TY - JOUR
T1 - An Empirical Approach to Derive Water T1 from Multiparametric MR Images Using an Automated Pipeline and Comparison With Liver Stiffness
AU - Michelotti, Filippo C.
AU - Kupriyanova, Yuliya
AU - Mori, Tim
AU - Küstner, Thomas
AU - Heilmann, Geronimo
AU - Bombrich, Maria
AU - Möser, Clara
AU - Schön, Martin
AU - Kuss, Oliver
AU - Roden, Michael
AU - Schrauwen-Hinderling, Vera B.
N1 - Funding Information:
We would like to thank the staff of the German Diabetes Center for their excellent support. We also thank Andrea Nagel, Franziska Paumen, Daniela Seeger, and Stefan Wierichts for valuable technical assistance. The authors would like to thank Julian Mevenkamp for the fruitful discussions. The GDS was initiated and financed by the German Diabetes Center (DDZ), which is funded by the German Federal Ministry of Health (Berlin, Germany) and the Ministry of Culture and Science of the State of Northrhine-Westphalia (Düsseldorf, Germany) and from the German Federal Ministry of Education and Research (BMBF) to the German Center for Diabetes Research (DZD e. V.). The GDS is supported in part by funds of the German Federal Ministry of Education and Research (BMBF) to the German Center for Diabetes Research (DZD e. V.). The research of M.R. is supported by grants from the Horizon Europe Framework Programme (HORIZON-HLTH-2022-STAYHLTH-02-01: Panel A) to the INTERCEPT-T2D consortium, EUREKA Eurostars-2 (E!-113230-DIA-PEP), the Deutsche Forschungsgemeinschaft (DFG; SFB/CRC1116, RTG/GRK 2576), the Schmutzler-Stiftung, and by the program “Profilbildung 2020,” an initiative of the Ministry of Culture and Science of the State of Northrhine-Westphalia and the Schmutzler Stiftung. The sole responsibility for the content of this publication lies with the authors. V.B.S.-H. was supported by the European Research Council starting grant (ERC; grant 759161 “MRS in diabetes”). T.K. was supported by the DFG under Germany's Excellence Strategy – EXC 2064/1 390727645 and EXC 2180 390900677. Open Access funding enabled and organized by Projekt DEAL.
Publisher Copyright:
© 2023 The Authors. Journal of Magnetic Resonance Imaging published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.
PY - 2024/4
Y1 - 2024/4
N2 - Background: Water T1 of the liver has been shown to be promising in discriminating the progressive forms of fatty liver diseases, inflammation, and fibrosis, yet proper correction for iron and lipid is required. Purpose: To examine the feasibility of an empirical approach for iron and lipid correction when measuring imaging-based T1 and to validate this approach by spectroscopy on in vivo data. Study Type: Retrospective. Population: Next to mixed lipid-iron phantoms, individuals with different hepatic lipid content were investigated, including people with type 1 diabetes (N = 15, %female = 15.6, age = 43.5 ± 14.0), or type 2 diabetes mellitus (N = 21, %female = 28.9, age = 59.8 ± 9.7) and healthy volunteers (N = 9, %female = 11.1, age = 58.0 ± 8.1). Field Strength/Sequences: 3 T, balanced steady-state free precession MOdified Look-Locker Inversion recovery (MOLLI), multi- and dual-echo gradient echo Dixon, gradient echo magnetic resonance elastography (MRE). Assessment: T1 values were measured in phantoms to determine the respective correction factors. The correction was tested in vivo and validated by proton magnetic resonance spectroscopy (1H-MRS). The quantification of liver T1 based on automatic segmentation was compared to the T1 values based on manual segmentation. The association of T1 with MRE-derived liver stiffness was evaluated. Statistical Tests: Bland–Altman plots and intraclass correlation coefficients (ICCs) were used for MOLLI vs. 1H-MRS agreement and to compare liver T1 values from automatic vs. manual segmentation. Pearson's r correlation coefficients for T1 with hepatic lipids and liver stiffness were determined. A P-value of 0.05 was considered statistically significant. Results: MOLLI T1 values after correction were found in better agreement with the 1H-MRS-derived water T1 (ICC = 0.60 [0.37; 0.76]) in comparison with the uncorrected T1 values (ICC = 0.18 [-0.09; 0.44]). Automatic quantification yielded similar liver T1 values (ICC = 0.9995 [0.9991; 0.9997]) as with manual segmentation. A significant correlation of T1 with liver stiffness (r = 0.43 [0.11; 0.67]) was found. A marked and significant reduction in the correlation strength of T1 with liver stiffness (r = 0.05 [-0.28; 0.38], P = 0.77) was found after correction for hepatic lipid content. Data Conclusion: Imaging-based correction factors enable accurate estimation of water T1 in vivo. Level of Evidence: 1. Technical Efficacy: Stage 1.
AB - Background: Water T1 of the liver has been shown to be promising in discriminating the progressive forms of fatty liver diseases, inflammation, and fibrosis, yet proper correction for iron and lipid is required. Purpose: To examine the feasibility of an empirical approach for iron and lipid correction when measuring imaging-based T1 and to validate this approach by spectroscopy on in vivo data. Study Type: Retrospective. Population: Next to mixed lipid-iron phantoms, individuals with different hepatic lipid content were investigated, including people with type 1 diabetes (N = 15, %female = 15.6, age = 43.5 ± 14.0), or type 2 diabetes mellitus (N = 21, %female = 28.9, age = 59.8 ± 9.7) and healthy volunteers (N = 9, %female = 11.1, age = 58.0 ± 8.1). Field Strength/Sequences: 3 T, balanced steady-state free precession MOdified Look-Locker Inversion recovery (MOLLI), multi- and dual-echo gradient echo Dixon, gradient echo magnetic resonance elastography (MRE). Assessment: T1 values were measured in phantoms to determine the respective correction factors. The correction was tested in vivo and validated by proton magnetic resonance spectroscopy (1H-MRS). The quantification of liver T1 based on automatic segmentation was compared to the T1 values based on manual segmentation. The association of T1 with MRE-derived liver stiffness was evaluated. Statistical Tests: Bland–Altman plots and intraclass correlation coefficients (ICCs) were used for MOLLI vs. 1H-MRS agreement and to compare liver T1 values from automatic vs. manual segmentation. Pearson's r correlation coefficients for T1 with hepatic lipids and liver stiffness were determined. A P-value of 0.05 was considered statistically significant. Results: MOLLI T1 values after correction were found in better agreement with the 1H-MRS-derived water T1 (ICC = 0.60 [0.37; 0.76]) in comparison with the uncorrected T1 values (ICC = 0.18 [-0.09; 0.44]). Automatic quantification yielded similar liver T1 values (ICC = 0.9995 [0.9991; 0.9997]) as with manual segmentation. A significant correlation of T1 with liver stiffness (r = 0.43 [0.11; 0.67]) was found. A marked and significant reduction in the correlation strength of T1 with liver stiffness (r = 0.05 [-0.28; 0.38], P = 0.77) was found after correction for hepatic lipid content. Data Conclusion: Imaging-based correction factors enable accurate estimation of water T1 in vivo. Level of Evidence: 1. Technical Efficacy: Stage 1.
KW - liver fibrosis
KW - liver inflammation
KW - multiparametric MRI
KW - water T 1
U2 - 10.1002/jmri.28906
DO - 10.1002/jmri.28906
M3 - Article
C2 - 37530755
SN - 1053-1807
VL - 59
SP - 1193
EP - 1203
JO - Journal of Magnetic Resonance Imaging
JF - Journal of Magnetic Resonance Imaging
IS - 4
ER -