An introduction to model-independent diffusion magnetic resonance imaging.

ABSTRACT: q-Space-based techniques such as diffusion spectrum imaging, q-ball imaging, and their variations have been used extensively in research for their desired capability to delineate complex neuronal architectures such as multiple fiber crossings in each of the image voxels. The purpose of this article was to provide an introduction to the q-space formalism and the principles of basic q-space techniques together with the discussion on the advantages as well as challenges in translating these techniques into the clinical environment. A review of the currently used q-space-based protocols in clinical research is also provided

Rotationally-invariant mapping of scalar and orientational metrics of neuronal microstructure with diffusion MRI

We develop a general analytical and numerical framework for estimating intra- and extra-neurite water fractions and diffusion coefficients, as well as neurite orientational dispersion, in each imaging voxel. By employing a set of rotational invariants and their expansion in the powers of diffusion weighting, we analytically uncover the nontrivial topology of the parameter estimation landscape, showing that multiple branches of parameters describe the measurement almost equally well, with only one of them corresponding to the biophysical reality. A comprehensive acquisition shows that the branch choice varies across the brain. Our framework reveals hidden degeneracies in MRI parameter estimation for neuronal tissue, provides microstructural and orientational maps in the whole brain without constraints or priors, and connects modern biophysical modeling with clinical MRI.Comment: 25 pages, 12 figures, elsarticle two-colum

Anisotropy Across Fields and Scales

This open access book focuses on processing, modeling, and visualization of anisotropy information, which are often addressed by employing sophisticated mathematical constructs such as tensors and other higher-order descriptors. It also discusses adaptations of such constructs to problems encountered in seemingly dissimilar areas of medical imaging, physical sciences, and engineering. Featuring original research contributions as well as insightful reviews for scientists interested in handling anisotropy information, it covers topics such as pertinent geometric and algebraic properties of tensors and tensor fields, challenges faced in processing and visualizing different types of data, statistical techniques for data processing, and specific applications like mapping white-matter fiber tracts in the brain. The book helps readers grasp the current challenges in the field and provides information on the techniques devised to address them. Further, it facilitates the transfer of knowledge between different disciplines in order to advance the research frontiers in these areas. This multidisciplinary book presents, in part, the outcomes of the seventh in a series of Dagstuhl seminars devoted to visualization and processing of tensor fields and higher-order descriptors, which was held in Dagstuhl, Germany, on October 28–November 2, 2018

Anisotropy Across Fields and Scales

This open access book focuses on processing, modeling, and visualization of anisotropy information, which are often addressed by employing sophisticated mathematical constructs such as tensors and other higher-order descriptors. It also discusses adaptations of such constructs to problems encountered in seemingly dissimilar areas of medical imaging, physical sciences, and engineering. Featuring original research contributions as well as insightful reviews for scientists interested in handling anisotropy information, it covers topics such as pertinent geometric and algebraic properties of tensors and tensor fields, challenges faced in processing and visualizing different types of data, statistical techniques for data processing, and specific applications like mapping white-matter fiber tracts in the brain. The book helps readers grasp the current challenges in the field and provides information on the techniques devised to address them. Further, it facilitates the transfer of knowledge between different disciplines in order to advance the research frontiers in these areas. This multidisciplinary book presents, in part, the outcomes of the seventh in a series of Dagstuhl seminars devoted to visualization and processing of tensor fields and higher-order descriptors, which was held in Dagstuhl, Germany, on October 28–November 2, 2018

Imaging diffusional variance by MRI [public] : The role of tensor-valued diffusion encoding and tissue heterogeneity

Diffusion MRI provides a non-invasive probe of tissue microstructure. We recently proposed a novel method for diffusion-weighted imaging, so-called q-space trajectory encoding, that facilitates tensor-valued diffusion encoding. This method grants access to b-tensors with multiple shapes and enables us to probe previously unexplored aspects of the tissue microstructure. Specifically, we can disentangle diffusional heterogeneity that originates from isotropic and anisotropic tissue structures; we call this diffusional variance decomposition (DIVIDE).In Paper I, we investigated the statistical uncertainty of the total diffusional variance in the healthy brain. We found that the statistical power was heterogeneous between brain regions which needs to be taken into account when interpreting results.In Paper II, we showed how spherical tensor encoding can be used to separate the total diffusional variance into its isotropic and anisotropic components. We also performed initial validation of the parameters in phantoms, and demonstrated that the imaging sequence could be implemented on a high-performance clinical MRI system. In Paper III and V, we explored DIVIDE parameters in healthy brain tissue and tumor tissue. In healthy tissue, we found that diffusion anisotropy can be probed on the microscopic scale, and that metrics of anisotropy on the voxel scale are confounded by the orientation coherence of the microscopic structures. In meningioma and glioma tumors, we found a strong association between anisotropic variance and cell eccentricity, and between isotropic variance and variable cell density. In Paper IV, we developed a method to optimize waveforms for tensor-valued diffusion encoding, and in Paper VI we demonstrated that whole-brain DIVIDE is technically feasible at most MRI systems in clinically feasible scan times

Multi-tensor model based tractography of axonal bundles

Cílem semestrální práce je návrh trasovacího algoritmu, který zohledňuje mikrostrukturní vlastnosti nervové tkáně. K této problematice je sepsána rešerše obsahující úvod do problematiky. Je zde popsán jev difuze, princip difuzně váženého MRI a odhad profilu anizotropní difuze. K podrobnější analýze byl vybrán algoritmus COMMIT, u kterého byla navržena alternativní optimalizační metoda.The aim of this work is to design the tractography algorithm which consider microstructure features of the neuronal tissue. The methodological background is described, where diffusion, diffusion weighted MRI and single voxel diffusion profile modeling are depicted. COMMIT model was chosen to be analyzed and alternative method of optimization was proposed.

Radiotherapy Response Using Intravoxel Incoherent Motion Magnetic Resonance Imaging in Liver Patients Treated with Stereotactic Body Radiotherapy

Magnetic resonance imaging is utilized as an important tool in radiation oncology for delineation of healthy and cancerous tissues, and evaluating the functionality of those tissues, structures, and organs. Currently, the clinical imaging protocol at Virginia Commonwealth University includes anatomical imaging for tissue and structure delineation, and to observe treatment induced changes. Diffusion weighted imaging (DWI) is also acquired for calculation of apparent diffusion coefficient (ADC) values to provide quantitative information on tissue diffusivity and microstructure. However, anatomical images and ADC values may not display the true extent of changes in tissue. This work seeks to further utilize the capabilities of MRI and expand its role in treatment response monitoring for liver cancer patients treated with stereotactic body radiotherapy (SBRT). To do so, an imaging protocol and image analysis methodology to evaluate treatment changes on pre- and post-treatment image sets was developed. An extension of DWI, termed intravoxel incoherent motion (IVIM) imaging, was utilized to quantitatively assess levels of perfusion and diffusion within the liver and tumor. Acquisition of high-quality diffusion weighted images of the liver necessitated the development of an MR safe respiratory motion management device, which was designed, constructed and evaluated in this work. An imaging protocol was developed providing anatomical and functional images of the liver, acquired under breath hold, utilizing the respiratory motion management device. An IVIM parameter calculation and texture analysis workflow was developed using MATLAB, and applied to acquired data sets from multiple studies, including past clinical cases, investigator, healthy volunteer, and liver cancer patient . Differences in IVIM and texture analysis parameters were investigated for healthy and diseased tissue, and for select dose regions from pre- and post-treatment imaging sessions. Significant differences, at a voxel level, were found between healthy and diseased tissue, and pre- and post-treatment volumes, for multiple parameters, including apparent diffusion coefficient, pure diffusion, and perfusion, as well as for various texture features. Overall, this study showed the potential of IVIM and texture analysis to be used for discriminating between healthy and diseased tissues in the liver, and for indication of treatment response