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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Background
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Method
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Background
The impact of semaglutide on liver outcomes in patients with or at risk of MASH: a dose and duration response meta-analysis of randomized trials
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“…biomarkers of liver injury, specifically changes in Liver Steatosis assessed by magnetic resonance imaging proton density fat fraction (MRI-PDFF) [20], alterations in Liver Stiffness measured via magnetic resonance elastography (MRE) in kPa, as well as changes in the Fibrosis Test Score (FTS)…”
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BackgroundMethod
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MethodInfluential
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BackgroundMethodInfluential
A prospective comparison of two ultrasound attenuation imaging modes using different frequencies for assessing hepatic steatosis
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“25,36 However, few reports have evaluated pancreatic fat, and no standardized method has been established.”
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“Low HU values have been shown to correlate significantly with intracellular lipid accumulation in organs such as muscle [19] and liver [20].”
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“…percentage of one tissue mixed with another tissue from voxel values (partial volume estimation), similar to what we do in the ETV calculation, has been used in applications such as assessing adipose tissue percentage in fatty liver [31] and improving volume measurements of brain compartments [32].”
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Imagerie opportuniste : un nouvel outil pour une politique de santé préventive
Larédo, J.-D. (2023) · Bulletin de l Académie Nationale de Médecine · doi:10.1016/j.banm.2023.07.017 · Cited by 3
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Circulating Proteomics Identifies a Dynamic Profile of Hepatic Steatosis During Metabolic Intervention
El-Sabawi, B. R., Tanrıverdi, K., Gajjar, P., Nayor, M. G., Landman, J. M., Below, J. E., Haff, M. G., Long, M. T., Ezpeleta, M., Freedman, J. E., et al. (2025) · Journal of the American Heart Association · doi:10.1161/jaha.124.037100 · Cited by 3
Metabolic Implications of Childhood Cancer
Tan, J. Y., Shabane, B., Orgel, E., & Mittelman, S. D. (2025) · Current Pediatrics Reports · doi:10.1007/s40124-025-00348-8 · Cited by 4
“However, both CT and MRI can be used to precisely differentiate fat distribution (e.g. visceral vs. subcutaneous), and even mea - sure fat content in specific organs of interest, making them the gold standard for studies in which precise measures are needed [29].”
Liver fat quantification using deep silicon photon-counting CT: an in silico imaging study
Panta, R. K., Yin, Z., Grönberg, F., Wildman‐Tobriner, B., Bhattarai, M., Abadi, E., Segars, W. P., & Samei, E. (2025) · Radiology Advances · doi:10.1093/radadv/umaf031 · Cited by 3
Hepatic MR imaging using IDEAL-IQ sequence: Will Gd-EOB-DTPA interfere with reproductivity of fat fraction quantification?
Tian, Y., Liu, P., Li, J., Li, Y., & Sun, P. (2023) · World Journal of Clinical Cases · doi:10.12998/wjcc.v11.i25.5887 · Cited by 3
Liver magnetic resonance elastography and fat fraction in pediatric patients with cystic fibrosis versus healthy children
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“Hepatosteatosis was accepted as a liver fat fraction > 5% on MR spectroscopy [21].”
Understanding ADC variation by fat content effect using a dual-function MRI phantom
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“Currently, fat-water phantoms and diffusion phantoms have been used to validate MRI-based methods in quantifying the FC [12, 29–33] and to measure the diffusivity of polyethylene particle suspensions or microbead impregnated gels on DWI [11, 19, 34, 35], respectively.”
Method
Fat fraction quantification with MRI estimates tumor proliferation of hepatocellular carcinoma
Huang, M., Zhang, F., Li, Z., Luo, Y., Li, J., Wang, Z., Ma, L., Chen, G., & Hu, X. (2024) · Frontiers in Oncology · doi:10.3389/fonc.2024.1367907 · Cited by 3
HepNet: Deep Neural Network for Classification of Early-Stage Hepatic Steatosis Using Microwave Signals
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Influential
El informe radiológico en paciente con hepatopatía crónica
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Background
Prevalence of Liver Steatosis in Tuberous Sclerosis Complex Patients: A Retrospective Cross-Sectional Study
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Background
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“Fat fraction calculated in MRI is a noninvasive means of quantifying fat within the BM, liver, and musculoskeletal system, and is reported to be useful in the prediction of remission status after chemotherapy for MM [16], in osteoporosis [35], in differentiation of acute benign and neoplastic compression fractures of…”
Background
Spectrally selective and interleaved water imaging and fat imaging ( siWIFI )
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“37”
Assessment of the Diagnostic Performance and Clinical Impact of AI in Hepatic Steatosis: Systematic Review and Meta-Analysis
Song, J., Liu, D., Li, J., Cong, H., Deng, R., Lu, Y., Sun, J., & Zhang, J. (2025) · Journal of Medical Internet Research · doi:10.2196/78310 · Cited by 3
“…they lack precision in quantifying HS. Quantitative imaging techniques, including magnetic resonance imaging–proton density fat fraction (MRI-PDFF) [8], controlled attenuation parameter-based transient elastography, and noninvasive analysis [9], offer superior accuracy but are constrained by cost…”

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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