| |  Online-Ressource |
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| Verfasst von: | Straub, Sina [VerfasserIn]  |
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| | Stiegeler, Janis [VerfasserIn] |
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| | El-Sanosy, Edris [VerfasserIn] |
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| | Bendszus, Martin [VerfasserIn] |
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| | Ladd, Mark E. [VerfasserIn] |
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| | Schneider, Till M. [VerfasserIn] |
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| Titel: | A novel gradient echo data based vein segmentation algorithm and its application for the detection of regional cerebral differences in venous susceptibility |
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| Verf.angabe: | Sina Straub, Janis Stiegeler, Edris El-Sanosy, Martin Bendszus, Mark E. Ladd, Till M. Schneider |
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| E-Jahr: | 2022 |
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| Jahr: | 24 January 2022 |
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| Umfang: | 11 S. |
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| Fussnoten: | Gesehen am 11.05.2022 |
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| Titel Quelle: | Enthalten in: NeuroImage |
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| Ort Quelle: | Orlando, Fla. : Academic Press, 1992 |
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| Jahr Quelle: | 2022 |
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| Band/Heft Quelle: | 250(2022), Artikel-ID 118931, Seite 1-11 |
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| ISSN Quelle: | 1095-9572 |
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| Abstract: | Accurate segmentation of cerebral venous vasculature from gradient echo data is of central importance in several areas of neuroimaging such as for the susceptibility-based assessment of brain oxygenation or planning of electrode placement in deep brain stimulation. In this study, a vein segmentation algorithm for single- and multi-echo gradient echo data is proposed. First, susceptibility maps, true susceptibility-weighted images, and, in the multi-echo case, R2* maps were generated from the gradient echo data. These maps were filtered with an inverted Hamming filter to suppress background contrast as well as artifacts from field inhomogeneities at the brain boundaries. A shearlet-based scale-wise representation was generated to calculate a vesselness function and to generate segmentations based on local thresholding. The accuracy of the proposed algorithm was evaluated for different echo times and image resolutions using a manually generated reference segmentation and two vein segmentation algorithms (Frangi vesselness-based, recursive vesselness filter) as a reference with the Dice and Cohen's coefficients as well as the modified Hausdorff distance. The Frangi-based and recursive vesselness filter methods were significantly outperformed with regard to all error metrics. Applying the algorithm, susceptibility differences likely related to differences in blood oxygenation between superficial and deep venous territories could be demonstrated. |
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| DOI: | doi:10.1016/j.neuroimage.2022.118931 |
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| URL: | Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.
Volltext ; Verlag: https://doi.org/10.1016/j.neuroimage.2022.118931 |
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| | Volltext: https://www.sciencedirect.com/science/article/pii/S105381192200060X |
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| | DOI: https://doi.org/10.1016/j.neuroimage.2022.118931 |
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| Datenträger: | Online-Ressource |
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| Sprache: | eng |
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| Sach-SW: | Arteries |
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| | Brain vessels |
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| | Magnetic resonance imaging |
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| | Quantitative susceptibility mapping |
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| | Segmentation |
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| | Veins |
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| K10plus-PPN: | 1801394210 |
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| Verknüpfungen: | → Zeitschrift |
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¬A¬ novel gradient echo data based vein segmentation algorithm and its application for the detection of regional cerebral differences in venous susceptibility / Straub, Sina [VerfasserIn]; 24 January 2022 (Online-Ressource)
