Multiparametric MRI for Characterization of the Basal Ganglia and the Midbrain

Language
en
Document Type
Article
Issue Date
2021-07-09
First published
2021-06-21
Issue Year
2021
Authors
Schneider, Till M.
Ma, Jackie
Wagner, Patrick
Behl, Nicolas
Nagel, Armin M.
Ladd, Mark E.
Heiland, Sabine
Bendszus, Martin
Straub, Sina
Editor
Publisher
Frontiers Media S.A.
Abstract

Objectives To characterize subcortical nuclei by multi-parametric quantitative magnetic resonance imaging. Materials and Methods: The following quantitative multiparametric MR data of five healthy volunteers were acquired on a 7T MRI system: 3D gradient echo (GRE) data for the calculation of quantitative susceptibility maps (QSM), GRE sequences with and without off-resonant magnetic transfer pulse for magnetization transfer ratio (MTR) calculation, a magnetization−prepared 2 rapid acquisition gradient echo sequence for T1 mapping, and (after a coil change) 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. Conclusion 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.

Journal Title
Frontiers in Neuroscience
Volume
15
Citation
Frontiers in Neuroscience 15 (2021): 661504. <https://www.frontiersin.org/articles/10.3389/fnins.2021.661504/full>
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