Magnetic Resonance Brain Imaging
Modeling and Data Analysis Using R
Chapter: Multiparameter Mapping
Unlike conventional weighted MRI, leading to T1-, T2-, T2*-, or proton density (PD) weighted images in arbitrary units, quantitative MRI (qMRI) aims to estimate absolute physical metrics. One example is dMRI considered in Chapter DWI. qMRI is of increasing interest in neuroscience and clinical research for its greater specificity and its sensitivity to micro-structural properties of brain tissue such as axon, myelin, iron and water concentration. Furthermore, the measurement of quantitative data allows for comparison across sites, time points and participants, and enables longitudinal studies and multi-centre trials. In order to maintain its comparability, quantitative maps obtained from qMRI have to be adjusted for instrumental biases. Then, in combination with biophysical models, qMRI can enable the in-vivo characterization of key microscopic brain tissue parameters which previously could only be achieved with ex vivo histology. Here, we focus on the quantities, that are accessible by the multi-parameter mapping (MPM) approach. We will also present an adaptive smoothing algorithm for this type of data.