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.
Highlights
Subcortical nuclei of the basal ganglia, midbrain and brainstem are interconnected structures of gray matter that play an instrumental role in the integration of motor as well as nonmotor behavioral functions of the brain (Nelson and Kreitzer, 2014; Simonyan, 2019)
quantitative susceptibility mapping (QSM) and the QSM/MT-overlay display the anatomical substructures of the basal ganglia and midbrain with the highest contrast, followed by T1 and magnetization transfer ratio (MTR)
A slight increase in contrast can be visually appreciated for the bed nucleus of the stria terminalis (BNST), pedunculopontine nucleus (PPN), dorsal raphe nucleus (DRN), and the medial lemniscus (ML) on the QSM/MT-overlay compared to QSM contrast alone
Summary
Subcortical nuclei of the basal ganglia, midbrain and brainstem are interconnected structures of gray matter that play an instrumental role in the integration of motor as well as nonmotor behavioral functions of the brain (Nelson and Kreitzer, 2014; Simonyan, 2019). The present study assesses subcortical nuclei of the basal ganglia and the midbrain using QSM, MTR, sodium imaging and T1 relaxation time mapping at 7T. QSM provides an excellent image contrast for optimized discrimination of basal ganglia (Deistung et al, 2013a; Keuken et al, 2014), and enables detection of increased iron deposition in the basal ganglia associated with a range of degenerative and inflammatory diseases such as multiple sclerosis, Parkinson’s and Huntington’s disease as well as alcohol use disorder (Wallis et al, 2008; Dominguez et al, 2016; Langkammer et al, 2016; Juhas et al, 2017; Zivadinov et al, 2018). Reduced subcortical T1 relaxation times have been associated with gray matter loss following neurodegenerative disease (Baudrexel et al., 2010), and MTR imaging of subcortical structures has shown promising results for the discrimination of Parkinson’s disease and atypical Parkinson syndromes (Eckert et al, 2004) as it uses radiofrequency off-resonance pulses to saturate macromoleculeassociated protons. The resulting magnetization transfer is dependent on the exchange rate between pools of coupled and free protons and correlates with the concentration of macromolecules (Wolff and Balaban, 1994; Henkelman et al, 2001; Horsfield et al, 2003; Peper et al, 2013)
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