Since the hypothalamus is involved in many neuroendocrine, metabolic, and affective disorders, detailed hypothalamic imaging has become of major interest to better characterize disease-induced tissue damages and abnormalities. Still, image contrast of conventional anatomical magnetic resonance imaging lacks morphological detail, thus complicating complete and precise segmentation of the hypothalamus. The hypothalamus’ position lateral to the third ventricle and close proximity to white matter tracts including the optic tract, fornix, and mammillothalamic tract display one of the remaining shortcomings of hypothalamic segmentation, as reliable exclusion of white matter is not yet possible. Recent studies found that quantitative magnetic resonance imaging (qMRI), a method to create maps of different standardized tissue contents, improved segmentation of cortical and subcortical brain regions (Carey et al., 2017). So far, this has not been tested for the hypothalamus. Therefore, in this study, we investigated the usability of qMRI and diffusion MRI for the purpose of detailed manual segmentation and data-driven parcellation of the hypothalamus and compared our results to recent state-of-the-art segmentations. Our results show that qMRI and diffusion parameters indeed differ between hypothalamic subunits, and that qMRI is helpful for hypothalamic segmentation. In addition, we provide a data-driven clustering algorithm to reliably exclude white matter from hypothalamic tissue. We propose that qMRI poses a useful addition to detailed hypothalamic segmentation and volumetry.
Quantitative magnetic resonance imaging for segmentation and white matter extraction of the hypothalamus
