Influence of water compartmentation and heterogeneous relaxation on quantitative magnetization transfer imaging in rodent brain tumors

Influence of water compartmentation and heterogeneous relaxation on quantitative magnetization transfer imaging in rodent brain tumors.

PURPOSE The goal of this study was to investigate the influence of water compartmentation and heterogeneous relaxation properties on quantitative magnetization transfer (qMT) imaging in tissues, and in particular whether a two-pool model is sufficient to describe qMT data in brain tumors. METHODS Computer simulations and in vivo experiments with a series of qMT measurements before and after i...

Pathological Assessment of Brain White Matter in Relapsing-Remitting MS Patients using Quantitative Magnetization Transfer Imaging

Introduction: Multiple sclerosis (MS) is characterized by lesions in the white matter (WM) of the central nervous system. Magnetic resonance imaging is the most specific and sensitive method for diagnosis of multiple sclerosis. However, the ability of conventional MRI to show histopathologic heterogeneity of MS lesions is insufficient. Quantitative magnetization transfer imaging (qMTI) is a rel...

Quantitative magnetization transfer imaging of human brain at 7 T

Quantitative magnetization transfer (qMT) imaging yields indices describing the interactions between free water protons and immobile macromolecular protons. These indices include the macromolecular to free pool size ratio (PSR), which has been shown to be correlated with myelin content in white matter. Because of the long scan times required for whole-brain imaging (≈20-30 min), qMT studies of ...

Quantitative Magnetization Transfer Imaging Techniques and Applications

A Novel Classification Method using Effective Neural Network and Quantitative Magnetization Transfer Imaging of Brain White Matter in Relapsing Remitting Multiple Sclerosis

Background: Quantitative Magnetization Transfer Imaging (QMTI) is often used to quantify the myelin content in multiple sclerosis (MS) lesions and normal appearing brain tissues. Also, automated classifiers such as artificial neural networks (ANNs) can significantly improve the identification and classification processes of MS clinical datasets.Objective: We classified patients with relapsing-r...