The DOI matches its Crossref record; 118 of the 118 references it cites were found in a registry or an index.
Quantification of Liver Fat Content with CT and MRI: State of the Art
Open accessAbstract
Hepatic steatosis is defined as pathologically elevated liver fat content and has many underlying causes. Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease worldwide, with an increasing prevalence among adults and children. Abnormal liver fat accumulation has serious consequences, including cirrhosis, liver failure, and hepatocellular carcinoma. In addition, hepatic steatosis is increasingly recognized as an independent risk factor for the metabolic syndrome, type 2 diabetes, and, most important, cardiovascular mortality. During the past 2 decades, noninvasive imaging-based methods for the evaluation of hepatic steatosis have been developed and disseminated. Chemical shift-encoded MRI is now established as the most accurate and precise method for liver fat quantification. CT is important for the detection and quantification of incidental steatosis and may play an increasingly prominent role in risk stratification, particularly with the emergence of CT-based screening and artificial intelligence. Quantitative imaging methods are increasingly used for diagnostic work-up and management of steatosis, including treatment monitoring. The purpose of this state-of-the-art review is to provide an overview of recent progress and current state of the art for liver fat quantification using CT and MRI, as well as important practical considerations related to clinical implementation.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“MRI can be used to quantify the triglyceride content (usually expressed as proton density fat fraction [PDFF]) in the liver and is the non-invasive gold standard for hepatic lipid quanti fi cation in MASLD.”
From EASL-EASD-EASO Clinical Practice Guidelines on the management of metabolic dysfunction-associated steatotic liver disease (MASLD). (2024) · cites this paper as [162]
In the citing paper
“While CT scans obtained in clinical routine (usually performed for other purposes) can provide a rough estimate of liver steatosis and thereby suggest SLD, 162 this method is not suitable for proper assessment of steatosis.”
From EASL-EASD-EASO Clinical Practice Guidelines on the management of metabolic dysfunction-associated steatotic liver disease (MASLD). (2024) · cites this paper as [162]
Citations
Cites
Younossi, Z. M., Koenig, A. B., Abdelatif, D., Fazel, Y., Henry, L., & Wymer, M. (2016) · Hepatology · doi:10.1002/hep.28431 · Cited by 10,781
Chalasani, N., Younossi, Z., Lavine, J. E., Charlton, M., Cusi, K., Rinella, M., Harrison, S. A., Brunt, E. M., & Sanyal, A. J. (2018) · Hepatology · doi:10.1002/hep.29367 · Cited by 7,239
Nonalcoholic Steatohepatitis: A Proposal for Grading and Staging The Histological Lesions
Brunt, E. M., Janney, C. G., Di Bisceglie, A. M., Neuschwander-Tetri, B. A., & Bacon, B. R. (1999) · American Journal of Gastroenterology · doi:10.1111/j.1572-0241.1999.01377.x · Cited by 3,907
MAFLD: a consensus-driven proposed nomenclature for metabolic associated fatty liver disease
Eslam, M., Sanyal, A. J., George, J., Sanyal, A., Neuschwander-Tetri, B., Tiribelli, C., Kleiner, D. E., Brunt, E., Bugianesi, E., Yki-Järvinen, H., et al. (2020) · Gastroenterology · doi:10.1053/j.gastro.2019.11.312 · Cited by 3,286
Estes, C., Razavi, H. A., Loomba, R. S., Younossi, Z. M., & Sanyal, A. J. (2017) · Hepatology · doi:10.1002/hep.29466 · Cited by 2,496
The utility of radiological imaging in nonalcoholic fatty liver disease
Saadeh, S. N., Younossi, Z. M., Remer, E. M., Gramlich, T., Ong, J. P., Hurley, M. E., Mullen, K. D., Cooper, J. N., & Sheridan, M. J. (2002) · Gastroenterology · doi:10.1053/gast.2002.35354 · Cited by 2,112
Sampling Variability of Liver Biopsy in Nonalcoholic Fatty Liver Disease
Ratziu, V., Charlotte, F., Heurtier, A., Gombert, S., Giral, P., Bruckert, E., Grimaldi, A., Capron, F., & Poynard, T. (2005) · Gastroenterology · doi:10.1053/j.gastro.2005.03.084 · Cited by 2,106
Szczepaniak, L. S., Nurenberg, P., Leonard, D. S., Browning, J. D., Reingold, J. S., Grundy, S. M., Hobbs, H. H., & Dobbins, R. L. (2004) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.00064.2004 · Cited by 1,573
Prevalence of Fatty Liver in Children and Adolescents
Schwimmer, J. B., Deutsch, R., Kahen, T., Lavine, J. E., Stanley, C. R., & Behling, C. A. (2006) · PEDIATRICS · doi:10.1542/peds.2006-1212 · Cited by 1,477
Adinolfi, L. E., Gambardella, M., Andreana, A., Tripodi, M., Utili, R., & Ruggiero, G. (2001) · Hepatology · doi:10.1053/jhep.2001.24432 · Cited by 1,052
Flagged first, then by citations
Research works printed but not linked: 2
- Bush EC, 2018, J Vis Exp, 2018, 9
- Chen X, 2017, Eur J Radiol, 94, 179
Cited by
Sorted by citations
Items 301–321 of 321
Ohtani, T., Shimada, M., Nishiyama, K., Shimoyama, H., Ueda, M., Hakoda, S., Tomita, Y., Takata, K., Tateishi, T., & Tsujikawa, T. (2026) · Academic Radiology · doi:10.1016/j.acra.2026.06.064
Associations of Helicobacter pylori infection with NAFLD, MAFLD, MASLD, and Chinese MAFLD
Yu, H., Gao, Y., Wang, X., Chen, L., Zhang, Y., Li, H., Méndez‐Sánchez, N., & Qi, X. (2026) · Therapeutic Advances in Gastroenterology · doi:10.1177/17562848261465720
Николаевич, С. Ю., Труфанов, Г. Е., Фокин, В. А., Ionova, E. A., Savchenkova, A. P., Робертовна, М. А., Бодрова, О. В., & Boykov, I. V. (2026) · Russian Military Medical Academy Reports · doi:10.17816/rmmar701080
Wu, X., Li, J., Deng, R., Xu, X., Zhou, J., Chong, H., Li, W., Wu, P., Chen, Y., Yang, Y., et al. (2026) · Radiology · doi:10.1148/radiol.253979
Mora, R., Irannejad, K., Abbas, N., Mogga, P., Iskander, B., Hubbard, L., Roy, S. K., Lakshmanan, S., Budoff, M. J., & Krishnan, S. (2026) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2026.1850945
“…for hepatic steatosis, and correlative studies show that both CT attenuation and MRI proton density fat fraction (MRI-PDFF) track histologic fat content closely, with MRI-PDFF providing the most accurate and reproducible noninvasive quantification across the full spectrum of steatosis (87).”
Wang, K., Han, R., Huang, Y., Cai, J., & Wu, X. (2026) · Frontiers in Endocrinology · doi:10.3389/fendo.2026.1915819
Hasan, S., Deriche, M., Rezaeieh, S. A., & Abbosh, A. M. (2026) · European Conference on Antennas and Propagation · doi:10.23919/eucap68105.2026.11612287
“Computed tomography (CT) also poses risks due to ionizing radiation [9].”
Normative values for quantitative MRI biomarkers of the liver in collegiate athletes
Anagnostopoulos, A. A., Heidenreich, J. F., Roshanshad, A., Tamada, D., Panagiotopoulos, N., Mao, L., Hernando, D., Reeder, S. B., & Starekova, J. (2026) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2026.110772
Nazli, S., Shaaban, A., Patil, A., Smith, L., Stetzik, L. A., Hasan, B., Elhalaby, R. E., COSTA, A. V., Sivasubramaniam, P., Park, B. U., et al. (2026) · Histopathology · doi:10.1111/his.70272
Wu, Y., Cheng, J., Xu, D., Wang, K., Dou, Z., He, N., & Wang, J. (2026) · Frontiers in Nutrition · doi:10.3389/fnut.2026.1867062
“Hepatic steatosis was defined primarily as MRI-PDFF of 5.0% or greater, consistent with the conventional boundary for abnormal liver fat; alternative definitions of 5.2, 5.5, and 6.0% were examined in sensitivity analyses (7, 16).”
InfluentialXu, X., Yuan, J., Cai, X., Deng, R., Li, J., Wu, X., Chong, H., Ma, H., Yang, Y., Chen, Y., et al. (2026) · Academic Radiology · doi:10.1016/j.acra.2026.08.094
Góngora-Chan, P., Jimeno-Figueroa, R. E., Williams-Jacquez, D., Chaurand-Lara, M., Solis, A. L. G., Ávila-Nava, A., Chim-Aké, R., Valdivieso‐Jiménez, J. A., Torres-Erazo, D. S., Vera, I., et al. (2026) · Diagnostics · doi:10.3390/diagnostics16182996
“In contrast, volumetric MRI-based approaches, particularly chemical shift-encoded MRI (CSE-MRI) with PDFF mapping, enable assessment of fat content across the entire liver and may therefore provide a more comprehensive characterization of hepatic steatosis [32].”
Opportunistic CT Screening: Clinical Applications, Technical Foundations, and Best Practices
Dogra, S., Lew, C. O., Bussey, O., Gyftopoulos, S., Bredella, M. A., Recht, M. P., & Dane, B. R. (2026) · Radiographics · doi:10.1148/rg.260009
Uniform quantification of hepatic fat fraction across body sizes using photon-counting CT
Li, X., Kallman, C. E., Zhang, D., Guo, C., & Zhou, Y. (2026) · European Journal of Radiology · doi:10.1016/j.ejrad.2026.113245
Zhang, H., 龚财判, Xiang, C., Wu, S., Liang, X., Song, H., Xiang, Y., Liang, S., Peng, X., Ren, X., et al. (2026) · BMJ Open · doi:10.1136/bmjopen-2026-121793
Braun, P., Zöllner, C., Wu, M., Graf, R., Stelter, J., Reik, A., Hauner, H., Kirschke, J. S., Holzapfel, C., Junker, D., et al. (2026) · NMR in Biomedicine · doi:10.1002/nbm.70407
Beyond FIB-4–VCTE agreement: an imaging-triggered pathway for fibrosis screening in MASLD
Xu, T., & Yang, T. (2026) · Annals of Hepatology · doi:10.1016/j.aohep.2026.102452
Loo, J. H., Yeow, S. M., Yong, K. R., Lee, H. Z. R., Yee, S. Y., Uppaluri, S. A. S., Aftab, S., Venkatesh, S. H., Jawan, F., Kok, S., et al. (2026) · Diagnostics · doi:10.3390/diagnostics16193260
“These exploratory thresholds were derived from previously reported MRI-PDFF categories [15,16], as validated severity cutoffs for MRI IP/OP FF measurements are not currently established.”
Karahacıoğlu, D., Kula, G. C., Kula, S., Veznikli, M., Böge, M., & Atasoy, Ç. (2026) · Radiology Cardiothoracic Imaging · doi:10.1148/ryct.260148
Teng, L., Luo, L., Shao, C., Sun, Y., Lai, J., 曹筱佩, Dong, Z., Ye, J., & Zhong, B. (2026) · Journal of the American Nutrition Association · doi:10.1080/27697061.2026.2742270
Sun, Y., Han, Z., Li, C., Hu, D., Xie, F., Li, Q., Luo, H., Liao, M., Zhou, X., Hu, J., et al. (2026) · Pharmacological Research · doi:10.1016/j.phrs.2026.109705
Metadata
- Authors
- Starekova, J., Hernando, D., Pickhardt, P. J., & Reeder, S. B.
- Affiliations
- University of Wisconsin–Madison (US)
- Venue
- Radiology
- Published
- 2021-11
- Volume
- 301
- Issue
- 2
- Pages
- 250-262
- ISSN
- 0033-8419 (ISSN-L)
- 1527-1315
- PMC
- PMC8574059
- OpenAlex
- W3199340922
- Cited by
- 320
- References
- 117
- Citations by year
- 2026: 86 · 2025: 96 · 2024: 69 · 2023: 46 · 2022: 22 · 2021: 1
- FWCI
- 5.32
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Radiology (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health contsponsor (R01 DK088925, R01 DK100651, R44-EB025729, R01-DK117354)
- Topics
- Health Sciences › Medicine › Hepatology › Liver Disease Diagnosis and Treatment
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- hepatic steatosis, liver fat quantification, non-alcoholic fatty liver disease, chemical shift MRI, non-invasive imaging, cardiovascular risk, metabolic syndrome, type 2 diabetes, artificial intelligence, treatment monitoring
- MeSH terms
- Fatty Liver, Humans, Liver, Magnetic Resonance Imaging, Tomography, X-Ray Computed, Reproducibility of Results
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
