000167750 001__ 167750
000167750 005__ 20240303004611.0
000167750 0247_ $$2doi$$a10.1088/1361-6560/abeae7
000167750 0247_ $$2pmid$$apmid:33647894
000167750 0247_ $$2ISSN$$a0031-9155
000167750 0247_ $$2ISSN$$a1361-6560
000167750 0247_ $$2altmetric$$aaltmetric:157524861
000167750 037__ $$aDKFZ-2021-00514
000167750 041__ $$aEnglish
000167750 082__ $$a530
000167750 1001_ $$00000-0001-8879-1895$$aMetzner, Selma$$b0
000167750 245__ $$aBayesian uncertainty quantification for magnetic resonance fingerprinting.
000167750 260__ $$aBristol$$bIOP Publ.$$c2021
000167750 3367_ $$2DRIVER$$aarticle
000167750 3367_ $$2DataCite$$aOutput Types/Journal article
000167750 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1642519537_14848
000167750 3367_ $$2BibTeX$$aARTICLE
000167750 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000167750 3367_ $$00$$2EndNote$$aJournal Article
000167750 500__ $$aPhys. Med. Biol. 66 (2021) 075006 
000167750 520__ $$aMagnetic Resonance Fingerprinting (MRF) is a promising technique for fast quantitative imaging of human tissue. In general, MRF is based on a sequence of highly undersampled MR images which are analyzed with a pre-computed dictionary. MRF provides valuable diagnostic parameters such as the $T_1$ and $T_2$ MR relaxation times. However, uncertainty characterization of dictionary-based MRF estimates for $T_1$ and $T_2$ has not been achieved so far, which makes it challenging to assess if observed differences in these estimates are significant and may indicate pathological changes of the underlying tissue. We propose a Bayesian approach for the uncertainty quantification of dictionary-based MRF which leads to probability distributions for $T_1$ and $T_2$ in every voxel. The distributions can be used to make probability statements about the relaxation times, and to assign uncertainties to their dictionary-based MRF estimates. All uncertainty calculations are based on the pre-computed dictionary and the observed sequence of undersampled MR images, and they can be calculated in short time. The approach is explored by analyzing MRF measurements of a phantom consisting of several tubes across which MR relaxation times are constant. The proposed uncertainty quantification is quantitatively consistent with the observed within-tube variability of estimated relaxation times. Furthermore, calculated uncertainties are shown to characterize well observed differences between the MRF estimates and the results obtained from high-accurate reference measurements. These findings indicate that a reliable uncertainty quantification is achieved. We also present results for simulated MRF data and an uncertainty quantification for an in vivo MRF measurement. MATLAB$^{\scriptsize \text{\textregistered}}$ source code implementing the proposed approach is made available.
000167750 536__ $$0G:(DE-HGF)POF4-315$$a315 - Bildgebung und Radioonkologie (POF4-315)$$cPOF4-315$$fPOF IV$$x0
000167750 588__ $$aDataset connected to CrossRef, PubMed,
000167750 650_7 $$2Other$$aBayesian inference
000167750 650_7 $$2Other$$aMRF
000167750 650_7 $$2Other$$auncertainty
000167750 7001_ $$aWuebbeler, Gerd$$b1
000167750 7001_ $$0P:(DE-He78)6f29c4a184536f50b8629af3480c5932$$aFlassbeck, Sebastian$$b2
000167750 7001_ $$aGatefait, Constance$$b3
000167750 7001_ $$aKolbitsch, Christoph$$b4
000167750 7001_ $$aElster, Clemens$$b5
000167750 773__ $$0PERI:(DE-600)1473501-5$$a10.1088/1361-6560/abeae7$$p075006 $$tPhysics in medicine and biology$$v66$$x1361-6560$$y2021
000167750 909CO $$ooai:inrepo02.dkfz.de:167750$$pVDB
000167750 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)6f29c4a184536f50b8629af3480c5932$$aDeutsches Krebsforschungszentrum$$b2$$kDKFZ
000167750 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
000167750 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
000167750 9141_ $$y2021
000167750 915__ $$0StatID:(DE-HGF)0420$$2StatID$$aNationallizenz$$d2021-01-28$$wger
000167750 915__ $$0StatID:(DE-HGF)0430$$2StatID$$aNational-Konsortium$$d2021-01-28$$wger
000167750 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2021-01-28
000167750 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2021-01-28
000167750 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2021-01-28
000167750 915__ $$0StatID:(DE-HGF)1030$$2StatID$$aDBCoverage$$bCurrent Contents - Life Sciences$$d2021-01-28
000167750 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2021-01-28
000167750 915__ $$0StatID:(DE-HGF)1050$$2StatID$$aDBCoverage$$bBIOSIS Previews$$d2021-01-28
000167750 915__ $$0StatID:(DE-HGF)1190$$2StatID$$aDBCoverage$$bBiological Abstracts$$d2021-01-28
000167750 915__ $$0StatID:(DE-HGF)0113$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2021-01-28
000167750 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2021-01-28
000167750 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bPHYS MED BIOL : 2019$$d2021-01-28
000167750 915__ $$0StatID:(DE-HGF)0600$$2StatID$$aDBCoverage$$bEbsco Academic Search$$d2021-01-28
000167750 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bASC$$d2021-01-28
000167750 915__ $$0StatID:(DE-HGF)9900$$2StatID$$aIF < 5$$d2021-01-28
000167750 9201_ $$0I:(DE-He78)E020-20160331$$kE020$$lE020 Med. Physik in der Radiologie$$x0
000167750 980__ $$ajournal
000167750 980__ $$aVDB
000167750 980__ $$aI:(DE-He78)E020-20160331
000167750 980__ $$aUNRESTRICTED