Multiparametric MRI for characterization of the basal ganglia and the midbrain

• Verfasst von: Schneider, Till M. [VerfasserIn]
• Verfasst von: Behl, Nicolas G. R. [VerfasserIn]
• Verfasst von: Nagel, Armin Michael [VerfasserIn]
• Verfasst von: Ladd, Mark E. [VerfasserIn]
• Verfasst von: Heiland, Sabine [VerfasserIn]
• Verfasst von: Bendszus, Martin [VerfasserIn]
• Verfasst von: Straub, Sina [VerfasserIn]
• Titel: Multiparametric MRI for characterization of the basal ganglia and the midbrain
• Verf.angabe: Till M. Schneider, Jackie Ma, Patrick Wagner, Nicolas Behl, Armin M. Nagel, Mark E. Ladd, Sabine Heiland, Martin Bendszus and Sina Straub
• E-Jahr: 2021
• Jahr: 21 June 2021
• Umfang: 14 S.
• Teil: volume:15
• Teil: year:2021
• Teil: day:21
• Teil: month:06
• Teil: elocationid:661504
• Teil: extent:14
• Fussnoten: Gesehen am 13.07.2021
• Titel Quelle: Enthalten in: Frontiers in neuroscience
• Ort Quelle: Lausanne : Frontiers Research Foundation, 2007
• Jahr Quelle: 2021
• Band/Heft Quelle: 15(2021) vom: 21. Juni, Artikel-ID 661504
• ISSN Quelle: 1662-453X
• DOI: doi:10.3389/fnins.2021.661504
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Basal Ganglia
• Sach-SW: machine learning
• Sach-SW: Magnetic Resonance Imaging - Ultra High Field
• Sach-SW: magnetic transfer
• Sach-SW: Quantitative susceptibility mapping
• Sach-SW: Sodium imaging
• K10plus-PPN: 1762746360
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext

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.