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  A semi-automated algorithm for hypothalamus volumetry in 3 Tesla magnetic resonance images

Wolff, J., Schindler, S., Lucas, C., Binninger, A.-S., Weinrich, L., Schreiber, J., et al. (2018). A semi-automated algorithm for hypothalamus volumetry in 3 Tesla magnetic resonance images. Psychiatry Research: Neuroimaging, 277, 45-51. doi:10.1016/j.pscychresns.2018.04.007.

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 Urheber:
Wolff, Julia1, Autor
Schindler, Stephanie1, Autor           
Lucas, Christian1, Autor
Binninger, Anne-Sophie1, Autor
Weinrich, Luise1, Autor
Schreiber, Jan1, Autor
Hegerl, Ulrich1, Autor
Möller, Harald E.2, Autor           
Leitzke, Marco3, Autor
Geyer, Stefan4, Autor           
Schönknecht, Peter1, Autor
Affiliations:
1Department of Psychiatry and Psychotherapy, University Hospital Leipzig, Germany, ou_persistent22              
2Methods and Development Unit Nuclear Magnetic Resonance, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634558              
3Department of Anesthesiology, Helios Hospital, Leisnig, Germany, ou_persistent22              
4Department Neurophysics (Weiskopf), MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_2205649              

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Schlagwörter: Magnetic resonance imaging; Hypothalamus; Semi-automated; Volumetry; Anatomy; Human
 Zusammenfassung: The hypothalamus, a small diencephalic gray matter structure, is part of the limbic system. Volumetric changes of this structure occur in psychiatric diseases, therefore there is increasing interest in precise volumetry. Based on our detailed volumetry algorithm for 7 Tesla magnetic resonance imaging (MRI), we developed a method for 3 Tesla MRI, adopting anatomical landmarks and work in triplanar view. We overlaid T1-weighted MR images with gray matter-tissue probability maps to combine anatomical information with tissue class segmentation. Then, we outlined regions of interest (ROIs) that covered potential hypothalamus voxels. Within these ROIs, seed growing technique helped define the hypothalamic volume using gray matter probabilities from the tissue probability maps. This yielded a semi-automated method with short processing times of 20–40 min per hypothalamus. In the MRIs of ten subjects, reliabilities were determined as intraclass correlations (ICC) and volume overlaps in percent. Three raters achieved very good intra-rater reliabilities (ICC 0.82–0.97) and good inter-rater reliabilities (ICC 0.78 and 0.82). Overlaps of intra- and inter-rater runs were very good (≥ 89.7%). We present a fast, semi-automated method for in vivo hypothalamus volumetry in 3 Tesla MRI.

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Sprache(n): eng - English
 Datum: 2018-04-292017-10-222018-04-302018-05-012018-07-30
 Publikationsstatus: Erschienen
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: DOI: 10.1016/j.pscychresns.2018.04.007
PMID: 29776867
Anderer: Epub 2018
 Art des Abschluß: -

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Projektname : -
Grant ID : HA-314
Förderprogramm : -
Förderorganisation : Helmholtz Alliance “ICEMED – Imaging and Curing Environmental Metabolic Diseases”

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Titel: Psychiatry Research: Neuroimaging
  Andere : Psychiatry Res. Neuroimaging
Genre der Quelle: Zeitschrift
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: Elsevier
Seiten: - Band / Heft: 277 Artikelnummer: - Start- / Endseite: 45 - 51 Identifikator: ISSN: 0925-4927
CoNE: https://pure.mpg.de/cone/journals/resource/954925566740