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Demian Wassermann - One of the best experts on this subject based on the ideXlab platform.

  • Diffusion MRI simulation of realistic neurons with SpinDoctor and the Neuron Module.
    NeuroImage, 2020
    Co-Authors: Chengran Fang, Van-dang Nguyen, Demian Wassermann
    Abstract:

    Abstract The Diffusion MRI signal arising from neurons can be numerically simulated by solving the Bloch-Torrey partial differential equation. In this paper we present the Neuron Module that we implemented within the Matlab-based Diffusion MRI simulation toolbox SpinDoctor. SpinDoctor uses finite element discretization and adaptive time integration to solve the Bloch-Torrey partial differential equation for general Diffusion-encoding sequences, at multiple b-values and in multiple Diffusion directions. In order to facilitate the Diffusion MRI simulation of realistic neurons by the research community, we constructed finite element meshes for a group of 36 pyramidal neurons and a group of 29 spindle neurons whose morphological descriptions were found in the publicly available neuron repository NeuroMorpho.Org. These finite elements meshes range from having 15,163 nodes to 622,553 nodes. We also broke the neurons into the soma and dendrite branches and created finite elements meshes for these cell components. Through the Neuron Module, these neuron and cell components finite element meshes can be seamlessly coupled with the functionalities of SpinDoctor to provide the Diffusion MRI signal attributable to spins inside neurons. We make these meshes and the source code of the Neuron Module available to the public as an open-source package. To illustrate some potential uses of the Neuron Module, we show numerical examples of the simulated Diffusion MRI signals in multiple Diffusion directions from whole neurons as well as from the soma and dendrite branches, and include a comparison of the high b-value behavior between dendrite branches and whole neurons. In addition, we demonstrate that the neuron meshes can be used to perform Monte-Carlo Diffusion MRI simulations as well. We show that at equivalent accuracy, if only one gradient direction needs to be simulated, SpinDoctor is faster than a GPU implementation of Monte-Carlo, but if many gradient directions need to be simulated, there is a break-even point when the GPU implementation of Monte-Carlo becomes faster than SpinDoctor. Furthermore, we numerically compute the eigenfunctions and the eigenvalues of the Bloch-Torrey and the Laplace operators on the neuron geometries using a finite elements discretization, in order to give guidance in the choice of the space and time discretization parameters for both finite elements and Monte-Carlo approaches. Finally, we perform a statistical study on the set of 65 neurons to test some candidate biomakers that can potentially indicate the soma size. This preliminary study exemplifies the possible research that can be conducted using the Neuron Module.

  • Diffusion MRI simulation of realistic neurons with SpinDoctor and the Neuron Module
    arXiv: Neurons and Cognition, 2019
    Co-Authors: Chengran Fang, Van-dang Nguyen, Demian Wassermann
    Abstract:

    In this paper we present the Neuron Module that we implemented within the Matlab-based Diffusion MRI simulation toolbox SpinDoctor. SpinDoctor uses finite element discretization and adaptive time integration to solve the Bloch-Torrey partial differential equation for general Diffusion-encoding sequences, at multiple b-values and in multiple Diffusion directions. In order to facilitate the Diffusion MRI simulation of realistic neurons by the research community, we constructed finite element meshes for a group of 36 pyramidal neurons and a group of 29 spindle neurons whose morphological descriptions were found in the publicly available neuron repository NeuroMorpho. We also broke the neurons into the soma and dendrite branches and created finite elements meshes for these cell components. Through the Neuron Module, these neuron and cell components finite element meshes can be seamlessly coupled with the functionalities of SpinDoctor. To illustrate some potential uses of the Neuron Module, we show numerical examples of the simulated Diffusion MRI signals in multiple Diffusion directions from whole neurons as well as from the soma and dendrite branches, and include a comparison of the high b-value behavior between dendrite branches and whole neurons. In addition, we demonstrate that the neuron meshes can be used to perform Monte-Carlo Diffusion MRI simulations as well. We show that at equivalent accuracy, if only one gradient direction needs to be simulated, SpinDoctor is faster than a GPU implementation of Monte-Carlo. Finally, we numerically compute the eigenfunctions and the eigenvalues of the Bloch-Torrey and the Laplace operators on the neuron geometries using a finite elements discretization, in order to give guidance in the choice of the space and time discretization parameters for both finite elements and Monte-Carlo approaches.

  • Portable simulation framework for Diffusion MRI.
    Journal of Magnetic Resonance, 2019
    Co-Authors: Van Dang Nguyen, Demian Wassermann, Massimiliano Leoni, Tamara Dancheva, Johan Jansson, Johan Hoffman, Jing-rebecca Li
    Abstract:

    Abstract The numerical simulation of the Diffusion MRI signal arising from complex tissue micro-structures is helpful for understanding and interpreting imaging data as well as for designing and optimizing MRI sequences. The discretization of the Bloch-Torrey equation by finite elements is a more recently developed approach for this purpose, in contrast to random walk simulations, which has a longer history. While finite element discretization is more difficult to implement than random walk simulations, the approach benefits from a long history of theoretical and numerical developments by the mathematical and engineering communities. In particular, software packages for the automated solutions of partial differential equations using finite element discretization, such as FEniCS, are undergoing active support and development. However, because Diffusion MRI simulation is a relatively new application area, there is still a gap between the simulation needs of the MRI community and the available tools provided by finite element software packages. In this paper, we address two potential difficulties in using FEniCS for Diffusion MRI simulation. First, we simplified software installation by the use of FEniCS containers that are completely portable across multiple platforms. Second, we provide a portable simulation framework based on Python and whose code is open source. This simulation framework can be seamlessly integrated with cloud computing resources such as Google Colaboratory notebooks working on a web browser or with Google Cloud Platform with MPI parallelization. We show examples illustrating the accuracy, the computational times, and parallel computing capabilities. The framework contributes to reproducible science and open-source software in computational Diffusion MRI with the hope that it will help to speed up method developments and stimulate research collaborations.

  • Portable simulation framework for Diffusion MRI
    Journal of magnetic resonance (San Diego Calif. : 1997), 2019
    Co-Authors: Van Dang Nguyen, Massimiliano Leoni, Tamara Dancheva, Johan Jansson, Johan Hoffman, Demian Wassermann
    Abstract:

    The numerical simulation of the Diffusion MRI signal arising from complex tissue micro-structures is helpful for understanding and interpreting imaging data as well as for designing and optimizing ...

  • Sensing Spindle Neurons in the Insula with Multi-shell Diffusion MRI
    2018
    Co-Authors: Demian Wassermann, Van-dang Nguyen, Guillermo Gallardo-diez, Weidong Cai, Vinod Menon
    Abstract:

    Sensing microstructural characteristics of human brain tissue with clinical scanners has been an area of heated debate in the Diffusion MRI (dMRI) community. In this work, we propose that Diffusion ...

Van-dang Nguyen - One of the best experts on this subject based on the ideXlab platform.

  • Diffusion MRI simulation of realistic neurons with SpinDoctor and the Neuron Module.
    NeuroImage, 2020
    Co-Authors: Chengran Fang, Van-dang Nguyen, Demian Wassermann
    Abstract:

    Abstract The Diffusion MRI signal arising from neurons can be numerically simulated by solving the Bloch-Torrey partial differential equation. In this paper we present the Neuron Module that we implemented within the Matlab-based Diffusion MRI simulation toolbox SpinDoctor. SpinDoctor uses finite element discretization and adaptive time integration to solve the Bloch-Torrey partial differential equation for general Diffusion-encoding sequences, at multiple b-values and in multiple Diffusion directions. In order to facilitate the Diffusion MRI simulation of realistic neurons by the research community, we constructed finite element meshes for a group of 36 pyramidal neurons and a group of 29 spindle neurons whose morphological descriptions were found in the publicly available neuron repository NeuroMorpho.Org. These finite elements meshes range from having 15,163 nodes to 622,553 nodes. We also broke the neurons into the soma and dendrite branches and created finite elements meshes for these cell components. Through the Neuron Module, these neuron and cell components finite element meshes can be seamlessly coupled with the functionalities of SpinDoctor to provide the Diffusion MRI signal attributable to spins inside neurons. We make these meshes and the source code of the Neuron Module available to the public as an open-source package. To illustrate some potential uses of the Neuron Module, we show numerical examples of the simulated Diffusion MRI signals in multiple Diffusion directions from whole neurons as well as from the soma and dendrite branches, and include a comparison of the high b-value behavior between dendrite branches and whole neurons. In addition, we demonstrate that the neuron meshes can be used to perform Monte-Carlo Diffusion MRI simulations as well. We show that at equivalent accuracy, if only one gradient direction needs to be simulated, SpinDoctor is faster than a GPU implementation of Monte-Carlo, but if many gradient directions need to be simulated, there is a break-even point when the GPU implementation of Monte-Carlo becomes faster than SpinDoctor. Furthermore, we numerically compute the eigenfunctions and the eigenvalues of the Bloch-Torrey and the Laplace operators on the neuron geometries using a finite elements discretization, in order to give guidance in the choice of the space and time discretization parameters for both finite elements and Monte-Carlo approaches. Finally, we perform a statistical study on the set of 65 neurons to test some candidate biomakers that can potentially indicate the soma size. This preliminary study exemplifies the possible research that can be conducted using the Neuron Module.

  • Diffusion MRI simulation of realistic neurons with SpinDoctor and the Neuron Module
    arXiv: Neurons and Cognition, 2019
    Co-Authors: Chengran Fang, Van-dang Nguyen, Demian Wassermann
    Abstract:

    In this paper we present the Neuron Module that we implemented within the Matlab-based Diffusion MRI simulation toolbox SpinDoctor. SpinDoctor uses finite element discretization and adaptive time integration to solve the Bloch-Torrey partial differential equation for general Diffusion-encoding sequences, at multiple b-values and in multiple Diffusion directions. In order to facilitate the Diffusion MRI simulation of realistic neurons by the research community, we constructed finite element meshes for a group of 36 pyramidal neurons and a group of 29 spindle neurons whose morphological descriptions were found in the publicly available neuron repository NeuroMorpho. We also broke the neurons into the soma and dendrite branches and created finite elements meshes for these cell components. Through the Neuron Module, these neuron and cell components finite element meshes can be seamlessly coupled with the functionalities of SpinDoctor. To illustrate some potential uses of the Neuron Module, we show numerical examples of the simulated Diffusion MRI signals in multiple Diffusion directions from whole neurons as well as from the soma and dendrite branches, and include a comparison of the high b-value behavior between dendrite branches and whole neurons. In addition, we demonstrate that the neuron meshes can be used to perform Monte-Carlo Diffusion MRI simulations as well. We show that at equivalent accuracy, if only one gradient direction needs to be simulated, SpinDoctor is faster than a GPU implementation of Monte-Carlo. Finally, we numerically compute the eigenfunctions and the eigenvalues of the Bloch-Torrey and the Laplace operators on the neuron geometries using a finite elements discretization, in order to give guidance in the choice of the space and time discretization parameters for both finite elements and Monte-Carlo approaches.

  • Diffusion MRI simulation in thin-layer and thin-tube media using a discretization on manifolds
    Journal of Magnetic Resonance, 2019
    Co-Authors: Van-dang Nguyen, Johan Jansson, Hoang Trong An Tran, Johan Hoffman
    Abstract:

    The Bloch-Torrey partial differential equation can be used to describe the evolution of the transverse magnetization of the imaged sample under the influence of Diffusion-encoding magnetic field gradients inside the MRI scanner. The integral of the magnetization inside a voxel gives the simulated Diffusion MRI signal. This paper proposes a finite element discretization on manifolds in order to efficiently simulate the Diffusion MRI signal in domains that have a thin layer or a thin tube geometrical structure. The variable thickness of the three-dimensional domains is included in the weak formulation established on the manifolds. We conducted a numerical study of the proposed approach by simulating the Diffusion MRI signals from the extracellular space (a thin layer medium) and from neurons (a thin tube medium), comparing the results with the reference signals obtained using a standard three-dimensional finite element discretization. We show good agreements between the simulated signals using our proposed method and the reference signals for a wide range of Diffusion MRI parameters. The approximation becomes better as the Diffusion time increases. The method helps to significantly reduce the required simulation time, computational memory, and difficulties associated with mesh generation, thus opening the possibilities to simulating complicated structures at low cost for a better understanding of Diffusion MRI in the brain.

  • Sensing Spindle Neurons in the Insula with Multi-shell Diffusion MRI
    2018
    Co-Authors: Demian Wassermann, Van-dang Nguyen, Guillermo Gallardo-diez, Weidong Cai, Vinod Menon
    Abstract:

    Sensing microstructural characteristics of human brain tissue with clinical scanners has been an area of heated debate in the Diffusion MRI (dMRI) community. In this work, we propose that Diffusion ...

Daniel C. Alexander - One of the best experts on this subject based on the ideXlab platform.

  • In utero Diffusion MRI: challenges, advances, and applications
    arXiv: Medical Physics, 2019
    Co-Authors: Daan Christiaens, Daniel C. Alexander, Paddy J. Slator, Lucilio Cordero-grande, Anthony N. Price, Maria Deprez, Mary A. Rutherford, Joseph V. Hajnal, Jana Hutter
    Abstract:

    In utero Diffusion MRI provides unique opportunities to non-invasively study the microstructure of tissue during fetal development. A wide range of developmental processes, such as the growth of white matter tracts in the brain, the maturation of placental villous trees, or the fibres in the fetal heart remain to be studied and understood in detail. Advances in fetal interventions and surgery furthermore increase the need for ever more precise antenatal diagnosis from fetal MRI. However, the specific properties of the in utero environment, such as fetal and maternal motion, increased field-of-view, tissue interfaces and safety considerations, are significant challenges for most MRI techniques, and particularly for Diffusion. Recent years have seen major improvements, driven by the development of bespoke techniques adapted to these specific challenges in both acquisition and processing. Fetal Diffusion MRI, an emerging research tool, is now adding valuable novel information for both research and clinical questions. This paper will highlight specific challenges, outline strategies to target them, and discuss two main applications: fetal brain connectomics and placental maturation.

  • Modelling, Fitting and Sampling in Diffusion MRI
    Mathematics and Visualization, 2009
    Co-Authors: Daniel C. Alexander
    Abstract:

    This chapter discusses issues of modelling the Diffusion MRI signal from brain tissue, fitting models of tissue microstructure to Diffusion MRI measurements and designing acquisition schemes that provide the best estimates of model parameters. We construct a simple geometric model of white-matter tissue and derive an expression that relates the model parameters to the Diffusion MRI signal.The axon density and diameter are parameters of the model and we examine the accuracy and precision with which we can estimate these potentially important biomarkers.Precision and accuracy depend on the set of measurements we acquire and the method we use to fit the model parameters to the data.We investigate various strategies to optimize the experiment design, as well as various objective functions for model fitting.Experiments and results compare the different methods and provide insight into the accuracy with which we can measure axon density and diameters.

  • ICCV - Axon radius measurements in vivo from Diffusion MRI: a feasibility study
    2007 IEEE 11th International Conference on Computer Vision, 2007
    Co-Authors: Daniel C. Alexander
    Abstract:

    This paper investigates the feasibility of using Diffusion MRI to measure axon-cell dimensions in the white matter of live subjects. A simple geometric model of white-matter tissue provides an expression that relates the axon radius to the Diffusion MRI signal. The aim is to determine the accuracy and precision with which we can estimate this potentially important new biomarker. Precision and accuracy depend critically on the acquisition protocol. The paper proposes a general strategy to optimize the experiment design of in-vivo Diffusion MRI experiments. The applicability of the design optimization extends well beyond the current work to optimizing the acquisition for any model of the Diffusion process. Simulation experiments and results suggest feasibility of measuring larger axon radii in vivo on modern MRI scanners using the optimized acquisition schemes, but that higher gradient strengths are required to measure smaller axons.

  • Visualization and Processing of Tensor Fields - An Introduction to Computational Diffusion MRI: the Diffusion Tensor and Beyond
    Mathematics and Visualization, 2006
    Co-Authors: Daniel C. Alexander
    Abstract:

    This chapter gives an introduction to the principles of Diffusion magnetic resonance imaging (MRI) with emphasis on the computational aspects. It introduces the philosophies underlying the technique and shows how to sensitize MRI measurements to the motion of particles within a sample material. The main body of the chapter is a technical review of Diffusion MRI reconstruction algorithms, which determine features of the material microstructure from Diffusion MRI measurements. The focus is on techniques developed for biomedical Diffusion MRI, but most of the methods discussed are applicable beyond this domain. The review begins by showing how the standard reconstruction algorithms in biomedical Diffusion MRI, Diffusion-tensor MRI and Diffusion spectrum imaging, arise from the principles of the measurement process. The discussion highlights the weaknesses of the standard approaches to motivate the development of a new generation of reconstruction algorithms and reviews the current state-of-the-art. The chapter concludes with a brief discussion of Diffusion MRI applications, in particular fibre tracking, followed by a summary and a glimpse into the future of Diffusion MRI acquisition and reconstruction.

  • IPMI - Maximum entropy spherical deconvolution for Diffusion MRI
    Information processing in medical imaging : proceedings of the ... conference, 2005
    Co-Authors: Daniel C. Alexander
    Abstract:

    This paper proposes a maximum entropy method for spherical deconvolution. Spherical deconvolution arises in various inverse problems. This paper uses the method to reconstruct the distribution of microstructural fibre orientations from Diffusion MRI measurements. Analysis shows that the PASMRI algorithm, one of the most accurate Diffusion MRI reconstruction algorithms in the literature, is a special case of the maximum entropy spherical deconvolution. Experiments compare the new method to linear spherical deconvolution, used previously in Diffusion MRI, and to the PASMRI algorithm. The new method compares favourably both in simulation and on standard brain-scan data.

Derek K Jones - One of the best experts on this subject based on the ideXlab platform.

  • The sensitivity of Diffusion MRI to microstructural properties and experimental factors.
    Journal of neuroscience methods, 2020
    Co-Authors: Maryam Afzali, Hu Cheng, Tomasz Pieciak, Sharlene D. Newman, Eleftherios Garyfallidis, Evren Özarslan, Derek K Jones
    Abstract:

    Abstract Diffusion MRI is a non-invasive technique to study brain microstructure. Differences in the microstructural properties of tissue, including size and anisotropy, can be represented in the signal if the appropriate method of acquisition is used. However, to depict the underlying properties, special care must be taken when designing the acquisition protocol as any changes in the procedure might impact on quantitative measurements. This work reviews the state-of-the-art methods for studying brain microstructure using Diffusion MRI and their sensitivity to microstructural differences and various experimental factors. Microstructural properties of the tissue at a micrometer scale can be linked to the Diffusion signal at a millimeter-scale using modeling. In this paper, we first give an introduction to Diffusion MRI and different encoding schemes. Then, signal representation-based methods and multi-compartment models are briefly explained. The sensitivity of the Diffusion MRI signal to the microstructural components and the effects of curvedness of axonal trajectories on the Diffusion signal are reviewed. Factors that impact on the quality (accuracy and precision) of derived metrics are then reviewed, including the impact of random noise, and variations in the acquisition parameters (i.e., number of sampled signals, b-value and number of acquisition shells). Finally, yet importantly, typical approaches to deal with experimental factors are depicted, including unbiased measures and harmonization. We conclude the work with some future directions and recommendations on this topic.

  • Diffusion MRI theory methods and applications
    Diffusion imaging in muscle, 2011
    Co-Authors: Derek K Jones
    Abstract:

    Since its initial development in the mid-1980's, and wide accessibility to perform Diffusion MRI on all MRI scanners, the use of Diffusion MRI has exploded. Nearly every MRI centre carries out Diffusion MRI of some kind. Obtaining good quality Diffusion MRI and making sound and robust inferences from the data is not trivial, however, and involves a long chain of events from ensuring that the hardware is performing optimally, the pulse sequence is carefully designed, the acquisition is optimal, the data quality is maximized while artifacts are minimized, the appropriate post-processing is used, and, where appropriate, the appropriate statistical testing is used, and the data are interpreted correctly. 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 a definitive, didactic and essential reference volume for those working with Diffusion MRI. Renowned for the clarity of his presentations, Prof Jones has worked closely with each author to ensure that the material is presented in the best possible and accessible manner. The book is aimed at those wishing to really understand where the Diffusion signal comes from, and obtain a thorough grounding in the theory, methods and applications of Diffusion MRI. The aim here is not to 'skim the surface' - but to dig deep into each topic - so that a thorough grounding is obtained. In assembling these topics (many of which have never previously appeared in a text book on the topic), Prof Jones carefully balances theoretical treatments with practical considerations. Destined to be a modern classic, this definitive and richly illustrated reference volume deserves a place on the bookshelf of all imaging centres.

  • Twenty-five pitfalls in the analysis of Diffusion MRI data.
    NMR in biomedicine, 2010
    Co-Authors: Derek K Jones, Mara Cercignani
    Abstract:

    Obtaining reliable data and drawing meaningful and robust inferences from Diffusion MRI can be challenging and is subject to many pitfalls. The process of quantifying Diffusion indices and eventually comparing them between groups of subjects and/or correlating them with other parameters starts at the acquisition of the raw data, followed by a long pipeline of image processing steps. Each one of these steps is susceptible to sources of bias, which may not only limit the accuracy and precision, but can lead to substantial errors. This article provides a detailed review of the steps along the analysis pipeline and their associated pitfalls. These are grouped into 1 pre-processing of data; 2 estimation of the tensor; 3 derivation of voxelwise quantitative parameters; 4 strategies for extracting quantitative parameters; and finally 5 intra-subject and inter-subject comparison, including region of interest, histogram, tract-specific and voxel-based analyses. The article covers important aspects of Diffusion MRI analysis, such as motion correction, susceptibility and eddy current distortion correction, model fitting, region of interest placement, histogram and voxel-based analysis. We have assembled 25 pitfalls (several previously unreported) into a single article, which should serve as a useful reference for those embarking on new Diffusion MRI-based studies, and as a check for those who may already be running studies but may have overlooked some important confounds. While some of these problems are well known to Diffusion experts, they might not be to other researchers wishing to undertake a clinical study based on Diffusion MRI.

  • Challenges and limitations of quantifying brain connectivity in vivo with Diffusion MRI
    Imaging in Medicine, 2010
    Co-Authors: Derek K Jones
    Abstract:

    This article addresses whether or not Diffusion MRI, a noninvasive technique that probes the microstructural aspects of tissue, can be used to quantify the white matter connectivity of the human brain in vivo. It begins by studying the motivation, that is, the increasing trend to look at ‘functional connectivity’ in the brain, which implies that the brain operates as a distributed network of active locations. A brief summary of Diffusion MRI and fiber tracking is given and the early applications of Diffusion MRI to study connectivity are reviewed. A close and critical inspection is then made of the limitations inherent in these different approaches, challenging the notion that it is possible to quantify brain connectivity in vivo with Diffusion MRI. Finally, steps toward improving quantification of connectivity, by integrating information from other techniques, are suggested.

Denis Le Bihan - One of the best experts on this subject based on the ideXlab platform.

  • Diffusion MRI: what water tells us about the brain
    EMBO molecular medicine, 2014
    Co-Authors: Denis Le Bihan
    Abstract:

    Diffusion MRI has been used worldwide to produce images of brain tissue structure and connectivity, in the normal and diseased brain. Diffusion MRI has revolutionized the management of acute brain ischemia (stroke), saving life of many patients and sparing them significant disabilities. In addition to stroke, Diffusion MRI is now widely used for the detection of cancers and metastases (breast, prostate, liver). Another major field of application of Diffusion MRI regards the wiring of the brain. Diffusion MRI is now used to map the circuitry of the human brain with incredible accuracy, opening up new lines of inquiry for human neuroscience and for the understanding of brain illnesses or mental disorders. Here, as a pioneer of the field, I provide a personal account on the historical development of these concepts over the last 30 years.

  • eMagRes - Le Bihan, Denis: Diffusion MRI: A Historical Account
    Encyclopedia of Magnetic Resonance, 2010
    Co-Authors: Denis Le Bihan
    Abstract:

    The circumstances that have permitted and accompanied the birth and early developments of Diffusion MRI in the 1980s are presented. It is shown how it was made possible to localize Diffusion measurements with MRI and how, thanks to echo-planar imaging (EPI), Diffusion MRI entered the clinical world (stroke, cancer, white matter diseases). Other concepts, such as IVIM, which gives access to perfusion, and Diffusion tensor imaging (DTI), which allows maps of brain connections to be obtained, are introduced. Recent applications of Diffusion MRI to brain function mapping are described, and it is suggested how Diffusion MRI may give clues on the role of water in biological processes. Keywords: MRI; Diffusion; neuroimaging; functional MRI; fMRI, perfusion; cancer; hyperthermia

  • eMagRes - Methods and Applications of Diffusion MRI
    Encyclopedia of Magnetic Resonance, 2007
    Co-Authors: Denis Le Bihan
    Abstract:

    The sections in this article are 1 Introduction 2 Diffusion MRI 3 Diffusion in Biological Systems: Effects of Microdynamics and Microstructure 4 Clinical Applications 5 Conclusion 6 Biographical Sketch Related Articles

  • Artifacts and pitfalls in Diffusion MRI.
    Journal of Magnetic Resonance Imaging, 2006
    Co-Authors: Denis Le Bihan, Cyril Poupon, Alexis Amadon, Franck Lethimonnier
    Abstract:

    Although over the last 20 years Diffusion MRI has become an established technique with a great impact on health care and neurosciences, like any other MRI technique it remains subject to artifacts and pitfalls. In addition to common MRI artifacts, there are specific problems that one may encounter when using MRI scanner gradient hardware for Diffusion MRI, especially in terms of eddy currents and sensitivity to motion. In this article we review those artifacts and pitfalls on a qualitative basis, and introduce possible strategies that have been developed to mitigate or overcome them.

  • Bridging the Gap between Brain Anatomy and Function with Diffusion MRI
    Rivista di Neuroradiologia, 2003
    Co-Authors: Denis Le Bihan
    Abstract:

    Water Diffusion MRI allows tissue structure to be probed and imaged at a microscopic scale well beyond the usual “millimetric” image resolution, providing unique clues to the fine architecture of neural tissues, and to changes associated with various physiological or pathological states. The leading clinical application of Diffusion MRI has been in the study of acute brain ischaemia. With its unmatched sensitivity, Diffusion MRI provides some patients with the opportunity to receive suitable treatment at a stage when brain tissue might still be salvageable. Moreover, because Diffusion is anisotropic in brain white matter, reflecting its organization in bundles of myelinated axonal fibres running in parallel, Diffusion MRI can be used to map out the orientation in space of the white matter tracks in the brain. Diffusion MRI is also a promising tool for the study of brain maturation and development.