Non-parametric graphnet-regularized representation of dMRI in space and time

Diffusion-relaxation scattered MR signal representation in a multi-parametric sequence

This work focuses on obtaining a more general diffusion magnetic resonance imaging (MRI) signal representation that accounts for a longitudinal T₁ and transverse T₂^⋆ relaxations while at the same time integrating directional diffusion in the context of scattered multi-parametric acquisitions, where only a few diffusion gradient directions and b-values are available for each pair of echo and inversion times. The method is based on the three-dimensional simple harmonic oscillator-based reconstruction and estimation (SHORE) representation of the diffusion signal, which enables the estimation of the orientation distribution function and the retrieval of various quantitative indices such as the generalized fractional anisotropy or the return-to-the-origin probability while simultaneously resolving for T₁ and T₂^⋆ relaxation times. Our technique, the Relax-SHORE, has been tested on both in silico and in vivo diffusion-relaxation scattered MR data. The results show that Relax-SHORE is accurate in the context of scattered acquisitions while guaranteeing flexibility in the diffusion signal representation from multi-parametric sequences.

The Dmipy Toolbox: Diffusion MRI Multi-Compartment Modeling and Microstructure Recovery Made Easy

Non-invasive estimation of brain microstructure features using diffusion MRI (dMRI)—known as Microstructure Imaging—has become an increasingly diverse and complicated field over the last decades. Multi-compartment (MC)-models, representing the measured diffusion signal as a linear combination of signal models of distinct tissue types, have been developed in many forms to estimate these features. However, a generalized implementation of MC-modeling as a whole, providing deeper insights in its capabilities, remains missing. To address this fact, we present Diffusion Microstructure Imaging in Python (Dmipy), an open-source toolbox implementing PGSE-based MC-modeling in its most general form. Dmipy allows on-the-fly implementation, signal modeling, and optimization of any user-defined MC-model, for any PGSE acquisition scheme. Dmipy follows a “building block”-based philosophy to Microstructure Imaging, meaning MC-models are modularly constructed to include any number and type of tissue models, allowing simultaneous representation of a tissue's diffusivity, orientation, volume fractions, axon orientation dispersion, and axon diameter distribution. In particular, Dmipy is geared toward facilitating reproducible, reliable MC-modeling pipelines, often allowing the whole process from model construction to parameter map recovery in fewer than 10 lines of code. To demonstrate Dmipy's ease of use and potential, we implement a wide range of well-known MC-models, including IVIM, AxCaliber, NODDI(x), Bingham-NODDI, the spherical mean-based SMT and MC-MDI, and spherical convolution-based single- and multi-tissue CSD. By allowing parameter cascading between MC-models, Dmipy also facilitates implementation of advanced approaches like CSD with voxel-varying kernels and single-shell 3-tissue CSD. By providing a well-tested, user-friendly toolbox that simplifies the interaction with the otherwise complicated field of dMRI-based Microstructure Imaging, Dmipy contributes to more reproducible, high-quality research.

Ultrahigh field imaging of myelin disease models: Toward specific markers of myelin integrity?

Specific magnetic resonance imaging (MRI) markers of myelin are critical for the evaluation and development of regenerative therapies for demyelinating diseases. Several MRI methods have been developed for myelin imaging, based either on acquisition schemes or on mathematical modeling of the signal. They generally showed good sensitivity but validation for specificity toward myelin is still warranted to allow a reliable interpretation in an in vivo complex pathological environment. Experimental models of dys-/demyelination are characterized by various levels of myelin disorders, axonal damage, gliosis and inflammation, and offer the opportunity for powerful correlative studies between imaging metrics and histology. Here, we review how ultrahigh field MRI markers have been correlated with histology in these models and provide insights into the trends for future developments of MRI tools in human myelin diseases. To this end, we present the biophysical basis of the main MRI methods for myelin imaging based on T₁ , T₂ , water diffusion, and magnetization transfer signal, the characteristics of animal models used and the outcomes of histological validations. To date such studies are limited, and demonstrate partial correlations with immunohistochemical and electron microscopy measures of myelin. These MRI metrics also often correlate with axons, glial, or inflammatory cells in models where axonal degeneration or inflammation occur as potential confounding factors. Therefore, the MRI markers' specificity for myelin is still perfectible and future developments should improve mathematical modeling of the MR signal based on more complex systems or provide multimodal approaches to better disentangle the biological processes underlying the MRI metrics.