000169797 001__ 169797
000169797 005__ 20240229133655.0
000169797 0247_ $$2doi$$a10.3389/fnins.2021.661504
000169797 0247_ $$2pmid$$apmid:34234639
000169797 0247_ $$2pmc$$apmc:PMC8255625
000169797 0247_ $$2ISSN$$a1662-453X
000169797 0247_ $$2ISSN$$a1662-4548
000169797 0247_ $$2altmetric$$aaltmetric:108670778
000169797 037__ $$aDKFZ-2021-01551
000169797 041__ $$aEnglish
000169797 082__ $$a610
000169797 1001_ $$aSchneider, Till M$$b0
000169797 245__ $$aMultiparametric MRI for Characterization of the Basal Ganglia and the Midbrain.
000169797 260__ $$aLausanne$$bFrontiers Research Foundation$$c2021
000169797 3367_ $$2DRIVER$$aarticle
000169797 3367_ $$2DataCite$$aOutput Types/Journal article
000169797 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1626089698_13917
000169797 3367_ $$2BibTeX$$aARTICLE
000169797 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000169797 3367_ $$00$$2EndNote$$aJournal Article
000169797 500__ $$a#LA:E020#
000169797 520__ $$aObjectives To characterize subcortical nuclei by multi-parametric quantitative magnetic resonance imaging. Materials and Methods: The following quantitative multiparametric MR data of five healthy volunteers were acquired on a 7T MRI system: 3D gradient echo (GRE) data for the calculation of quantitative susceptibility maps (QSM), GRE sequences with and without off-resonant magnetic transfer pulse for magnetization transfer ratio (MTR) calculation, a magnetization-prepared 2 rapid acquisition gradient echo sequence for T1 mapping, and (after a coil change) a density-adapted 3D radial pulse sequence for 23Na imaging. First, all data were co-registered to the GRE data, volumes of interest (VOIs) for 21 subcortical structures were drawn manually for each volunteer, and a combined voxel-wise analysis of the four MR contrasts (QSM, MTR, T1, 23Na) in each structure was conducted to assess the quantitative, MR value-based differentiability of structures. Second, a machine learning algorithm based on random forests was trained to automatically classify the groups of multi-parametric voxel values from each VOI according to their association to one of the 21 subcortical structures. Results The analysis of the integrated multimodal visualization of quantitative MR values in each structure yielded a successful classification among nuclei of the ascending reticular activation system (ARAS), the limbic system and the extrapyramidal system, while classification among (epi-)thalamic nuclei was less successful. The machine learning-based approach facilitated quantitative MR value-based structure classification especially in the group of extrapyramidal nuclei and reached an overall accuracy of 85% regarding all selected nuclei. Conclusion Multimodal quantitative MR enabled excellent differentiation of a wide spectrum of subcortical nuclei with reasonable accuracy and may thus enable sensitive detection of disease and nucleus-specific MR-based contrast alterations in the future.
000169797 536__ $$0G:(DE-HGF)POF4-315$$a315 - Bildgebung und Radioonkologie (POF4-315)$$cPOF4-315$$fPOF IV$$x0
000169797 588__ $$aDataset connected to CrossRef, PubMed, , Journals: inrepo01.inet.dkfz-heidelberg.de
000169797 650_7 $$2Other$$abasal ganglia
000169797 650_7 $$2Other$$amachine learning
000169797 650_7 $$2Other$$amagnetic resonance imaging
000169797 650_7 $$2Other$$amagnetization transfer
000169797 650_7 $$2Other$$aquantitative susceptibility mapping
000169797 650_7 $$2Other$$asodium imaging
000169797 650_7 $$2Other$$aultra high field
000169797 7001_ $$aMa, Jackie$$b1
000169797 7001_ $$aWagner, Patrick$$b2
000169797 7001_ $$0P:(DE-He78)596c7f2f2a07a37019b79f94ad8a4190$$aBehl, Nicolas$$b3
000169797 7001_ $$0P:(DE-He78)054fd7a5195b75b11fbdc5c360276011$$aNagel, Armin M$$b4$$udkfz
000169797 7001_ $$0P:(DE-He78)022611a2317e4de40fd912e0a72293a8$$aLadd, Mark E$$b5$$udkfz
000169797 7001_ $$aHeiland, Sabine$$b6
000169797 7001_ $$aBendszus, Martin$$b7
000169797 7001_ $$0P:(DE-He78)4e04dcea1b6a4449a8fa005bcf36322b$$aStraub, Sina$$b8$$eLast author$$udkfz
000169797 773__ $$0PERI:(DE-600)2411902-7$$a10.3389/fnins.2021.661504$$gVol. 15, p. 661504$$p661504$$tFrontiers in neuroscience$$v15$$x1662-453X$$y2021
000169797 909CO $$ooai:inrepo02.dkfz.de:169797$$pVDB
000169797 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)596c7f2f2a07a37019b79f94ad8a4190$$aDeutsches Krebsforschungszentrum$$b3$$kDKFZ
000169797 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)054fd7a5195b75b11fbdc5c360276011$$aDeutsches Krebsforschungszentrum$$b4$$kDKFZ
000169797 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)022611a2317e4de40fd912e0a72293a8$$aDeutsches Krebsforschungszentrum$$b5$$kDKFZ
000169797 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)4e04dcea1b6a4449a8fa005bcf36322b$$aDeutsches Krebsforschungszentrum$$b8$$kDKFZ
000169797 9130_ $$0G:(DE-HGF)POF3-315$$1G:(DE-HGF)POF3-310$$2G:(DE-HGF)POF3-300$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bGesundheit$$lKrebsforschung$$vImaging and radiooncology$$x0
000169797 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
000169797 9141_ $$y2021
000169797 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2021-05-04
000169797 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2021-05-04
000169797 915__ $$0StatID:(DE-HGF)0320$$2StatID$$aDBCoverage$$bPubMed Central$$d2021-05-04
000169797 915__ $$0StatID:(DE-HGF)0501$$2StatID$$aDBCoverage$$bDOAJ Seal$$d2021-05-04
000169797 915__ $$0StatID:(DE-HGF)0500$$2StatID$$aDBCoverage$$bDOAJ$$d2021-05-04
000169797 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bDOAJ : Blind peer review$$d2021-05-04
000169797 915__ $$0LIC:(DE-HGF)CCBYNV$$2V:(DE-HGF)$$aCreative Commons Attribution CC BY (No Version)$$bDOAJ$$d2021-05-04
000169797 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bFRONT NEUROSCI-SWITZ : 2019$$d2021-05-04
000169797 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2021-05-04
000169797 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2021-05-04
000169797 915__ $$0StatID:(DE-HGF)1050$$2StatID$$aDBCoverage$$bBIOSIS Previews$$d2021-05-04
000169797 915__ $$0StatID:(DE-HGF)1110$$2StatID$$aDBCoverage$$bCurrent Contents - Clinical Medicine$$d2021-05-04
000169797 915__ $$0StatID:(DE-HGF)1190$$2StatID$$aDBCoverage$$bBiological Abstracts$$d2021-05-04
000169797 915__ $$0StatID:(DE-HGF)0113$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2021-05-04
000169797 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2021-05-04
000169797 915__ $$0StatID:(DE-HGF)9900$$2StatID$$aIF < 5$$d2021-05-04
000169797 915__ $$0StatID:(DE-HGF)0561$$2StatID$$aArticle Processing Charges$$d2021-05-04
000169797 915__ $$0StatID:(DE-HGF)0561$$2StatID$$aArticle Processing Charges$$d2021-05-04
000169797 915__ $$0StatID:(DE-HGF)0700$$2StatID$$aFees$$d2021-05-04
000169797 9201_ $$0I:(DE-He78)E020-20160331$$kE020$$lE020 Med. Physik in der Radiologie$$x0
000169797 980__ $$ajournal
000169797 980__ $$aVDB
000169797 980__ $$aI:(DE-He78)E020-20160331
000169797 980__ $$aUNRESTRICTED