Accelerated T mapping combining parallel MRI and model-based reconstruction: GRAPPATINI.

Background: Quantitative T measurements are sensitive to intra- and extracellular water accumulation and myelin loss. Therefore, quantitative T promises to be a good biomarker of disease. However, T measurements require long acquisition times.

Multicontrast MRI Quantification of Focal Inflammation and Degeneration in Multiple Sclerosis.

Introduction: Local microstructural pathology in multiple sclerosis patients might influence their clinical performance. This study applied multicontrast MRI to quantify inflammation and neurodegeneration in MS lesions. We explored the impact of MRI-based lesion pathology in cognition and disability.

Multicontrast connectometry: a new tool to assess cerebellum alterations in early relapsing-remitting multiple sclerosis.

Background: Cerebellar pathology occurs in late multiple sclerosis (MS) but little is known about cerebellar changes during early disease stages. In this study, we propose a new multicontrast "connectometry" approach to assess the structural and functional integrity of cerebellar networks and connectivity in early MS.

Methods: We used diffusion spectrum and resting-state functional MRI (rs-fMRI) to establish the structural and functional cerebellar connectomes in 28 early relapsing-remitting MS patients and 16 healthy controls (HC).

Advanced MRI unravels the nature of tissue alterations in early multiple sclerosis.

Introduction: In patients with multiple sclerosis (MS), conventional magnetic resonance imaging (MRI) provides only limited insights into the nature of brain damage with modest clinic-radiological correlation. In this study, we applied recent advances in MRI techniques to study brain microstructural alterations in early relapsing-remitting MS (RRMS) patients with minor deficits. Further, we investigated the potential use of advanced MRI to predict functional performances in these patients.

Fast T2 mapping with improved accuracy using undersampled spin-echo MRI and model-based reconstructions with a generating function.

A model-based reconstruction technique for accelerated T2 mapping with improved accuracy is proposed using undersampled Cartesian spin-echo magnetic resonance imaging (MRI) data. The technique employs an advanced signal model for T2 relaxation that accounts for contributions from indirect echoes in a train of multiple spin echoes. An iterative solution of the nonlinear inverse reconstruction problem directly estimates spin-density and T2 maps from undersampled raw data.

Model-based nonlinear inverse reconstruction for T2 mapping using highly undersampled spin-echo MRI.

Purpose: To develop a model-based reconstruction technique for T2 mapping based on multi-echo spin-echo MRI sequences with highly undersampled Cartesian data encoding.

Materials And Methods: The proposed technique relies on a nonlinear inverse reconstruction algorithm which directly estimates a T2 and spin-density map from a train of undersampled spin echoes. The method is applicable to acquisitions with single receiver coils but benefits from multi-element coil arrays.