Multiparametric MRI for Characterization of the Basal Ganglia and the Midbrain
| Autor: | Martin Bendszus, Armin M. Nagel, Jackie Ma, Mark E. Ladd, Patrick Wagner, Sina Straub, Nicolas G.R. Behl, Till M. Schneider, Sabine Heiland |
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| Přispěvatelé: | Publica |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Computer science
magnetization transfer Neurosciences. Biological psychiatry. Neuropsychiatry computer.software_genre Nuclear magnetic resonance Limbic system Voxel Basal ganglia medicine magnetic resonance imaging Magnetization transfer ddc:610 Original Research quantitative susceptibility mapping medicine.diagnostic_test General Neuroscience Magnetic resonance imaging Quantitative susceptibility mapping Random forest medicine.anatomical_structure machine learning basal ganglia Extrapyramidal system ultra high field computer sodium imaging RC321-571 Neuroscience |
| Zdroj: | Frontiers in Neuroscience Frontiers in Neuroscience, Vol 15 (2021) |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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