Cited by 321Liver Disease Diagnosis and TreatmentAdvanced MRI Techniques and ApplicationsRadiomics and Machine Learning in Medical Imaging

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Quantification of Liver Fat Content with CT and MRI: State of the Art

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Starekova, Jitka, Hernando, Diego, Pickhardt, Perry J., Reeder, Scott B.·2021·Radiology

DOI 10.1148/radiol.2021204288PMC PMC8574059

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Abstract

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.

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Authors: Starekova, J., Hernando, D., Pickhardt, P. J., & Reeder, S. B.
Affiliations: University of Wisconsin–Madison (US)
ORCID: Starekova, Jitka
Hernando, Diego
Pickhardt, Perry J.
Reeder, Scott B.
Venue: Radiology
Publisher: Radiological Society of North America (RSNA)
Published: 2021-11
Volume: 301
Issue: 2
Pages: 250-262
ISSN: 0033-8419 (ISSN-L)
1527-1315
DOI: 10.1148/radiol.2021204288
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

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Quantification of Liver Fat Content with CT and MRI: State of the Art

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