000178654 001__ 178654
000178654 005__ 20240229143552.0
000178654 0247_ $$2doi$$a10.1016/j.neuroimage.2022.118931
000178654 0247_ $$2pmid$$apmid:35085764
000178654 0247_ $$2ISSN$$a1053-8119
000178654 0247_ $$2ISSN$$a1095-9572
000178654 037__ $$aDKFZ-2022-00183
000178654 041__ $$aEnglish
000178654 082__ $$a610
000178654 1001_ $$0P:(DE-He78)4e04dcea1b6a4449a8fa005bcf36322b$$aStraub, Sina$$b0$$eFirst author$$udkfz
000178654 245__ $$aA novel gradient echo data based vein segmentation algorithm and its application for the detection of regional cerebral differences in venous susceptibility.
000178654 260__ $$aOrlando, Fla.$$bAcademic Press$$c2022
000178654 3367_ $$2DRIVER$$aarticle
000178654 3367_ $$2DataCite$$aOutput Types/Journal article
000178654 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1645449234_26267
000178654 3367_ $$2BibTeX$$aARTICLE
000178654 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000178654 3367_ $$00$$2EndNote$$aJournal Article
000178654 500__ $$a#EA:E020# / Volume 250, 15 April 2022, 118931
000178654 520__ $$aAccurate segmentation of cerebral venous vasculature from gradient echo data is of central importance in several areas of neuroimaging such as for the susceptibility-based assessment of brain oxygenation or planning of electrode placement in deep brain stimulation. In this study, a vein segmentation algorithm for single- and multi-echo gradient echo data is proposed. First, susceptibility maps, true susceptibility-weighted images, and, in the multi-echo case, R2* maps were generated from the gradient echo data. These maps were filtered with an inverted Hamming filter to suppress background contrast as well as artifacts from field inhomogeneities at the brain boundaries. A shearlet-based scale-wise representation was generated to calculate a vesselness function and to generate segmentations based on local thresholding. The accuracy of the proposed algorithm was evaluated for different echo times and image resolutions using a manually generated reference segmentation and two vein segmentation algorithms (Frangi vesselness-based, recursive vesselness filter) as a reference with the Dice and Cohen's coefficients as well as the modified Hausdorff distance. The Frangi-based and recursive vesselness filter methods were significantly outperformed with regard to all error metrics. Applying the algorithm, susceptibility differences likely related to differences in blood oxygenation between superficial and deep venous territories could be demonstrated.
000178654 536__ $$0G:(DE-HGF)POF4-315$$a315 - Bildgebung und Radioonkologie (POF4-315)$$cPOF4-315$$fPOF IV$$x0
000178654 588__ $$aDataset connected to CrossRef, PubMed, , Journals: inrepo01.inet.dkfz-heidelberg.de
000178654 650_7 $$2Other$$aarteries
000178654 650_7 $$2Other$$abrain vessels
000178654 650_7 $$2Other$$amagnetic resonance imaging
000178654 650_7 $$2Other$$aquantitative susceptibility mapping
000178654 650_7 $$2Other$$asegmentation
000178654 650_7 $$2Other$$aveins
000178654 7001_ $$0P:(DE-He78)07285d15125d8d608004e3efe21221c5$$aStiegeler, Janis$$b1$$udkfz
000178654 7001_ $$0P:(DE-He78)7c6e89ee4f7ff15c21f1a62a468c70bc$$aEl-Sanosy, Edris$$b2$$udkfz
000178654 7001_ $$aBendszus, Martin$$b3
000178654 7001_ $$0P:(DE-He78)022611a2317e4de40fd912e0a72293a8$$aLadd, Mark E$$b4$$udkfz
000178654 7001_ $$0P:(DE-He78)82c2b7c6cf14cf300b561faf9f46818d$$aSchneider, Till M$$b5
000178654 773__ $$0PERI:(DE-600)1471418-8$$a10.1016/j.neuroimage.2022.118931$$gp. 118931 -$$p118931$$tNeuroImage$$v250$$x1053-8119$$y2022
000178654 909CO $$ooai:inrepo02.dkfz.de:178654$$pVDB
000178654 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)4e04dcea1b6a4449a8fa005bcf36322b$$aDeutsches Krebsforschungszentrum$$b0$$kDKFZ
000178654 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)07285d15125d8d608004e3efe21221c5$$aDeutsches Krebsforschungszentrum$$b1$$kDKFZ
000178654 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)7c6e89ee4f7ff15c21f1a62a468c70bc$$aDeutsches Krebsforschungszentrum$$b2$$kDKFZ
000178654 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)022611a2317e4de40fd912e0a72293a8$$aDeutsches Krebsforschungszentrum$$b4$$kDKFZ
000178654 9131_ $$0G:(DE-HGF)POF4-315$$1G:(DE-HGF)POF4-310$$2G:(DE-HGF)POF4-300$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$aDE-HGF$$bGesundheit$$lKrebsforschung$$vBildgebung und Radioonkologie$$x0
000178654 9141_ $$y2022
000178654 915__ $$0LIC:(DE-HGF)CCBYNCNDNV$$2V:(DE-HGF)$$aCreative Commons Attribution-NonCommercial-NoDerivs CC BY-NC-ND (No Version)$$bDOAJ$$d2021-01-29
000178654 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2021-01-29
000178654 915__ $$0StatID:(DE-HGF)1190$$2StatID$$aDBCoverage$$bBiological Abstracts$$d2021-01-29
000178654 915__ $$0StatID:(DE-HGF)0113$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2021-01-29
000178654 915__ $$0StatID:(DE-HGF)0561$$2StatID$$aArticle Processing Charges$$d2021-01-29
000178654 915__ $$0StatID:(DE-HGF)0561$$2StatID$$aArticle Processing Charges$$d2021-01-29
000178654 915__ $$0StatID:(DE-HGF)0700$$2StatID$$aFees$$d2021-01-29
000178654 915__ $$0StatID:(DE-HGF)0420$$2StatID$$aNationallizenz$$d2022-11-12$$wger
000178654 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bNEUROIMAGE : 2021$$d2022-11-12
000178654 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2022-11-12
000178654 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2022-11-12
000178654 915__ $$0StatID:(DE-HGF)0501$$2StatID$$aDBCoverage$$bDOAJ Seal$$d2022-09-27T20:29:23Z
000178654 915__ $$0StatID:(DE-HGF)0500$$2StatID$$aDBCoverage$$bDOAJ$$d2022-09-27T20:29:23Z
000178654 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bDOAJ : Blind peer review$$d2022-09-27T20:29:23Z
000178654 915__ $$0StatID:(DE-HGF)0600$$2StatID$$aDBCoverage$$bEbsco Academic Search$$d2022-11-12
000178654 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bASC$$d2022-11-12
000178654 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2022-11-12
000178654 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2022-11-12
000178654 915__ $$0StatID:(DE-HGF)1050$$2StatID$$aDBCoverage$$bBIOSIS Previews$$d2022-11-12
000178654 915__ $$0StatID:(DE-HGF)1030$$2StatID$$aDBCoverage$$bCurrent Contents - Life Sciences$$d2022-11-12
000178654 915__ $$0StatID:(DE-HGF)9905$$2StatID$$aIF >= 5$$bNEUROIMAGE : 2021$$d2022-11-12
000178654 9201_ $$0I:(DE-He78)E020-20160331$$kE020$$lE020 Med. Physik in der Radiologie$$x0
000178654 9201_ $$0I:(DE-He78)E010-20160331$$kE010$$lE010 Radiologie$$x1
000178654 9200_ $$0I:(DE-He78)E020-20160331$$kE020$$lE020 Med. Physik in der Radiologie$$x0
000178654 980__ $$ajournal
000178654 980__ $$aVDB
000178654 980__ $$aI:(DE-He78)E020-20160331
000178654 980__ $$aI:(DE-He78)E010-20160331
000178654 980__ $$aUNRESTRICTED