guest ::
| Home > Publications database > Multiparametric MRI for Characterization of the Basal Ganglia and the Midbrain. > print |
| Home > Publications database > Multiparametric MRI for Characterization of the Basal Ganglia and the Midbrain. > print |
| 001 | 169797 | ||
| 005 | 20240229133655.0 | ||
| 024 | 7 | _ | |a 10.3389/fnins.2021.661504 |2 doi |
| 024 | 7 | _ | |a pmid:34234639 |2 pmid |
| 024 | 7 | _ | |a pmc:PMC8255625 |2 pmc |
| 024 | 7 | _ | |a 1662-453X |2 ISSN |
| 024 | 7 | _ | |a 1662-4548 |2 ISSN |
| 024 | 7 | _ | |a altmetric:108670778 |2 altmetric |
| 037 | _ | _ | |a DKFZ-2021-01551 |
| 041 | _ | _ | |a English |
| 082 | _ | _ | |a 610 |
| 100 | 1 | _ | |a Schneider, Till M |b 0 |
| 245 | _ | _ | |a Multiparametric MRI for Characterization of the Basal Ganglia and the Midbrain. |
| 260 | _ | _ | |a Lausanne |c 2021 |b Frontiers Research Foundation |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1626089698_13917 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a JOURNAL_ARTICLE |2 ORCID |
| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 500 | _ | _ | |a #LA:E020# |
| 520 | _ | _ | |a Objectives 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. |
| 536 | _ | _ | |a 315 - Bildgebung und Radioonkologie (POF4-315) |0 G:(DE-HGF)POF4-315 |c POF4-315 |x 0 |f POF IV |
| 588 | _ | _ | |a Dataset connected to CrossRef, PubMed, , Journals: inrepo01.inet.dkfz-heidelberg.de |
| 650 | _ | 7 | |a basal ganglia |2 Other |
| 650 | _ | 7 | |a machine learning |2 Other |
| 650 | _ | 7 | |a magnetic resonance imaging |2 Other |
| 650 | _ | 7 | |a magnetization transfer |2 Other |
| 650 | _ | 7 | |a quantitative susceptibility mapping |2 Other |
| 650 | _ | 7 | |a sodium imaging |2 Other |
| 650 | _ | 7 | |a ultra high field |2 Other |
| 700 | 1 | _ | |a Ma, Jackie |b 1 |
| 700 | 1 | _ | |a Wagner, Patrick |b 2 |
| 700 | 1 | _ | |a Behl, Nicolas |0 P:(DE-He78)596c7f2f2a07a37019b79f94ad8a4190 |b 3 |
| 700 | 1 | _ | |a Nagel, Armin M |0 P:(DE-He78)054fd7a5195b75b11fbdc5c360276011 |b 4 |u dkfz |
| 700 | 1 | _ | |a Ladd, Mark E |0 P:(DE-He78)022611a2317e4de40fd912e0a72293a8 |b 5 |u dkfz |
| 700 | 1 | _ | |a Heiland, Sabine |b 6 |
| 700 | 1 | _ | |a Bendszus, Martin |b 7 |
| 700 | 1 | _ | |a Straub, Sina |0 P:(DE-He78)4e04dcea1b6a4449a8fa005bcf36322b |b 8 |e Last author |u dkfz |
| 773 | _ | _ | |a 10.3389/fnins.2021.661504 |g Vol. 15, p. 661504 |0 PERI:(DE-600)2411902-7 |p 661504 |t Frontiers in neuroscience |v 15 |y 2021 |x 1662-453X |
| 909 | C | O | |o oai:inrepo02.dkfz.de:169797 |p VDB |
| 910 | 1 | _ | |a Deutsches Krebsforschungszentrum |0 I:(DE-588b)2036810-0 |k DKFZ |b 3 |6 P:(DE-He78)596c7f2f2a07a37019b79f94ad8a4190 |
| 910 | 1 | _ | |a Deutsches Krebsforschungszentrum |0 I:(DE-588b)2036810-0 |k DKFZ |b 4 |6 P:(DE-He78)054fd7a5195b75b11fbdc5c360276011 |
| 910 | 1 | _ | |a Deutsches Krebsforschungszentrum |0 I:(DE-588b)2036810-0 |k DKFZ |b 5 |6 P:(DE-He78)022611a2317e4de40fd912e0a72293a8 |
| 910 | 1 | _ | |a Deutsches Krebsforschungszentrum |0 I:(DE-588b)2036810-0 |k DKFZ |b 8 |6 P:(DE-He78)4e04dcea1b6a4449a8fa005bcf36322b |
| 913 | 0 | _ | |a DE-HGF |b Gesundheit |l Krebsforschung |1 G:(DE-HGF)POF3-310 |0 G:(DE-HGF)POF3-315 |3 G:(DE-HGF)POF3 |2 G:(DE-HGF)POF3-300 |4 G:(DE-HGF)POF |v Imaging and radiooncology |x 0 |
| 913 | 1 | _ | |a DE-HGF |b Gesundheit |l Krebsforschung |1 G:(DE-HGF)POF4-310 |0 G:(DE-HGF)POF4-315 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-300 |4 G:(DE-HGF)POF |v Bildgebung und Radioonkologie |x 0 |
| 914 | 1 | _ | |y 2021 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0200 |2 StatID |b SCOPUS |d 2021-05-04 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0300 |2 StatID |b Medline |d 2021-05-04 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0320 |2 StatID |b PubMed Central |d 2021-05-04 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0501 |2 StatID |b DOAJ Seal |d 2021-05-04 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0500 |2 StatID |b DOAJ |d 2021-05-04 |
| 915 | _ | _ | |a Peer Review |0 StatID:(DE-HGF)0030 |2 StatID |b DOAJ : Blind peer review |d 2021-05-04 |
| 915 | _ | _ | |a Creative Commons Attribution CC BY (No Version) |0 LIC:(DE-HGF)CCBYNV |2 V:(DE-HGF) |b DOAJ |d 2021-05-04 |
| 915 | _ | _ | |a JCR |0 StatID:(DE-HGF)0100 |2 StatID |b FRONT NEUROSCI-SWITZ : 2019 |d 2021-05-04 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0199 |2 StatID |b Clarivate Analytics Master Journal List |d 2021-05-04 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0160 |2 StatID |b Essential Science Indicators |d 2021-05-04 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1050 |2 StatID |b BIOSIS Previews |d 2021-05-04 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1110 |2 StatID |b Current Contents - Clinical Medicine |d 2021-05-04 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1190 |2 StatID |b Biological Abstracts |d 2021-05-04 |
| 915 | _ | _ | |a WoS |0 StatID:(DE-HGF)0113 |2 StatID |b Science Citation Index Expanded |d 2021-05-04 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0150 |2 StatID |b Web of Science Core Collection |d 2021-05-04 |
| 915 | _ | _ | |a IF < 5 |0 StatID:(DE-HGF)9900 |2 StatID |d 2021-05-04 |
| 915 | _ | _ | |a Article Processing Charges |0 StatID:(DE-HGF)0561 |2 StatID |d 2021-05-04 |
| 915 | _ | _ | |a Article Processing Charges |0 StatID:(DE-HGF)0561 |2 StatID |d 2021-05-04 |
| 915 | _ | _ | |a Fees |0 StatID:(DE-HGF)0700 |2 StatID |d 2021-05-04 |
| 920 | 1 | _ | |0 I:(DE-He78)E020-20160331 |k E020 |l E020 Med. Physik in der Radiologie |x 0 |
| 980 | _ | _ | |a journal |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a I:(DE-He78)E020-20160331 |
| 980 | _ | _ | |a UNRESTRICTED |
| Library | Collection | CLSMajor | CLSMinor | Language | Author |
|---|