Comparison of Neurite Orientation Dispersion and Density Imaging and Two-Compartment Spherical Mean Technique Parameter Maps in Multiple Sclerosis.

Neurite orientation dispersion and density imaging (NODDI) and the spherical mean technique (SMT) are diffusion MRI methods providing metrics with sensitivity to similar characteristics of white matter microstructure. There has been limited comparison of changes in NODDI and SMT parameters due to multiple sclerosis (MS) pathology in clinical settings. To compare group-wise differences between healthy controls and MS patients in NODDI and SMT metrics, investigating associations with disability and correlations with diffusion tensor imaging (DTI) metrics.

On the potential for mapping apparent neural soma density via a clinically viable diffusion MRI protocol.

Diffusion MRI is a valuable tool for probing tissue microstructure in the brain noninvasively. Today, model-based techniques are widely available and used for white matter characterisation where their development is relatively mature. Conversely, tissue modelling in grey matter is more challenging, and no generally accepted models exist.

Tractographic and Microstructural Analysis of the Dentato-Rubro-Thalamo-Cortical Tracts in Children Using Diffusion MRI.

The dentato-rubro-thalamo-cortical tract (DRTC) is the main outflow pathway of the cerebellum, contributing to a finely balanced corticocerebellar loop involved in cognitive and sensorimotor functions. Damage to the DRTC has been implicated in cerebellar mutism syndrome seen in up to 25% of children after cerebellar tumor resection. Multi-shell diffusion MRI (dMRI) combined with quantitative constrained spherical deconvolution tractography and multi-compartment spherical mean technique modeling was used to explore the frontocerebellar connections and microstructural signature of the DRTC in 30 healthy children.

Uncertainty modelling in deep learning for safer neuroimage enhancement: Demonstration in diffusion MRI.

Deep learning (DL) has shown great potential in medical image enhancement problems, such as super-resolution or image synthesis. However, to date, most existing approaches are based on deterministic models, neglecting the presence of different sources of uncertainty in such problems. Here we introduce methods to characterise different components of uncertainty, and demonstrate the ideas using diffusion MRI super-resolution.

Cross-scanner and cross-protocol multi-shell diffusion MRI data harmonization: Algorithms and results.

Cross-scanner and cross-protocol variability of diffusion magnetic resonance imaging (dMRI) data are known to be major obstacles in multi-site clinical studies since they limit the ability to aggregate dMRI data and derived measures. Computational algorithms that harmonize the data and minimize such variability are critical to reliably combine datasets acquired from different scanners and/or protocols, thus improving the statistical power and sensitivity of multi-site studies. Different computational approaches have been proposed to harmonize diffusion MRI data or remove scanner-specific differences.

Microscopic susceptibility anisotropy imaging.

Purpose: The gradient-echo MR signal in brain white matter depends on the orientation of the fibers with respect to the external magnetic field. To map microstructure-specific magnetic susceptibility in orientationally heterogeneous material, it is thus imperative to regress out unwanted orientation effects.

Methods: This work introduces a novel framework, referred to as microscopic susceptibility anisotropy imaging, that disentangles the 2 principal effects conflated in gradient-echo measurements, (a) the susceptibility properties of tissue microenvironments, especially the myelin microstructure, and (b) the axon orientation distribution relative to the magnetic field.

Microstructural Investigations of the Visual Pathways in Pediatric Epilepsy Neurosurgery: Insights From Multi-Shell Diffusion Magnetic Resonance Imaging.

Background: Surgery is a key approach for achieving seizure freedom in children with focal onset epilepsy. However, the resection can affect or be in the vicinity of the optic radiations. Multi-shell diffusion MRI and tractography can better characterize tissue structure and provide guidance to help minimize surgical related deficits.

MRI profiling of focal cortical dysplasia using multi-compartment diffusion models.

Objective: Focal cortical dysplasia (FCD) lesion detection and subtyping remain challenging on conventional MRI. New diffusion models such as the spherical mean technique (SMT) and neurite orientation dispersion and density imaging (NODDI) provide measurements that potentially produce more specific maps of abnormal tissue microstructure. This study aims to assess the SMT and NODDI maps for computational and radiological lesion characterization compared to standard fractional anisotropy (FA) and mean diffusivity (MD).

White matter microstructural abnormalities in children with severe congenital hypothyroidism.

This study assessed white matter microstructural integrity and behavioral correlates for children with severe congenital hypothyroidism (CH) who were identified and treated early following newborn screening. Eighteen children with severe CH and 21 healthy controls underwent a battery of behavioral measures of hearing, language and communication, along with diffusion MR imaging. Tract-based spatial statistics were performed on standard diffusion parameters of fractional anisotropy and diffusivity metrics.

Probing axons using multi-compartmental diffusion in multiple sclerosis.

Objects: The diffusion-based spherical mean technique (SMT) provides a novel model to relate multi-b-value diffusion magnetic resonance imaging (MRI) data to features of tissue microstructure. We propose the first clinical application of SMT to image the brain of patients with multiple sclerosis (MS) and investigate clinical feasibility and translation.

Methods: Eighteen MS patients and nine age- and sex-matched healthy controls (HCs) underwent a 3.

In vivo demonstration of microscopic anisotropy in the human kidney using multidimensional diffusion MRI.

Purpose: To demonstrate the feasibility of multidimensional diffusion MRI to probe and quantify microscopic fractional anisotropy (µFA) in human kidneys in vivo.

Methods: Linear tensor encoded (LTE) and spherical tensor encoded (STE) renal diffusion MRI scans were performed in 10 healthy volunteers. Respiratory triggering and image registration were used to minimize motion artefacts during the acquisition.

Tractography reproducibility challenge with empirical data (TraCED): The 2017 ISMRM diffusion study group challenge.

Background: Fiber tracking with diffusion-weighted MRI has become an essential tool for estimating in vivo brain white matter architecture. Fiber tracking results are sensitive to the choice of processing method and tracking criteria.

Purpose: To assess the variability for an algorithm in group studies reproducibility is of critical context.

Cross-scanner and cross-protocol diffusion MRI data harmonisation: A benchmark database and evaluation of algorithms.

Diffusion MRI is being used increasingly in studies of the brain and other parts of the body for its ability to provide quantitative measures that are sensitive to changes in tissue microstructure. However, inter-scanner and inter-protocol differences are known to induce significant measurement variability, which in turn jeopardises the ability to obtain 'truly quantitative measures' and challenges the reliable combination of different datasets. Combining datasets from different scanners and/or acquired at different time points could dramatically increase the statistical power of clinical studies, and facilitate multi-centre research.

Relevance of time-dependence for clinically viable diffusion imaging of the spinal cord.

Purpose: Time-dependence is a key feature of the diffusion-weighted (DW) signal, knowledge of which informs biophysical modelling. Here, we study time-dependence in the human spinal cord, as its axonal structure is specific and different from the brain.

Methods: We run Monte Carlo simulations using a synthetic model of spinal cord white matter (WM) (large axons), and of brain WM (smaller axons).

Multi-compartment microscopic diffusion imaging.

This paper introduces a multi-compartment model for microscopic diffusion anisotropy imaging. The aim is to estimate microscopic features specific to the intra- and extra-neurite compartments in nervous tissue unconfounded by the effects of fibre crossings and orientation dispersion, which are ubiquitous in the brain. The proposed MRI method is based on the Spherical Mean Technique (SMT), which factors out the neurite orientation distribution and thus provides direct estimates of the microscopic tissue structure.

Quantitative mapping of the per-axon diffusion coefficients in brain white matter.

Purpose: This article presents a simple method for estimating the effective diffusion coefficients parallel and perpendicular to the axons unconfounded by the intravoxel fiber orientation distribution. We also call these parameters the per-axon or microscopic diffusion coefficients.

Theory And Methods: Diffusion MR imaging is used to probe the underlying tissue material.

PGSE, OGSE, and sensitivity to axon diameter in diffusion MRI: Insight from a simulation study.

Purpose: To identify optimal pulsed gradient spin-echo (PGSE) and oscillating gradient spin-echo (OGSE) sequence settings for maximizing sensitivity to axon diameter in idealized and practical conditions.

Methods: Simulations on a simple two-compartment white matter model (with nonpermeable cylinders) are used to investigate a wide space of clinically plausible PGSE and OGSE sequence parameters with trapezoidal diffusion gradient waveforms. Signal sensitivity is measured as a derivative of the signal with respect to axon diameter.

Can T2-spectroscopy resolve submicrometer axon diameters?

The microscopic geometry of white matter carries rich information about brain function in health and disease. A key challenge for medical imaging is to estimate microstructural features noninvasively. One important parameter is the axon diameter, which correlates with the conduction time delay of action potentials and is affected by various neurological disorders.

Nonparametric Bayesian inference of the fiber orientation distribution from diffusion-weighted MR images.

Diffusion MR imaging provides a unique tool to probe the microgeometry of nervous tissue and to explore the wiring diagram of the neural connections noninvasively. Generally, a forward model is established to map the intra-voxel fiber architecture onto the observable diffusion signals, which is reformulated in this article by adopting a measure-theoretic approach. However, the inverse problem, i.

A reproducing kernel hilbert space approach for q-ball imaging.

Diffusion magnetic resonance (MR) imaging has enabled us to reveal the white matter geometry in the living human brain. The Q-ball technique is widely used nowadays to recover the orientational heterogeneity of the intra-voxel fiber architecture. This article proposes to employ the Funk-Radon transform in a Hilbert space with a reproducing kernel derived from the spherical Laplace-Beltrami operator, thus generalizing previous approaches that assume a bandlimited diffusion signal.

Variational inference of the fiber orientation density using diffusion MR imaging.

Diffusion MR imaging has enabled the in vivo exploration of the connectional architecture in human brain. This method particularly reveals the complex system of long-range nerve fibers that integrate the functionally distinct areas of the cerebral cortex. Since the fibers are not directly observed but the diffusion process of water molecules in the underlying material, a forward model is established that maps the microgeometry of nervous tissue onto the diffusion-weighted signals.

Parametric spherical deconvolution: inferring anatomical connectivity using diffusion MR imaging.

The human brain forms a complex neural network with a connectional architecture that is still far from being known in full detail, even at the macroscopic level. The advent of diffusion MR imaging has enabled the exploration of the structural properties of white matter in vivo. In this article we propose a new forward model that maps the microscopic geometry of nervous tissue onto the water diffusion process and further onto the measured MR signals.