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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“In contrast, noninvasive imaging modalities have undergone remarkable advancements over the past two decades, offering safe, cost-effective, and re-producible whole-liver volumetric fat quantification, thereby becoming the preferred clinical approach for steatosis evaluation (Starekova et al. 2021).”
Quantification of liver steatosis using rapid kilovoltage-switching dual-energy computed tomography in individuals who are overweight or obese
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From pixels to prognosis: A comprehensive evidence synthesis on pre-retrieval imaging in deceased and living liver donors
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Additional file 1 of Bidirectional two-sample mendelian randomization analysis identifies causal associations of MRI-based cortical thickness and surface area relation to NAFLD
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Additional file 1 of Positive association of nap duration with risk of non-alcoholic fatty liver disease in an occupational population in Guangdong Province, China: a cross-sectional study
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Additional file 1 of Associations between types and sources of dietary carbohydrates and liver fat: a UK Biobank study
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Additional file 2 of Bidirectional two-sample mendelian randomization analysis identifies causal associations of MRI-based cortical thickness and surface area relation to NAFLD
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Role of chemical shift imaging in characterizing musculoskeletal soft tissue tumors
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“Cross-sectional imaging with CT or MRI can help assess liver fat content non-invasively [9].”
Changes in Three-Dimensional Volumetric Liver Fat Fraction after Cure of Chronic Hepatitis C Infection With Direct-Acting Antivirals
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Quantitative evaluation of the fatty fraction of lumbar vertebral bone marrow by the 3-point water-fat separation technology of IDEAL-IQ: A preliminary study on healthy adult volunteers
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“[8] In addition, the choice of Dixon MR sequence and the methods employed for postprocessing also significantly affect the accuracy and reliability of the measurements of fat fraction (FF) values.”
Early, Late, & Self-Selected Time-Restricted Eating: Impact on Hepatic Fat, Liver Health, & Fecal Microbiota in Adults with Overweight or ObesityPreprint
Dote‐Montero, M., Clavero‐Jimeno, A., Cortés‐Martín, A., López-Pascual, A., Merchán-Ramírez, E., Camacho-Cardeñosa, A., Alavarez, M. C., Osés, M., López-Vázquez, A., Amaro‐Gahete, F. J., et al. (2026) · medRxiv · doi:10.64898/2026.01.17.26344338
Breath-Holding No Longer Needs to Be a Barrier to Quantitative Ultrasound for Liver Fat
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Association of Visceral Fat Mass Index with Diabetes and Vascular Complications: NHANES 2011–2018 Cross-Sectional Study
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“This enhances the efficiency and cost-effectiveness of clinical evaluations, offering a promising approach for vFMI assessment [ 11 , 15 , 16 ].”
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“In clinical practice, quantitative assessment evaluation of liver fat using CT images is generally performed on non-contrast abdominal CT by measuring the liver attenuation value, the liver-to-spleen ratio (L/S ratio), and the liver-to-spleen difference (L/S difference) [12-14].”
LYPLAL1 rare loss-of-function variants in humans and deletion in human hepatoma cells protect against MASLD
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Noninvasive Assessment of Hepatic Steatosis in Living Liver Donors
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“…living donor candidates it is not routinely justified because it exposes otherwise healthy individuals to procedural risks (including pain, bleeding, and rare serious complications), in addition to potential sampling variability, thereby limiting its routine applicability in this population [4,9].”
How Accurate Is “Almost Zero”? Reconsidering CT-Based Quantification of Minimal Hepatic Fat
Wang, C. (2026) · Academic Radiology · doi:10.1016/j.acra.2026.02.037
DNA hypermethylation of choline kinases drives blockage of choline-phosphatidylcholine biosynthesis: lipidomic biomarkers and epigenetic insights of hepatic steatosis induced by arsenic
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Electron density engineering of chalcone derivatives towards synergistically strengthened lipid droplet probes for fluorescence diagnosis and drug evaluation of non-alcoholic fatty liver disease
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Association between metabolic signatures of predicted VAT mass and risk of MASLD and other chronic liver diseases
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Fraction Conversion Models Based on Ultrasound Attenuation Coefficient for Assessing Liver Steatosis
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“Because a heterogeneous pattern of steatosis has been reported in up to 60% of patients with NAFLD [20], the other model converted the AC value into a percentage based on the range of AC values from 0.5 to 1.0 dB/cm/MHz.”
Influential
Comparative Analysis of Test-Specific Assessments for Measuring Fatty Liver
Park, E.-K., Chae, M.-J., & Lim, C.-H. (2026) · Bangsaseon gisul gwahak/Journal of radiological science and technology · doi:10.17946/jrst.2026.49.1.59
CT assessment of body composition and liver fat fraction predicts prognosis in gastric cancer
Xiao, G.-L., Huang, Z.-N., Wang, Y., Wu, D.-Z., Liu, R., Luo, S.-K., Tu, R., Lin, G., Lin, M., Xie, J.-W., et al. (2026) · European Journal of Surgical Oncology · doi:10.1016/j.ejso.2026.111855
Overview of Imaging Methods for Ectopic Lipid Deposition
刘, 虹. (2026) · Advances in Clinical Medicine · doi:10.12677/acm.2026.1651841
A dose-response study of the association between MRI-derived liver proton density fat fraction and prevalent T2DM in postmenopausal women with abnormal glucose metabolism
Zheng, Y.-L., Zhang, W., Yuan, T., Gao, L., Zhu, X., Liu, Y., Shi, R.-Q., Gui-fen, H., & Quan, G.-M. (2026) · Frontiers in Endocrinology · doi:10.3389/fendo.2026.1777573
Integrated model based on ultrasound attenuation and metabolic biomarkers for noninvasive assessment of hepatic fat fraction categories in MASLD: a QCT-referenced study
Ma, Z., Wang, H., Sun, X., Zhou, S., Sun, Y., Wang, X., Li, T., Yang, X., 许建成, Guo, W., et al. (2026) · Frontiers in Physiology · doi:10.3389/fphys.2026.1804061
“Importantly, QCT is increasingly used in health check-up settings, where it provides a feasible and scalable reference for validating new imaging tools (Starekova et al., 2021).”
Human-Mouse Convergence in Metabolic Dysfunction-Associated Steatotic Liver Disease: Mouse Model Selection and Non-Invasive Diagnostic Strategies
Bonente, D., Gargiulo, S., Livi, L., Gramanzini, M., Tamborrino, T., Gherardini, L. M., Inzalaco, G., Franci, L., Chiariello, M., & Barone, V. (2026) · Livers · doi:10.3390/livers6030046
“%) for the detection of moderate and severe hepatic steatosis, while the ability to detect mild and diffuse liver disease is limited [121–123].”
CT attenuation–based hepatic steatosis assessment using 3D organ segmentation: Impact of tube voltage and need for cutoff adjustment
Yoo, J., Joo, I., Jeon, S. K., Park, J., & Lee, J. M. (2026) · European Journal of Radiology · doi:10.1016/j.ejrad.2026.113021
Fat-containing liver lesions expanding the magnetic resonance imaging signature of hepatoblastoma
Özer, G., Dalkıran, B., Özcan, H. N., Orhan, D., Oguz, B., & Haliloğlu, M. (2026) · Pediatric Radiology · doi:10.1007/s00247-026-06698-9
“First, dual-echo chemical shift imaging exhibits limited sensitivity for small-volume intravoxel lipid deposits, in which the fat fraction may be insufficient to produce detectable signal loss [13].”
Immunological prospects of modern non-invasive and minimally invasive methods for diagnosing non-alcoholic fatty liver disease
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Quantitative pancreatic MRI in diabetes mellitus: current advances and future directions
He, W., Cao, C., & Jiang, P. (2026) · Frontiers in Endocrinology · doi:10.3389/fendo.2026.1887007
Whole body CT attenuation and volume charts from routine clinical scans via LLM report filtering
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“Examplesincludehepaticsteatosis andliverfatassessment 2,3 , pancreatic fatty in ﬁ ltration 4,5 , and opportunistic screening for osteoporosis and sarcopenia based on vertebral and muscle attenuation 6 – 9 .”
Imaging-based fibrosis assessment and risk stratification in MASLD
Jin, S., Zhou, T., Jin, D., Xie, J., Sheng, G., Li, H., Li, G., Qin, C., & Gao, P. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1880238
Cohort Profile Update: The Finnish Gestational Diabetes (FinnGeDi) study
Lingaiah, S., Keikkala, E., Mustaniemi, S., Ylöstalo, P., Tervonen, T., Laitala, M., Pernu, H., Lammentausta, E., Nevalainen, M., Paakki, J., et al. (2026) · International Journal of Epidemiology · doi:10.1093/ije/dyag101
Clinical Outcomes of Cross-Sectional Imaging to Investigate Hepatic Focal Fatty Sparing in Patients with Advanced Chronic Liver Disease
Prada, C., Vaitiekunas, L., Reyes, M., Elmes, J., Starr, K., Finnigan, J., & Braund, A. (2026) · EMJ Hepatology · doi:10.33590/emjhepatol/218c81s8
Dual Energy Computed Tomography Versus Magnetic Resonance Imaging for Liver Fat Fraction QuantificationPreprint
Loo, J. H., Yeow, S. M., Yong, K. D., Lee, H. Z. R., Yee, S. Y., Uppaluri, S. A. S., Aftab, S., Venkatesh, S. H., Jawan, F., Kok, S., et al. (2026) · Research Square · doi:10.21203/rs.3.rs-10317795/v1
Nanotheranostics for alcohol-related liver disease: advances in precision diagnosis and targeted therapy
Tay, A. X. Y., Iyer, S. G., Ang, T. L., Ho, H. K., & Srinivasan, D. K. (2026) · Singapore Medical Journal · doi:10.4103/singaporemedj.smj-2025-271
Characteristics and Outcomes of Patients With Imaging-Defined MASLD and Histological Steatosis Grade S0
Liu, W. T., Lian, L., Kim, S. U., Yip, T. C., Petta, S., Nakajima, A., Tsochatzis, E. A., Shi, J., Chan, W., Boursier, J., et al. (2026) · The American Journal of Gastroenterology · doi:10.14309/ajg.0000000000004116
Automated Deep Learning Detection of Hepatic Steatosis on Noncontrast Computed Tomography Scans and Discrepancy With Scan Reports
Yardeni, D., Liu, J., Mukherjee, P., Shen, T. C., Elton, D. C., Parnell, C., Trang, W., Aleyadeh, W., Franco, G. R., Lee, S., et al. (2026) · Clinical Gastroenterology and Hepatology · doi:10.1016/j.cgh.2026.07.009

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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