AUTHOR=Schneider Till M. , Ma Jackie , Wagner Patrick , Behl Nicolas , Nagel Armin M. , Ladd Mark E. , Heiland Sabine , Bendszus Martin , Straub Sina 

TITLE=Multiparametric MRI for Characterization of the Basal Ganglia and the Midbrain

JOURNAL=Frontiers in Neuroscience

VOLUME=Volume 15 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2021.661504

DOI=10.3389/fnins.2021.661504

ISSN=1662-453X

ABSTRACT=Objectives: 
To characterize subcortical nuclei by multiparametric quantitative magnetic resonance imaging. 
Materials and Methods: 
The following quantitative multiparametric MR data of five healthy volunteers were acquired on a 7T MRI system: (1) 3D gradient echo (GRE) data for the calculation of quantitative susceptibility maps (QSM), (2) GRE sequences with and without off-resonant magnetic transfer pulse for magnetization transfer ratio (MTR) calculation, (3) a magnetization‐prepared 2 rapid acquisition gradient echo sequence for T1 mapping, and (after a coil change) (4) a density-adapted 3D radial pulse sequence for 23Na imaging. First, all data were co-registered to the GRE data, volumes of interest (VOIs) for 21 subcortical structures were drawn manually for each volunteer, and a combined voxel-wise analysis of the four MR contrasts (QSM, MTR, T1, 23Na) in each structure was conducted to assess the quantitative, MR value-based differentiability of structures. Second, a machine learning algorithm based on random forests was trained to automatically classify the groups of multi-parametric voxel values from each VOI according to their association to one of the 21 subcortical structures.
Results:
The analysis of the integrated multimodal visualization of quantitative MR values in each structure yielded a successful classification among nuclei of the ascending reticular activation system (ARAS), the limbic system and the extrapyramidal system, while classification among (epi-)thalamic nuclei was less successful. The machine learning-based approach facilitated quantitative MR value-based structure classification especially in the group of extrapyramidal nuclei and reached an overall accuracy of 85% regarding all selected nuclei. 
Conclusions: 
Multimodal quantitative MR enabled excellent differentiation of a wide spectrum of subcortical nuclei with reasonable accuracy and may thus enable sensitive detection of disease and nucleus-specific MR-based contrast alterations in the future.