Author: Noémi G. GyőriPublisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description
Microstructure Imaging in the Human Brain with Advanced Diffusion MRI and Machine Learning
Author: Noémi G. GyőriAdvanced 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.
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.
Brain Network Analysis
Author: Moo K. ChungPublisher: Cambridge University Press
ISBN: 110718486X
Category : Computers
Languages : en
Pages : 343
Book Description
This coherent mathematical and statistical approach aimed at graduate students incorporates regression and topology as well as graph theory.
Advanced Diffusion MRI for Microstructure Imaging
Author: A. SavickasIntroduction 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
Artificial Intelligence in Diffusion MRI
Author: Mohammad ShehabPublisher: Springer Nature
ISBN: 3030360830
Category : Technology & Engineering
Languages : en
Pages : 170
Book Description
This book focuses on the use of artificial intelligence to address a specific problem in the brain – the orientation distribution function. It discusses three aspects: (i) Preparing, enhancing and evaluating one of the cuckoo search algorithms (CSA); (ii) Describing the problem: Diffusion-weighted magnetic resonance imaging (DW-MRI) is used for non-invasive investigations of anatomical connectivity in the human brain, while Q-ball imaging (QBI) is a diffusion MRI reconstruction technique based on the orientation distribution function (ODF), which detects the dominant fiber orientations; however, ODF lacks local estimation accuracy along the path. (iii) Evaluating the performance of the CSA versions in solving the ODF problem using synthetic and real-world data. This book appeals to both postgraduates and researchers who are interested in the fields of medicine and computer science.
Author: Maira Tariq
Author: 李鴻禧
Author: F. Grussu