Multiparametric MRI for Characterization of the Basal Ganglia and the Midbrain

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.

Variability by region and method in human brain sodium concentrations estimated by 23Na magnetic resonance imaging: a meta-analysis

“…CSF 100 4 64 8 0 20–35 None Gerhalter et al 2021 92 3 FLORET Int. VH 100 0.2 216 19 12 31.4 ± 7.5 TBI Brownlee et al 2019 19 3 Cones External 120 0.22 27 34 23 35.5 ± 10.1 MS Schneider et al 2021 59 7 DA radial Int. CSF 100 0.35 8 5 3 28.4 ± 6.5 None a Int .…”

“…In the absence of complete histologic information, another source of insight could come from comparing 23 Na-MRI to other imaging indices that might capture relevant features, with other quantitative imaging modalities being of particular interest. Parallel changes in 23 Na-MRI measures and diffusion imaging 15,[55][56][57] and proton density 58 are suggestive, but extending these to direct evaluations of the redundancy and complementarity with other quantitative modalities 59 , especially in healthy controls, in a range of regions and tissue structures would be a welcome development.…”

Variability by region and method in human brain sodium concentrations estimated by23Na magnetic resonance imaging: a meta-analysis

“…In the absence of complete histologic information, another source of insight could come from comparing 23 Na-MRI to other imaging indices that might capture relevant features, with other quantitative imaging modalities being of particular interest. Parallel changes in 23 Na-MRI measures and diffusion imaging 15,[76][77][78] and proton density 79 are suggestive, but extending these to direct evaluations of the redundancy and complementarity with other quantitative modalities 63 , especially in healthy controls, in a range of regions and tissue structures would be a welcome development.…”

A comprehensive protocol for quantitative magnetic resonance imaging of the brain at 3 Tesla

“…qMRI also exhibits superiority in tracking demyelinating processes in MS, mild cognitive impairment, and dementia [20,21]; progressive atrophy and loss of function of neurons in AD, Parkinson's disease, Wilson's disease, Huntington's disease, spinocerebellar ataxia, and myotonic dystrophy [22]; tumor development and infiltration [23,24]; and to observe neurological changes in the presence of obesity [25] and diabetes [26]. Another example for the utility of qMRI it its use for improving the characterization of complex brain regions such as the Basal Ganglia and Midbrain [27].…”

“…Another study by Filo et al [17] presented a multidimensional approach for characterization of aging-related changes associated with alterations in the molecular composition of the brain based on T 1 , T 2 , MTsat and MTVF maps. Schneider et al [27] developed a machine learning algorithm, trained to classify the multiparametric data (QSM, MTR, and T 1 maps) to characterize 21 subcortical nuclei structures. Tabelow et al [34] developed a toolbox for multiparametric qMRI, termed hMRI, aiming to improve the accessibility and standardization in the field.…”