| Zugriffsnummer | 45720 |
| Dokumenttyp | Zeitschriftenartikel |
| Peer Review | mit Peer Review |
| Sprache | Englisch |
| Titel | Bayesian uncertainty quantification for magnetic resonance fingerprinting |
| Autor(in); Institution |
Metzner, Selma; 8.4, Mathematische Modellierung und Datenanalyse, PTB-Berlin
Wübbeler, Gerd; 8.4, Mathematische Modellierung und Datenanalyse, PTB-Berlin
Elster, Clemens; 8.4, Mathematische Modellierung und Datenanalyse, PTB-Berlin
Gatefait, Constance; 8.1, Biomedizinische Magnetresonanz, PTB-Berlin; 8.4, Mathematische Modellierung und Datenanalyse, PTB-Berlin
Kolbitsch, Christoph; 8.1, Biomedizinische Magnetresonanz, PTB-Berlin
Flassbeck, Sebastian; Department of Radiology, Center for Biomedical Imaging, New York University School of Medicine, New York, USA; Center for Advanced Imaging Innovation and Research, New York University School of Medicine, New York, USA
|
| Quelle/Jahr | Physics in Medicine and Biology: 66 (2021), 18 S. |
| Artikelnummer | 075006 |
| ISSN | 0031-9155 (print) ; 1361-6560 (online) |
| DOI | |
| Verlag | Bristol: IOP Publishing |
| Freie Schlagworte | MRF ; Bayesian inference ; uncertainty |
| Zusammenfassung | Magnetic 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 T1 and T2 MR relaxation times. However, uncertainty characterization of dictionary-based MRF estimates for T1 and T2 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 T1 and T2 in every voxel. The distributions can be used to make probability statements about the relaxation times, and to assign uncertainties to their dictionary-based MR festimates. All uncertainty calculateions 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 MR festimates 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® source code implementing the proposed approach is made available. |
| Kostenfreier Zugang | Open Access Hybrid |
| Rechteinformation | CC BY 4.0 ; Creative Commons Attribution 4.0 License |
| Themenbereich der Metrologie | Mathematik und metrologische Informationstechnik |
| Forschungsprojekt | 18HLT05: QUIERO: Quantitative MR-based imaging of physical biomarkers |
| Förderinformationen (1) |
Förderername: European Commission (EC)
Förderer ID: 0000 0001 2242 8989 Förderer ID Typ: ISNI Förderprogramm: EMPIR 2018 Health Titel der Förderung: 18HLT05: QUIERO: Quantitative MR-based imaging of physical biomarkers Förderungsnummer: 18HLT05 |