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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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Item Permalink: http://hdl.handle.net/21.11116/0000-0001-6ABB-6 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-9A34-5
Genre: Journal Article

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 Creators:
Wolff, Julia1, Author
Schindler, Stephanie1, Author              
Lucas, Christian1, Author
Binninger, Anne-Sophie1, Author
Weinrich, Luise1, Author
Schreiber, Jan1, Author
Hegerl, Ulrich1, Author
Möller, Harald E.2, Author              
Leitzke, Marco3, Author
Geyer, Stefan4, Author              
Schönknecht, Peter1, Author
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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Free keywords: Magnetic resonance imaging; Hypothalamus; Semi-automated; Volumetry; Anatomy; Human
 Abstract: 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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Language(s): eng - English
 Dates: 2018-04-292017-10-222018-04-302018-05-012018-07-30
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1016/j.pscychresns.2018.04.007
PMID: 29776867
Other: Epub 2018
 Degree: -

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Project name : -
Grant ID : HA-314
Funding program : -
Funding organization : Helmholtz Alliance “ICEMED – Imaging and Curing Environmental Metabolic Diseases”

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Title: Psychiatry Research: Neuroimaging
  Other : Psychiatry Res. Neuroimaging
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: Elsevier
Pages: - Volume / Issue: 277 Sequence Number: - Start / End Page: 45 - 51 Identifier: ISSN: 0925-4927
CoNE: https://pure.mpg.de/cone/journals/resource/954925566740