Funding: This work was supported by National Natural Science Foundation of China (Grant 82171927), the Beijing Natural Science Foundation (Grant 7212126), and the Beijing New Health Industry Development Foundation (Grant XM2020-02-006).

Yali Li, Dan Jin, and Suwei Liu contributed equally to this work.

Read the full text

About

PDF

Tools

Share a link

Email
Wechat
Bluesky

ABSTRACT

Background

Proton density fat fraction (PDFF) and R2* are noninvasive MRI biomarkers for quantifying fat and iron in abdominal organs. While 3.0 T MRI is widely used clinically, 5.0 T may offer improved accuracy and reliability. With the increasing availability of 5.0 T, assessing its feasibility and utility for quantifying PDFF and R2* in abdominal organs is needed.

Purpose

To determine the agreement of the PDFF and R2* in the liver, pancreas, kidney, and paraspinal muscle at 2 different field strengths.

Study Type

Prospective.

Population

A total of 127 participants, including 60 healthy volunteers and 67 with metabolic dysfunction-associated steatotic liver disease (100 women and 27 men, 52 ± 9 years old).

Field Strength/Sequence

3.0 T and 5.0 T; Chemical shift-encoded multi-echo gradient echo sequence.

Assessment

PDFF and R2* values in the liver, pancreas, kidney, and paraspinal muscle were measured by three observers to evaluate the interobserver and interfield agreement using two 3.0 T scanners (scanner 1 and scanner 2) and one 5.0 T scanner.

Statistical Tests

Linear regression, Bland–Altman analyses, and intraclass correlation coefficients (ICCs). The p-value < 0.05 indicated statistically significant.

Results

PDFF and R2* at 5.0 T were strongly correlated with those at two 3.0 T scanners for all organs (R ² = 0.905–0.998 for PDFF; 0.831–0.991 for R2*), indicating high interfield ICCs (0.892–0.993 for PDFF; 0.910–0.981 for R2*). Comparisons between 5.0 T and two 3.0 T scanners showed good interfield agreement for PDFF (mean bias, −0.57% to 0.03%) while a higher bias for R2* (mean bias, −16.53 to −2.64 s⁻¹, 95% LoA, −34.28 to 1.21 s⁻¹) at 5.0 T compared to the comparison between 3T scanners.

Data Conclusion

5.0 T revealed high interfield agreement between the PDFF and R2* with 3.0 T, which could provide reliable quantification of fat and iron contents in abdominal organs.

Evidence Level

Technical Efficacy

Stage 2.

References

1J. C. Bleeker, G. Visser, F. A. Wijburg, et al., “Severe Fat Accumulation in Multiple Organs in Pediatric Autopsies: An Uncommon but Significant Finding,” Pediatric and Developmental Pathology 20, no. 4 (2017): 269–276.
10.1177/1093526617691708
PubMed Web of Science® Google Scholar
2P. Dongiovanni, A. L. Fracanzani, S. Fargion, and L. Valenti, “Iron in Fatty Liver and in the Metabolic Syndrome: A Promising Therapeutic Target,” Journal of Hepatology 55, no. 4 (2011): 920–932.
10.1016/j.jhep.2011.05.008
CAS PubMed Web of Science® Google Scholar
3G. C. Farrell and C. Z. Larter, “Nonalcoholic Fatty Liver Disease: From Steatosis to Cirrhosis,” Hepatology 43, no. 2 Suppl 1 (2006): S99–S112.
10.1002/hep.20973
CAS PubMed Web of Science® Google Scholar
4F. Negro and A. J. Sanyal, “Hepatitis C Virus, Steatosis and Lipid Abnormalities: Clinical and Pathogenic Data,” Liver International 29, no. Suppl 2 (2009): 26–37.
10.1111/j.1478-3231.2008.01950.x
CAS PubMed Web of Science® Google Scholar
5V. W. Setiawan, D. O. Stram, J. Porcel, et al., “Prevalence of Chronic Liver Disease and Cirrhosis by Underlying Cause in Understudied Ethnic Groups: The Multiethnic Cohort,” Hepatology 64, no. 6 (2016): 1969–1977.
10.1002/hep.28677
PubMed Web of Science® Google Scholar
6M. Ekstedt, L. E. Franzén, U. L. Mathiesen, et al., “Long-Term Follow-Up of Patients With NAFLD and Elevated Liver Enzymes,” Hepatology 44, no. 4 (2006): 865–873.
10.1002/hep.21327
CAS PubMed Web of Science® Google Scholar
7J. M. Alústiza, A. Castiella, M. D. De Juan, et al., “Iron Overload in the Liver Diagnostic and Quantification,” European Journal of Radiology 61, no. 3 (2007): 499–506.
10.1016/j.ejrad.2006.11.012
PubMed Web of Science® Google Scholar
8E. Angelucci, G. M. Brittenham, C. E. McLaren, et al., “Hepatic Iron Concentration and Total Body Iron Stores in Thalassemia Major,” New England Journal of Medicine 343, no. 5 (2000): 327–331.
10.1056/NEJM200008033430503
CAS PubMed Web of Science® Google Scholar
9L. J. Britton, V. N. Subramaniam, and D. H. Crawford, “Iron and Non-alcoholic Fatty Liver Disease,” World Journal of Gastroenterology 22, no. 36 (2016): 8112–8122.
10.3748/wjg.v22.i36.8112
CAS PubMed Web of Science® Google Scholar
10X. Wang, X. Zhang, L. Ma, and S. Li, “Simultaneous Quantification of Hepatic MRI-PDFF and R2* in a Rabbit Model With Nonalcoholic Fatty Liver Disease,” Science China. Life Sciences 61, no. 9 (2018): 1107–1114.
10.1007/s11427-017-9279-1
CAS PubMed Web of Science® Google Scholar
11P. Bedossa, D. Dargère, and V. Paradis, “Sampling Variability of Liver Fibrosis in Chronic Hepatitis C,” Hepatology 38, no. 6 (2003): 1449–1457.
10.1016/j.hep.2003.09.022
PubMed Web of Science® Google Scholar
12D. Joy, V. R. Thava, and B. B. Scott, “Diagnosis of Fatty Liver Disease: Is Biopsy Necessary?,” European Journal of Gastroenterology & Hepatology 15, no. 5 (2003): 539–543.
10.1097/01.meg.0000059112.41030.2e
PubMed Web of Science® Google Scholar
13V. Ratziu, F. Charlotte, A. Heurtier, et al., “Sampling Variability of Liver Biopsy in Nonalcoholic Fatty Liver Disease,” Gastroenterology 128, no. 7 (2005): 1898–1906.
10.1053/j.gastro.2005.03.084
PubMed Web of Science® Google Scholar
14C. E. Fonvig, E. Chabanova, J. D. Ohrt, et al., “Multidisciplinary Care of Obese Children and Adolescents for One Year Reduces Ectopic Fat Content in Liver and Skeletal Muscle,” BMC Pediatrics 15 (2015): 196.
10.1186/s12887-015-0513-6
PubMed Web of Science® Google Scholar
15B. Gaborit, I. Abdesselam, F. Kober, et al., “Ectopic Fat Storage in the Pancreas Using 1H-MRS: Importance of Diabetic Status and Modulation With Bariatric Surgery-Induced Weight Loss,” International Journal of Obesity 39, no. 3 (2015): 480–487.
10.1038/ijo.2014.126
CAS Web of Science® Google Scholar
16İ. S. İdilman, F. Gümrük, M. Haliloğlu, et al., “The Feasibility of Magnetic Resonance Imaging for Quantification of Liver, Pancreas, Spleen, Vertebral Bone Marrow, and Renal Cortex R2* and Proton Density Fat Fraction in Transfusion-Related Iron Overload,” Turkish Journal of Haematology 33, no. 1 (2016): 21–27.
10.4274/tjh.2015.0142
CAS PubMed Web of Science® Google Scholar
17A. M. Li, D. Chan, C. K. Li, E. Wong, Y. L. Chan, and T. F. Fok, “Respiratory Function in Patients With Thalassaemia Major: Relation With Iron Overload,” Archives of Disease in Childhood 87, no. 4 (2002): 328–330.
10.1136/adc.87.4.328
CAS PubMed Web of Science® Google Scholar
18N. Ueda and K. Takasawa, “Impact of Inflammation on Ferritin, Hepcidin and the Management of Iron Deficiency Anemia in Chronic Kidney Disease,” Nutrients 10, no. 9 (2018): 1173, https://doi.org/10.3390/nu10091173.
10.3390/nu10091173
PubMed Web of Science® Google Scholar
19T. Armstrong, X. Zhong, S. F. Shih, et al., “Free-Breathing 3D Stack-Of-Radial MRI Quantification of Liver Fat and R(2)* in Adults With Fatty Liver Disease,” Magnetic Resonance Imaging 85 (2022): 141–152.
10.1016/j.mri.2021.10.016
CAS PubMed Web of Science® Google Scholar
20J. S. Hankins, M. B. McCarville, R. B. Loeffler, et al., “R2* Magnetic Resonance Imaging of the Liver in Patients With Iron Overload,” Blood 113, no. 20 (2009): 4853–4855.
10.1182/blood-2008-12-191643
CAS PubMed Web of Science® Google Scholar
21M. Obrzut, V. Atamaniuk, K. J. Glaser, et al., “Value of Liver Iron Concentration in Healthy Volunteers Assessed by MRI,” Scientific Reports 10, no. 1 (2020): 17887.
10.1038/s41598-020-74968-z
CAS PubMed Web of Science® Google Scholar
22Y. Zhang, C. Yang, L. Liang, et al., “Preliminary Experience of 5.0 T Higher Field Abdominal Diffusion-Weighted MRI: Agreement of Apparent Diffusion Coefficient With 3.0 T Imaging,” Journal of Magnetic Resonance Imaging 56, no. 4 (2022): 1009–1017.
10.1002/jmri.28097
PubMed Web of Science® Google Scholar
23J. Liu, Z. Wang, D. Yu, et al., “Comparative Analysis of Hepatic Fat Quantification Across 5 T, 3 T and 1.5 T: A Study on Consistency and Feasibility,” European Journal of Radiology 180 (2024): 111709.
10.1016/j.ejrad.2024.111709
PubMed Web of Science® Google Scholar
24J. C. Wood, C. Enriquez, N. Ghugre, et al., “MRI R2 and R2* Mapping Accurately Estimates Hepatic Iron Concentration in Transfusion-Dependent Thalassemia and Sickle Cell Disease Patients,” Blood 106, no. 4 (2005): 1460–1465.
10.1182/blood-2004-10-3982
CAS PubMed Web of Science® Google Scholar
25T. K. Koo and M. Y. Li, “A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research,” Journal of Chiropractic Medicine 15, no. 2 (2016): 155–163.
10.1016/j.jcm.2016.02.012
PubMed Web of Science® Google Scholar
26L. Zheng, C. Yang, L. Liang, S. Rao, Y. Dai, and M. Zeng, “T2-Weighted MRI and Reduced-FOV Diffusion-Weighted Imaging of the Human Pancreas at 5 T: A Comparison Study With 3 T,” Medical Physics 50, no. 1 (2023): 344–353.
10.1002/mp.15970
CAS PubMed Web of Science® Google Scholar
27A. de Boer, J. M. Hoogduin, P. J. Blankestijn, et al., “7 T Renal MRI: Challenges and Promises,” Magma 29, no. 3 (2016): 417–433.
10.1007/s10334-016-0538-3
PubMed Google Scholar
28A. A. Harteveld, A. G. van der Kolk, J. J. Zwanenburg, et al., “7-T MRI in Cerebrovascular Diseases: Challenges to Overcome and Initial Results,” Topics in Magnetic Resonance Imaging 25, no. 2 (2016): 89–100.
10.1097/RMR.0000000000000080
PubMed Google Scholar
29B. Vachha and S. Y. Huang, “MRI With Ultrahigh Field Strength and High-Performance Gradients: Challenges and Opportunities for Clinical Neuroimaging at 7 T and Beyond,” European Radiology Experimental 5, no. 1 (2021): 35.
10.1186/s41747-021-00216-2
PubMed Web of Science® Google Scholar
30Q. X. Yang, J. Wang, X. Zhang, et al., “Analysis of Wave Behavior in Lossy Dielectric Samples at High Field,” Magnetic Resonance in Medicine 47, no. 5 (2002): 982–989.
10.1002/mrm.10137
PubMed Web of Science® Google Scholar
31M. E. Ladd, P. Bachert, M. Meyerspeer, et al., “Pros and Cons of Ultra-High-Field MRI/MRS for Human Application,” Progress in Nuclear Magnetic Resonance Spectroscopy 109 (2018): 1–50.
10.1016/j.pnmrs.2018.06.001
CAS PubMed Web of Science® Google Scholar
32J. Ma, “Dixon Techniques for Water and Fat Imaging,” Journal of Magnetic Resonance Imaging 28, no. 3 (2008): 543–558.
10.1002/jmri.21492
CAS PubMed Web of Science® Google Scholar
33R. Kořínek, L. Pfleger, K. Eckstein, et al., “Feasibility of Hepatic Fat Quantification Using Proton Density Fat Fraction by Multi-Echo Chemical-Shift-Encoded MRI at 7T,” Frontiers of Physics 9 (2021): 665562.
10.3389/fphy.2021.665562
PubMed Web of Science® Google Scholar
34Y. Zhang, R. Sheng, C. Yang, et al., “Higher Field Reduced FOV Diffusion-Weighted Imaging for Abdominal Imaging at 5.0 Tesla: Image Quality Evaluation Compared With 3.0 Tesla,” Insights Into Imaging 14, no. 1 (2023): 171.
10.1186/s13244-023-01513-7
CAS PubMed Web of Science® Google Scholar
35G. Simchick, R. Zhao, Q. Yuan, et al., “Practical Application of Multivendor MRI-Based R2* Mapping for Liver Iron Quantification at 1.5 T and 3.0 T,” Journal of Magnetic Resonance Imaging 61, no. 1 (2024): 150–165, https://doi.org/10.1002/jmri.29401.
10.1002/jmri.29401
PubMed Web of Science® Google Scholar
36G. Simchick, Z. Liu, T. Nagy, M. Xiong, and Q. Zhao, “Assessment of MR-Based R2* and Quantitative Susceptibility Mapping for the Quantification of Liver Iron Concentration in a Mouse Model at 7T,” Magnetic Resonance in Medicine 80, no. 5 (2018): 2081–2093.
10.1002/mrm.27173
CAS PubMed Web of Science® Google Scholar
37D. Hernando, R. J. Cook, N. Qazi, C. A. Longhurst, C. A. Diamond, and S. B. Reeder, “Complex Confounder-Corrected R2* Mapping for Liver Iron Quantification With MRI,” European Radiology 31, no. 1 (2021): 264–275.
10.1007/s00330-020-07123-x
PubMed Web of Science® Google Scholar
38M. P. Luttje, L. D. van Buuren, P. R. Luijten, M. van Vulpen, U. A. van der Heide, and D. W. J. Klomp, “Towards Intrinsic R2* Imaging in the Prostate at 3 and 7tesla,” Magnetic Resonance Imaging 42 (2017): 16–21.
10.1016/j.mri.2017.04.014
PubMed Web of Science® Google Scholar
39P. Storey, A. A. Thompson, C. L. Carqueville, J. C. Wood, R. A. de Freitas, and C. K. Rigsby, “R2* Imaging of Transfusional Iron Burden at 3T and Comparison With 1.5T,” Journal of Magnetic Resonance Imaging 25, no. 3 (2007): 540–547.
10.1002/jmri.20816
CAS PubMed Web of Science® Google Scholar
40K. R. Thulborn, S. Y. Chang, G. X. Shen, and J. T. Voyvodic, “High-Resolution Echo-Planar fMRI of Human Visual Cortex at 3.0 Tesla,” NMR in Biomedicine 10, no. 4–5 (1997): 183–190.
10.1002/(SICI)1099-1492(199706/08)10:4/5<183::AID-NBM469>3.0.CO;2-W
CAS PubMed Web of Science® Google Scholar
41A. M. Peters, M. J. Brookes, F. G. Hoogenraad, et al., “T2* Measurements in Human Brain at 1.5, 3 and 7 T,” Magnetic Resonance Imaging 25, no. 6 (2007): 748–753.
10.1016/j.mri.2007.02.014
PubMed Web of Science® Google Scholar
42C. Triantafyllou, R. D. Hoge, G. Krueger, et al., “Comparison of Physiological Noise at 1.5 T, 3 T and 7 T and Optimization of fMRI Acquisition Parameters,” NeuroImage 26, no. 1 (2005): 243–250.
10.1016/j.neuroimage.2005.01.007
CAS PubMed Web of Science® Google Scholar
43R. R. Regatte and M. E. Schweitzer, “Ultra-High-Field MRI of the Musculoskeletal System at 7.0T,” Journal of Magnetic Resonance Imaging 25, no. 2 (2007): 262–269.
10.1002/jmri.20814
PubMed Web of Science® Google Scholar
44S. D. Serai, J. R. Dillman, and A. T. Trout, “Proton Density Fat Fraction Measurements at 1.5- and 3-T Hepatic MR Imaging: Same-Day Agreement Among Readers and Across Two Imager Manufacturers,” Radiology 284, no. 1 (2017): 244–254.
10.1148/radiol.2017161786
PubMed Web of Science® Google Scholar
45G. H. Kang, I. Cruite, M. Shiehmorteza, et al., “Reproducibility of MRI-Determined Proton Density Fat Fraction Across Two Different MR Scanner Platforms,” Journal of Magnetic Resonance Imaging 34, no. 4 (2011): 928–934.
10.1002/jmri.22701
PubMed Web of Science® Google Scholar
46H. J. Kim, H. J. Cho, B. Kim, et al., “Accuracy and Precision of Proton Density Fat Fraction Measurement Across Field Strengths and Scan Intervals: A Phantom and Human Study,” Journal of Magnetic Resonance Imaging 50, no. 1 (2019): 305–314.
10.1002/jmri.26575
PubMed Web of Science® Google Scholar
47L. Athithan, G. S. Gulsin, M. J. House, et al., “A Comparison of Liver Fat Fraction Measurement on MRI at 3T and 1.5T,” PLoS One 16, no. 7 (2021): e0252928.
10.1371/journal.pone.0252928
CAS PubMed Web of Science® Google Scholar
48N. C. Andrews, “Disorders of Iron Metabolism,” New England Journal of Medicine 341, no. 26 (1999): 1986–1995.
10.1056/NEJM199912233412607
CAS PubMed Web of Science® Google Scholar
49B. Wu, W. Han, Z. Li, et al., “Reproducibility of Intra- and Inter-Scanner Measurements of Liver Fat Using Complex Confounder-Corrected Chemical Shift Encoded MRI at 3.0 Tesla,” Scientific Reports 6 (2016): 19339.
10.1038/srep19339
CAS PubMed Web of Science® Google Scholar
50E. Pickles, S. Kumar, M. Brady, et al., “Comparison of Liver Iron Concentration Calculated From R2* at 1.5 T and 3 T,” Abdominal Radiology 48, no. 3 (2023): 865–873.
PubMed Web of Science® Google Scholar

Citing Literature

Volume62, Issue2

August 2025

Pages 551-560

Quantification of the Proton Density Fat Fraction and Iron Content: A Comparative Study Between 3.0 T and 5.0 T MRI

ABSTRACT

Background

Purpose

Study Type

Population

Field Strength/Sequence

Assessment

Statistical Tests

Results

Data Conclusion

Evidence Level

Technical Efficacy

References

Citing Literature

References

Information

About Wiley Online Library

Help & Support

Opportunities

Connect with Wiley

Quantification of the Proton Density Fat Fraction and Iron Content: A Comparative Study Between 3.0 T and 5.0 T MRI

ABSTRACT

Background

Purpose

Study Type

Population

Field Strength/Sequence

Assessment

Statistical Tests

Results

Data Conclusion

Evidence Level

Technical Efficacy

References

Citing Literature

References

Related

Information