Multiparametric MRI for characterization of the basal ganglia and the midbrain
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 qua...
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| Main Authors: | , , , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
21 June 2021
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| In: |
Frontiers in neuroscience
Year: 2021, Volume: 15 |
| ISSN: | 1662-453X |
| DOI: | 10.3389/fnins.2021.661504 |
| Online Access: | Verlag, kostenfrei, Volltext: https://doi.org/10.3389/fnins.2021.661504 Verlag, kostenfrei, Volltext: https://www.frontiersin.org/articles/10.3389/fnins.2021.661504/full 
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| Author Notes: | Till M. Schneider, Jackie Ma, Patrick Wagner, Nicolas Behl, Armin M. Nagel, Mark E. Ladd, Sabine Heiland, Martin Bendszus and Sina Straub |
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| 520 | |a 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. | ||
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