers and practitioners in the fields of computer science, MR physics, and applied mathematics.
Advancing White Matter Tractometry of the Brain Using Diffusion MRI and Machine Learning
Author: Bramsh Qamar ChandioPublisher:
ISBN:
Category : Brain
Languages : en
Pages : 0
Book Description
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.
Diffusion MRI
Author: Derek K JonesPublisher: Oxford University Press
ISBN: 0199708703
Category : Science
Languages : en
Pages : 784
Book Description
Professor Derek Jones, a world authority on diffusion MRI, has assembled most of the world's leading scientists and clinicians developing and applying diffusion MRI to produce an authorship list that reads like a "Who's Who" of the field and an essential resource for those working with diffusion MRI. Destined to be a modern classic, this definitive and richly illustrated work covers all aspects of diffusion MRI from basic theory to clinical application. Oxford Clinical Neuroscience is a comprehensive, cross-searchable collection of resources offering quick and easy access to eleven of Oxford University Press's prestigious neuroscience texts. Joining Oxford Medicine Online these resources offer students, specialists and clinical researchers the best quality content in an easy-to-access format.
Novel Deep Learning Methods for Brain Image Analysis
Author: Tao ZengPublisher:
ISBN:
Category :
Languages : en
Pages : 170
Book Description
Recent development of novel imaging technologies has led to the acquisition of massive amounts of high-dimensional brain images at various brain scales. Yet, many image analysis tasks mainly rely on human labor to obtain reliable results, making reliable and automated image analysis methods highly demanded.