Quantification of liver fat content with CT and MRI: state of the art

Starekova, Jitka; Hernando, Diego; Pickhardt, Perry J.; Reeder, Scott B.

doi:10.1148/radiol.2021204288

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

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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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 quantification in MASLD.
“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]
CT scans obtained in clinical routine can provide a rough estimate of liver steatosis and thereby suggest SLD, but this method is not suitable for proper assessment of steatosis.
“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]

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Computed tomography attenuation values in fatty liver
Bydder, G. M., Chapman, R. W., Harry, D. S., Bassan, L., Sherlock, S., & Kreel, L. (1981) · Journal of Computed Tomography · doi:10.1016/0149-936x(81)90054-0 · Cited by 112 · see citing works
MR Spectroscopy of the Liver: Principles and Clinical Applications
Qayyum, A. (2009) · Radiographics · doi:10.1148/rg.296095520 · Cited by 110 · see citing works
Multimaterial Decomposition Algorithm for the Quantification of Liver Fat Content by Using Fast-Kilovolt-Peak Switching Dual-Energy CT: Clinical Evaluation
Hyodo, T., Yada, N., Hori, M., Maenishi, O., Lamb, P., Sasaki, K., Onoda, M., Kudo, M., Mochizuki, T., & Murakami, T. (2017) · Radiology · doi:10.1148/radiol.2017160130 · Cited by 104 · see citing works
Medical liver biopsy: background, indications, procedure and histopathology
Boyd, A., Cain, O. L., Chauhan, A., & Webb, G. J. (2019) · Frontline Gastroenterology · doi:10.1136/flgastro-2018-101139 · Cited by 102 · see citing works
Comparison of magnetic resonance spectroscopy, proton density fat fraction and histological analysis in the quantification of liver steatosis in children and adolescents
Martino, M. D., Pacifico, L., Bezzi, M., Miscio, R. D., Sacconi, B., Chiesa, C., & Catalano, C. (2016) · World Journal of Gastroenterology · doi:10.3748/wjg.v22.i39.8812 · Cited by 97 · see citing works
Quantitative chemical shift-encoded MRI is an accurate method to quantify hepatic steatosis
Kühn, J., Hernando, D., Mensel, B., Krüger, P. C., Ittermann, T., Mayerle, J. V., Hosten, N., & Reeder, S. B. (2013) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.24289 · Cited by 90 · see citing works
Automated analysis of liver fat, muscle and adipose tissue distribution from CT suitable for large-scale studies
Kullberg, J., Hedström, A., Brandberg, J., Strand, R., Johansson, L., Bergström, G. M. L., & Ahlström, H. K. (2017) · Scientific Reports · doi:10.1038/s41598-017-08925-8 · Cited by 89 · see citing works
Quantification of Hepatic Steatosis With Dual-Energy Computed Tomography
Artz, N. S., Hines, C. D. G., Brunner, S. T., Agni, R. M., Kühn, J., Roldán‐Alzate, A., Chen, G., & Reeder, S. B. (2012) · Investigative Radiology · doi:10.1097/rli.0b013e318261fad0 · Cited by 86 · see citing works
Multi‐parametric liver tissue characterization using MR fingerprinting: Simultaneous T1, T2, T2*, and fat fraction mapping
Jaubert, O., Arrieta, C., da Cruz, G. J. L., Bustin, A., Schneider, T., Georgiopoulos, G. A., Masci, P. G., Sing‐Long, C., Botnar, R. M., & Prieto, C. (2020) · Magnetic Resonance in Medicine · doi:10.1002/mrm.28311 · Cited by 84 · see citing works
Natural History of Hepatic Steatosis: Observed Outcomes for Subsequent Liver and Cardiovascular Complications
Pickhardt, P. J., Hahn, L., del Río, A. M., Park, S. H., Reeder, S. B., & Said, A. (2014) · American Journal of Roentgenology · doi:10.2214/ajr.13.11367 · Cited by 83 · see citing works
Reproducibility of MR‐based liver fat quantification across field strength: Same‐day comparison between 1.5T and 3T in obese subjects
Artz, N. S., Haufe, W. M., Hooker, C. A., Hamilton, G., Wolfson, T. J., Campos, G. M., Gamst, A. C., Schwimmer, J. B., Sirlin, C. B., & Reeder, S. B. (2015) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.24842 · Cited by 82 · see citing works
Quantification of Hepatic Steatosis with 3-T MR Imaging: Validation inob/obMice
Hines, C. D. G., Yu, H., Shimakawa, A. S., McKenzie, C. A., WARNER, T. F. C. S., Brittain, J. H., & Reeder, S. B. (2009) · Radiology · doi:10.1148/radiol.09090131 · Cited by 76 · see citing works
Contrast-enhanced computed tomography for the diagnosis of fatty liver: prospective study with same-day biopsy used as the reference standard
Kim, D. Y., Park, S. H., Lee, S. S., Kim, H. J., Kim, S. Y., Kim, M.-Y., Lee, Y., Kim, T. K., Khalili, K., Bae, M. H., et al. (2009) · European Radiology · doi:10.1007/s00330-009-1560-x · Cited by 75 · see citing works
On the confounding effect of temperature on chemical shift‐encoded fat quantification
Hernando, D., Sharma, S. D., Kramer, H., & Reeder, S. B. (2013) · Magnetic Resonance in Medicine · doi:10.1002/mrm.24951 · Cited by 72 · see citing works
Proton density fat-fraction is an accurate biomarker of hepatic steatosis in adolescent girls and young women
Rehm, J. L., Wolfgram, P. M., Hernando, D., Eickhoff, J. C., Allen, D. B., & Reeder, S. B. (2015) · European Radiology · doi:10.1007/s00330-015-3724-1 · Cited by 71 · see citing works
MRI Steatosis Grading: Development and Initial Validation of a Color Mapping System
Qayyum, A., Nystrom, M. M., Noworolski, S. M., Chu, P., Mohanty, A., & Merriman, R. B. (2012) · American Journal of Roentgenology · doi:10.2214/ajr.11.6729 · Cited by 71 · see citing works
Quantification of liver iron content with CT—added value of dual-energy
Fischer, M. A., Reiner, C. S., Raptis, D. A., Donati, O. F., Goetti, R. P., Clavien, P., & Alkadhi, H. (2011) · European Radiology · doi:10.1007/s00330-011-2119-1 · Cited by 70 · see citing works
Intra- and inter-examination repeatability of magnetic resonance spectroscopy, magnitude-based MRI, and complex-based MRI for estimation of hepatic proton density fat fraction in overweight and obese children and adults
Tyagi, A., Yeganeh, O., Levin, Y. S., Hooker, J. C., Hamilton, G., Wolfson, T. J., Gamst, A. C., Zand, A. K., Heba, E. R., Loomba, R. S., et al. (2015) · Abdominal Imaging · doi:10.1007/s00261-015-0542-5 · Cited by 64 · see citing works
Quick assessment with controlled attenuation parameter for hepatic steatosis in children based on MRI-PDFF as the gold standard
Shin, J., Kim, M., Shin, H. J., Yoon, H., Kim, S., Koh, H., & Lee, M. (2019) · BMC Pediatrics · doi:10.1186/s12887-019-1485-8 · Cited by 64 · see citing works
Free‐breathing liver fat quantification using a multiecho 3 D stack‐of‐radial technique
Armstrong, T., Dregely, I., Stemmer, A., Han, F., Natsuaki, Y., Sung, K., & Wu, H. H. (2017) · Magnetic Resonance in Medicine · doi:10.1002/mrm.26693 · Cited by 60 · see citing works
MRI‐determined liver proton density fat fraction, with MRS validation: Comparison of regions of interest sampling methods in patients with type 2 diabetes
Vu, K., Gilbert, G., Chalut, M., Chagnon, M., Chartrand, G., & Tang, A. (2015) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.25083 · Cited by 55 · see citing works
Free-breathing quantification of hepatic fat in healthy children and children with nonalcoholic fatty liver disease using a multi-echo 3-D stack-of-radial MRI technique
Armstrong, T., Ly, K. V., Murthy, S., Ghahremani, S., Kim, G. H. J., Calkins, K. L., & Wu, H. H. (2018) · Pediatric Radiology · doi:10.1007/s00247-018-4127-7 · Cited by 52 · see citing works
Validation of MRI biomarkers of hepatic steatosis in the presence of iron overload in the ob/ob mouse
Hines, C. D. G., Agni, R. M., Roen, C. N., Rowland, I. J., Hernando, D., Bultman, E. M., Horng, D. E., Yu, H., Shimakawa, A. S., Brittain, J. H., et al. (2011) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.22890 · Cited by 51 · see citing works
Fully automatic liver attenuation estimation combing CNN segmentation and morphological operations
Huo, Y., Terry, J. G., Wang, J., Nair, S., Lasko, T. A., Freedman, B. I., Carr, J. J., & Landman, B. A. (2019) · Medical Physics · doi:10.1002/mp.13675 · Cited by 49 · see citing works
Accelerating MR Imaging Liver Steatosis Measurement Using Combined Compressed Sensing and Parallel Imaging: A Quantitative Evaluation
Mann, L. W., Higgins, D. M., Peters, C. N., Cassidy, S., Hodson, K. K., Coombs, A., Taylor, R., & Hollingsworth, K. G. (2015) · Radiology · doi:10.1148/radiol.2015150320 · Cited by 44 · see citing works
Multimaterial Decomposition Algorithm for the Quantification of Liver Fat Content by Using Fast-Kilovolt-Peak Switching Dual-Energy CT: Experimental Validation
Hyodo, T., Hori, M., Lamb, P., Sasaki, K., Wakayama, T., Chiba, Y., Mochizuki, T., & Murakami, T. (2016) · Radiology · doi:10.1148/radiol.2016160129 · Cited by 44 · see citing works
In vivomagnetic resonance spectroscopy of liver tumors and metastases
ter Voert, E. E. G. W. (2011) · World Journal of Gastroenterology · doi:10.3748/wjg.v17.i47.5133 · Cited by 40 · see citing works
In vivo breath‐hold 1H MRS simultaneous estimation of liver proton density fat fraction, and T1 and T2 of water and fat, with a multi‐TR, multi‐TE sequence
Hamilton, G., Middleton, M. S., Hooker, J. C., Haufe, W. M., Forbang, N. I., Allison, M., Loomba, R. S., & Sirlin, C. B. (2015) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.24946 · Cited by 38 · see citing works
Fat-Water Phantoms for Magnetic Resonance Imaging Validation: A Flexible and Scalable Protocol
Bush, E., Gifford, A., Coolbaugh, C. L., Towse, T. F., Damon, B. M., & Welch, E. B. (2018) · Journal of Visualized Experiments · doi:10.3791/57704 · Cited by 38 · see citing works
Accuracy of common proton density fat fraction thresholds for magnitude- and complex-based chemical shift-encoded MRI for assessing hepatic steatosis in patients with obesity
Cunha, G. M., Thai, T., Hamilton, G., Covarrubias, Y., Schlein, A. N., Middleton, M. S., Wiens, C. N., McMillan, A. B., Agni, R. M., Funk, L. M., et al. (2019) · Abdominal Radiology · doi:10.1007/s00261-019-02350-3 · Cited by 37 · see citing works
Free‐breathing fat and R2* quantification in the liver using a stack‐of‐stars multi‐echo acquisition with respiratory‐resolved model‐based reconstruction
Schneider, M., Benkert, T., Solomon, E., Nickel, D. M., Fenchel, M., Kiefer, B., Maier, A., Chandarana, H., & Block, K. T. (2020) · Magnetic Resonance in Medicine · doi:10.1002/mrm.28280 · Cited by 34 · see citing works
Separation of Hepatic Iron and Fat by Dual-Source Dual-Energy Computed Tomography Based on Material Decomposition: An Animal Study
Ma, J., Song, Z., & Yan, F. (2014) · PLoS ONE · doi:10.1371/journal.pone.0110964 · Cited by 33 · see citing works
Quantification of liver fat with respiratory‐gated quantitative chemical shift encoded MRI
Motosugi, U., Hernando, D., Bannas, P., Holmes, J. H., Wang, K., Shimakawa, A. S., Iwadate, Y., Taviani, V., Rehm, J. L., & Reeder, S. B. (2015) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.24896 · Cited by 31 · see citing works
The effects of concomitant gradients on chemical shift encoded MRI
Colgan, T. J., Hernando, D., Sharma, S. D., & Reeder, S. B. (2016) · Magnetic Resonance in Medicine · doi:10.1002/mrm.26461 · Cited by 31 · see citing works
Pediatric Nonalcoholic Fatty Liver Disease: the Rise of a Lethal Disease Among Mexican American Hispanic Children
Betancourt-Garcia, M. M., Arguelles, A., Montes, J., Hernandez, A., Singh, M. K., & Forse, R. A. (2016) · Obesity Surgery · doi:10.1007/s11695-016-2440-5 · Cited by 28 · see citing works
Noise properties of proton density fat fraction estimated using chemical shift–encoded MRI
Roberts, N. T., Hernando, D., Holmes, J. H., Wiens, C. N., & Reeder, S. B. (2018) · Magnetic Resonance in Medicine · doi:10.1002/mrm.27065 · Cited by 28 · see citing works
Hepatic steatosis in participants in a program of low-dose CT screening for lung cancer
Chen, X., Li, K., Yip, R., Perumalswami, P., Branch, A. D., Lewis, S. C., Bello, D. D., Becker, B. J., Yankelevitz, D. F., & Henschke, C. I. (2017) · European Journal of Radiology · doi:10.1016/j.ejrad.2017.06.024 · Cited by 27 · see citing works
Is MR Spectroscopy Really the Best MR-Based Method for the Evaluation of Fatty Liver in Diabetic Patients in Clinical Practice?
Parente, D. B., Rodrigues, R. S., Paiva, F. F., Neto, J. A. O., Machado‐Silva, L., Lanzoni, V. P., Campos, C. F. F., de Araújo, A. L. E., do Brasil, P. E. A. A., Garteiser, P., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0112574 · Cited by 26 · see citing works
Clinical Implementation of a Focused MRI Protocol for Hepatic Fat and Iron Quantification
Pooler, B. D., Hernando, D., & Reeder, S. B. (2019) · American Journal of Roentgenology · doi:10.2214/ajr.18.20947 · Cited by 26 · see citing works
Automated measurement of liver attenuation to identify moderate-to-severe hepatic steatosis from chest CT scans
Jirapatnakul, A. C., Reeves, A. P., Lewis, S. C., Chen, X., Ma, T., Yip, R., Chin, X., Liu, S., Perumalswami, P., Yankelevitz, D. F., et al. (2019) · European Journal of Radiology · doi:10.1016/j.ejrad.2019.108723 · Cited by 26 · see citing works
Transitions of histopathologic criteria for diagnosis of nonalcoholic fatty liver disease during the last three decades
Ikura, Y. (2014) · World Journal of Hepatology · doi:10.4254/wjh.v6.i12.894 · Cited by 23 · see citing works
Validation of a motion‐robust 2D sequential technique for quantification of hepatic proton density fat fraction during free breathing
Pooler, B. D., Hernando, D., Ruby, J. A., Ishii, H., Shimakawa, A. S., & Reeder, S. B. (2018) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.26056 · Cited by 23 · see citing works
MRI in donor candidates for living donor liver transplant: Technical and practical considerations
Kim, B., Kim, S. Y., Kim, K. W., Jang, H. Y., Jang, J. K., Song, G., & Lee, S. (2018) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.26257 · Cited by 23 · see citing works
Temperature‐corrected proton density fat fraction estimation using chemical shift‐encoded MRI in phantoms
Navaratna, R., Zhao, R., Colgan, T. J., Hu, P., Bydder, M., Yokoo, T., Bashir, M. R., Middleton, M. S., Serai, S. D., Malyarenko, D. I., et al. (2021) · Magnetic Resonance in Medicine · doi:10.1002/mrm.28669 · Cited by 18 · see citing works
Estimating of hepatic fat amount using MRI proton density fat fraction in a real practice setting
Lee, H., Jun, D. W., Kang, B.-K., Nam, E., Chang, M., Kim, M., Song, S.-Y., Yoon, B. C., Lee, H. L., Lee, O. Y., et al. (2017) · Medicine · doi:10.1097/md.0000000000007778 · Cited by 17 · see citing works
Motion‐robust, high‐SNR liver fat quantification using a 2D sequential acquisition with a variable flip angle approach
Zhao, R., Zhang, Y., Wang, X., Colgan, T. J., Rehm, J. L., Reeder, S. B., Johnson, K. M., & Hernando, D. (2020) · Magnetic Resonance in Medicine · doi:10.1002/mrm.28263 · Cited by 15 · see citing works
Accuracy of Automated Liver Contouring, Fat Fraction, and R2* Measurement on Gradient Multiecho Magnetic Resonance Images
Stocker, D., Bashir, M. R., Kannengießer, S., & Reiner, C. S. (2018) · Journal of Computer Assisted Tomography · doi:10.1097/rct.0000000000000759 · Cited by 11 · see citing works
Free‐breathing liver fat and quantification using motion‐corrected averaging based on a nonlocal means algorithm
Luo, H., Zhu, A., Wiens, C. N., Starekova, J., Shimakawa, A. S., Reeder, S. B., Johnson, K. M., & Hernando, D. (2020) · Magnetic Resonance in Medicine · doi:10.1002/mrm.28439 · Cited by 7 · see citing works

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EASL-EASD-EASO Clinical Practice Guidelines on the management of metabolic dysfunction-associated steatotic liver disease (MASLD).
Tacke, F., Horn, P., Wai-Sun Wong, V., Ratziu, V., Bugianesi, E., Francque, S., Zelber-Sagi, S., Valenti, L., Roden, M., Schick, F., et al. (2024) · Journal of Hepatology · doi:10.1016/j.jhep.2024.04.031 · Cited by 2,210 · see citing works
EASL-EASD-EASO Clinical Practice Guidelines on the Management of Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD)
(2024) · Obesity Facts · doi:10.1159/000539371 · Cited by 510 · see citing works
Value-added Opportunistic CT Screening: State of the Art
Pickhardt, P. J. (2022) · Radiology · doi:10.1148/radiol.211561 · Cited by 247 · see citing works
Fast and Low-GPU-memory abdomen CT organ segmentation: The FLARE challenge
Ma, J., Zhang, Y., Gu, S., An, X., Wang, Z., Ge, C., Wang, C., Zhang, F., Wang, Y., Xu, Y., et al. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102616 · Cited by 166 · see citing works
Opportunistic Screening: Radiology Scientific Expert Panel
Pickhardt, P. J., Summers, R. M., Garrett, J. W., Krishnaraj, A., Agarwal, S., Dreyer, K. J., & Nicola, G. N. (2023) · Radiology · doi:10.1148/radiol.222044 · Cited by 134 · see citing works
Frailty and risk of metabolic dysfunction-associated steatotic liver disease and other chronic liver diseases
Zhong, Q., Zhou, R., Huang, Y., Huang, R.-D., Li, F., Chen, H.-W., Wei, Y.-F., Liu, K., Cao, B., Liao, K.-Y., et al. (2024) · Journal of Hepatology · doi:10.1016/j.jhep.2024.08.024 · Cited by 105 · see citing works
Diagnostic Modalities of Non-Alcoholic Fatty Liver Disease: From Biochemical Biomarkers to Multi-Omics Non-Invasive Approaches
Martinou, E., Pericleous, M., Stefanova, I., Kaur, V., & Angelidi, A. M. (2022) · Diagnostics · doi:10.3390/diagnostics12020407 · Cited by 81 · see citing works
Aflatoxin B1 exposure triggers hepatic lipotoxicity via p53 and perilipin 2 interaction-mediated mitochondria-lipid droplet contacts: An in vitro and in vivo assessment
Che, L., Huang, J., Lin, J., Xu, C.-Y., Wu, X.-M., Du, Z., Wu, J., Lin, Z., & Lin, Y. (2022) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2022.130584 · Cited by 69 · see citing works
Prevalence of at‐risk MASH , M et ALD and alcohol‐associated steatotic liver disease in the general population
Schneider, C. V., Schneider, K. M., Raptis, A. A., Huang, H. Y. R., Trautwein, C., & Loomba, R. S. (2024) · Alimentary Pharmacology & Therapeutics · doi:10.1111/apt.17958 · Cited by 69 · see citing works
Two-dimensional Convolutional Neural Network Using Quantitative US for Noninvasive Assessment of Hepatic Steatosis in NAFLD
Jeon, S. K., Lee, J. M., Joo, I., Yoon, J. H., & Lee, G. (2023) · Radiology · doi:10.1148/radiol.221510 · Cited by 56 · see citing works
Non-Invasive Imaging Methods to Evaluate Non-Alcoholic Fatty Liver Disease with Fat Quantification: A Review
Jang, W., & Song, J. S. (2023) · Diagnostics · doi:10.3390/diagnostics13111852 · Cited by 55 · see citing works
“However, most US examinations are qualitative and restricted in their performance, especially in overweight individuals at high risk of NAFLD [19].”
Background
Pulse-Echo Quantitative US Biomarkers for Liver Steatosis: Toward Technical Standardization
Fetzer, D. T., Rosado-Méndez, I. M., Wang, M. H., Robbin, M. L., Ozturk, A., Wear, K. A., Ormachea, J., Stiles, T. A., Fowlkes, J. B., Hall, T. J., et al. (2022) · Radiology · doi:10.1148/radiol.212808 · Cited by 45 · see citing works
AI-based opportunistic CT screening of incidental cardiovascular disease, osteoporosis, and sarcopenia: cost-effectiveness analysis
Pickhardt, P. J., Correale, L., & Hassan, C. (2023) · Abdominal Radiology · doi:10.1007/s00261-023-03800-9 · Cited by 41 · see citing works
“In general, the cardiovascular complications present a much greater risk for individuals with NAFLD compared with progression of liver disease [67, 68].”
Background
Imaging modalities for measuring body composition in patients with cancer: opportunities and challenges
Shah, U. A., Ballinger, T. J., Bhandari, R., Dieli-Conwright, C. M., Guertin, K. A., Hibler, E. A., Kalam, F., Lohmann, A. E., & Ippolito, J. E. (2023) · JNCI Monographs · doi:10.1093/jncimonographs/lgad001 · Cited by 43 · see citing works
US Quantification of Liver Fat: Past, Present, and Future
Fetzer, D. T., Pierce, T. T., Robbin, M. L., Cloutier, G., Mufti, A., Hall, T. J., Chauhan, A., Kubale, R., & Tang, A. (2023) · Radiographics · doi:10.1148/rg.220178 · Cited by 41 · see citing works
RETRACTED: Current aspects of multimodal ultrasound liver diagnostics using contrast-enhanced ultrasonography (CEUS), fat evaluation, fibrosis assessment, and perfusion analysis – An update
Jung, F., Dong, Y., & Jung, F. (2023) · Clinical Hemorheology and Microcirculation · doi:10.3233/ch-239100 · Cited by 38 · see citing works
Retracted
Pathophysiology, noninvasive diagnostics and emerging personalized treatments for metabolic associated liver diseases
Tantu, M. T., Farhana, F. Z., Haque, F., Koo, K. M., Qiao, L., Ross, A. G., & Shiddiky, M. J. A. (2025) · npj gut and liver. · doi:10.1038/s44355-025-00030-2 · Cited by 43 · see citing works
“…machine learning classi ﬁ ers may detect trace hepatic lipid content that is not clinically meaningful, further complicating diagnostic calibration 105 . address these challenges, we propose an interpretive framework that combines…”
Superoxide Dismutase‐Like Regulated Fe/Ppa@PDA/B for Synergistically Targeting Ferroptosis/Apoptosis to Enhance Anti‐Tumor Efficacy
Ding, X., Wang, Z., Yu, Q., Michał, N., Roman, S., Liu, Y., & Peng, N. (2023) · Advanced Healthcare Materials · doi:10.1002/adhm.202301824 · Cited by 37 · see citing works
Application of artificial intelligence techniques for non-alcoholic fatty liver disease diagnosis: A systematic review (2005–2023)
Zamanian, H., Shalbaf, A., Zali, M. R., Khalaj, A. R., Dehghan, P., Tabesh, M. R., Hatami, B., Alizadehsani, R., Tan, R., & Acharya, U. R. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107932 · Cited by 37 · see citing works
Liver fat quantification in photon counting CT in head to head comparison with clinical MRI – First experience
Schwartz, F. R., Ashton, J. R., Wildman‐Tobriner, B., Molvin, L. Z., Ramírez-Giraldo, J. C., Samei, E., Bashir, M. R., & Marin, D. (2023) · European Journal of Radiology · doi:10.1016/j.ejrad.2023.110734 · Cited by 36 · see citing works
Automatic Liver Tumor Segmentation from CT Images Using Graph Convolutional Network
Khoshkhabar, M., Meshgini, S., Afrouzian, R., & Danishvar, S. (2023) · Sensors · doi:10.3390/s23177561 · Cited by 34 · see citing works
Associations between types and sources of dietary carbohydrates and liver fat: a UK Biobank study
Orliacq, J., Perez‐Cornago, A., Parry, S. A., Kelly, R. K., Koutoukidis, D. A., & Carter, J. L. (2023) · BMC Medicine · doi:10.1186/s12916-023-03135-8 · Cited by 33 · see citing works
“…this study sought to study the associations between different types and sources of dietary carbohydrates in the largest prospective study to date with liver fat measured using the most accurate and precise non-invasive method for liver fat quantification, magnetic resonance imaging (MRI) [31].”
Background
A randomized, placebo‐controlled clinical trial of hydrogen/oxygen inhalation for non‐alcoholic fatty liver disease
Tao, G., Zhang, G., Chen, W., Yang, C., Xue, Y., Song, G., & Qin, S. (2022) · Journal of Cellular and Molecular Medicine · doi:10.1111/jcmm.17456 · Cited by 32 · see citing works
Quantification of ectopic fat storage in the liver and pancreas using six-point Dixon MRI and its association with insulin sensitivity and β-cell function in patients with central obesity
Cao, M., Wu, W., Chen, J., Fang, X., Ren, Y., Zhu, X., Cheng, H., & Tang, Q. (2023) · European Radiology · doi:10.1007/s00330-023-09856-x · Cited by 31 · see citing works
“Chemical shift-based MR techniques, such as the six-point Dixon method, have been applied to various organs, including the liver and pancreas [26–32].”
Method
Curcumin protects against bisphenol A‐induced hepatic steatosis by inhibiting cholesterol absorption and synthesis in CD ‐1 mice
Hong, T., Zou, J., Yang, J., Liu, H., Cao, Z., He, Y., & Feng, D. (2023) · Food Science & Nutrition · doi:10.1002/fsn3.3468 · Cited by 30 · see citing works
Detection of Moderate Hepatic Steatosis on Portal Venous Phase Contrast-Enhanced CT: Evaluation Using an Automated Artificial Intelligence Tool
Pickhardt, P. J., Blake, G. M., Kimmel, Y., Weinstock, E., Shaanan, K., Hassid, S., Abbas, A., & Fox, M. A. (2023) · American Journal of Roentgenology · doi:10.2214/ajr.23.29651 · Cited by 30 · see citing works
Radiomics and liver: Where we are and where we are headed?
Maino, C., Vernuccio, F., Cannella, R., Franco, P. N., Giannini, V., Dezio, M., Pisani, A. R., Blandino, A., Faletti, R., Bernardi, E. D., et al. (2024) · European Journal of Radiology · doi:10.1016/j.ejrad.2024.111297 · Cited by 30 · see citing works
Multimodality Imaging in Metabolic Syndrome: State-of-the-Art Review
Kalisz, K. R., Navin, P. J., Itani, M., Agarwal, A. K., Venkatesh, S. K., & Rajiah, P. S. (2024) · Radiographics · doi:10.1148/rg.230083 · Cited by 29 · see citing works
Nonalcoholic fatty liver disease (NAFLD) detection and deep learning in a Chinese community-based population
Yang, Y., Liu, J., Sun, C., Shi, Y., Hsing, J. C., Kamya, A., Keller, C. A., Antil, N., Rubin, D. L., Wang, H., et al. (2023) · European Radiology · doi:10.1007/s00330-023-09515-1 · Cited by 28 · see citing works
“Imaging modalities such as magnetic resonance imaging (MRI), proton density fat fraction (PDFF, in percentage), chemical shift–encoded MRI, magnetic resonance spectroscopy, and computed tomographic (CT) are highly accurate and precise to quantify liver fat content (especially MRI-PDFF) [10].”
Background
Comparing ultrasound-derived fat fraction and MRI-PDFF for quantifying hepatic steatosis: a real-world prospective study
Qi, R., Lu, L., He, T., Zhang, L., Lin, Y., & Bao, L. (2024) · European Radiology · doi:10.1007/s00330-024-11119-2 · Cited by 28 · see citing works
“Many studies have analyzed the diagnostic ef ﬁ cacy of QUS for hepatic steatosis, using MRI-PDFF as the reference standard, but the diagnostic thresholds of MRI-PDFF are various [27 – 30].”
Liver fat as risk factor of hepatic and cardiometabolic diseases
Demir, M., Bornstein, S. R., Mantzoros, C. S., & Perakakis, N. (2023) · Obesity Reviews · doi:10.1111/obr.13612 · Cited by 29 · see citing works
“on very high linear correlations allows the conversion of the HU measurements to PDFF.(34) However, because of the risk of ionized”
BackgroundMethodInfluential
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
Hong, C., Wu, C., Ma, P., Cui, H., Chen, L., Li, R., Li, Q., Zeng, L., Liao, S., Xiao, L., et al. (2022) · BMC Gastroenterology · doi:10.1186/s12876-022-02246-5 · Cited by 27 · see citing works
“In clinical practice, the initial diagnosis of NAFLD is usually radiological and based on the presence of fat within the liver that involves at least 5% of the liver weight [8, 9].”
Background
Photon-counting Detector CT for Liver Fat Quantification: Validation across Protocols in Metabolic Dysfunction–associated Steatotic Liver Disease
Lin, H., Xu, X., Deng, R., Xu, Z., Cai, X., Dong, H., & Yan, F. (2024) · Radiology · doi:10.1148/radiol.240038 · Cited by 27 · see citing works
A data-driven approach to decode metabolic dysfunction-associated steatotic liver disease
Ramos, M. J., Kendall, T. J., Drozdov, I. A., & Fallowfield, J. A. (2023) · Annals of Hepatology · doi:10.1016/j.aohep.2023.101278 · Cited by 26 · see citing works
“[38].”
Comparing Three Ultrasound-Based Techniques for Diagnosing and Grading Hepatic Steatosis in Metabolic Dysfunction-Associated Steatotic Liver Disease
Wang, P., Song, D., Han, J., Zhang, J., Chen, H., Gao, R., Shen, H., & Li, J. (2024) · Academic Radiology · doi:10.1016/j.acra.2024.09.002 · Cited by 23 · see citing works
Multifunction in One Nanoparticle for Anticancer Therapy: Bowl-Shaped Au@PDA Yolk–Shell NPs
Ding, X., Wang, T., Bai, S., Wan, Y., Zhu, S., Li, T., Peng, N., Qiu, T., & Liu, Y. (2022) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.2c07671 · Cited by 23 · see citing works
Comparison of the accuracy of commercial two-point and multi-echo Dixon MRI for quantification of fat in liver, paravertebral muscles, and vertebral bone marrow
Haueise, T., Schick, F., Stefan, N., & Machann, J. (2024) · European Journal of Radiology · doi:10.1016/j.ejrad.2024.111359 · Cited by 22 · see citing works
Revolutionizing MASLD: How Artificial Intelligence Is Shaping the Future of Liver Care
Pugliese, N., Bertazzoni, A., Hassan, C., Schattenberg, J. M., & Aghemo, A. (2025) · Cancers · doi:10.3390/cancers17050722 · Cited by 23 · see citing works
“These methods have shown strong correlation with histopathological findings [53,58–60] AI-driven approaches further improve fat quantification by automating analysis in specific regions of interest (ROIs) [53].”
Influential
Post-contrast CT liver attenuation alone is superior to the liver–spleen difference for identifying moderate hepatic steatosis
Pickhardt, P. J., Blake, G. M., Moeller, A., Garrett, J. W., & Summers, R. M. (2024) · European Radiology · doi:10.1007/s00330-024-10816-2 · Cited by 21 · see citing works
“If a more precise assessment or follow-up of hepatic steatosis incidentally detected on US or CT is desired, MR-PDFF is now the accepted reference standard [12].”
Advancements in Liver Tumor Detection: A Comprehensive Review of Various Deep Learning Models
Hariharan, S., Anandan, D., Krishnamoorthy, M., Kukreja, V., Goyal, N., & Chen, S.-Y. (2024) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2024.057214 · Cited by 21 · see citing works
“Noise elimination [22,25,91–94] Image enhancement [24,44,95–99] Segmentation Deep learning Knowledge constraint [25,52,100–104] Metric calculation Similarity mismatch [26,105–107] Feature extraction Loss [31,53,108–112] Entropy Low level [66,113–117] Classification Performance Algorithm complexity…”
Noninvasive Quantitative CT for Diffuse Liver Diseases: Steatosis, Iron Overload, and Fibrosis
Pickhardt, P. J., & Lubner, M. G. (2024) · Radiographics · doi:10.1148/rg.240176 · Cited by 21 · see citing works
Noninvasive Quantification of Hepatic Steatosis Using Ultrasound‐Derived Fat Fraction (CHESS2303): A Prospective Multicenter Study
Huang, Y., Li, J., Liu, C., Song, D., Zhu, C., Ren, Y., Lv, J., Jiang, L., Shan, R., Wang, H., et al. (2025) · MedComm · doi:10.1002/mco2.70123 · Cited by 20 · see citing works
Performance of Magnetic Resonance Imaging and Ultrasound for Identifying the Different Degrees of Hepatic Steatosis: A Systematic Review and Meta-analysis
Wu, S., Pan, J., Song, M., Zhao, Y.-C., Chen, W., Huang, H., Zhu, Y., & Chen, F. (2025) · Academic Radiology · doi:10.1016/j.acra.2025.03.008 · Cited by 20 · see citing works
Correlation between magnetic resonance imaging proton density fat fraction (MRI-PDFF) and liver biopsy to assess hepatic steatosis in obesity
Wibulpolprasert, P., Subpinyo, B., Chirnaksorn, S., Shantavasinkul, P. C., Putadechakum, S., Phongkitkarun, S., Sritara, C., Angkathunyakul, N., & Sumritpradit, P. (2024) · Scientific Reports · doi:10.1038/s41598-024-57324-3 · Cited by 20 · see citing works
“There is no consensus on a standardized approach to measuring liver fat with manually drawn regions of interest 29 .”
Background
Multiparametric MR assessment of liver fat, iron, and fibrosis: a concise overview of the liver “Triple Screen”
Low, G., Ferguson, C., Locas, S., Tu, W., Manolea, F., Sam, M., & Wilson, M. P. (2023) · Abdominal Radiology · doi:10.1007/s00261-023-03887-0 · Cited by 19 · see citing works
“, B0 inhomogeneity, T1 bias, T2* effects, noise bias, spectral complexity of fat, eddy currents, and J-coupling) and has a limited dynamic range of 0–50% fat content [3, 26, 33].”
BackgroundMethodInfluential
Multi-modal AI for opportunistic screening, staging and progression risk stratification of steatotic liver disease
Gao, Y., Li, C., Chang, W., Du, B., Ye, X., Yeo, Y. H., Xia, Y., Guo, H., Zhang, X., Liu, W., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-68414-3 · Cited by 19 · see citing works
Genetic risk of fatty liver disease and mortality in the general population: A Mendelian randomization study
Gellert‐Kristensen, H., Tybjærg‐Hansen, A., Nordestgaard, B. G., Ghouse, J., Fuchs, A., Kühl, J. T., Sigvardsen, P. E., Kofoed, K. F., & Stender, S. (2023) · Liver International · doi:10.1111/liv.15629 · Cited by 18 · see citing works
“Of the individuals with available CT scans, 783 (11%) had hepatic attenuation values of less than 48 HU, consistent with moderate to severe steatosis [17].”
Background
The effect of SGLT2 inhibitors on hepatic steatosis detected by MRI-PDFF in patients with type 2 Diabetes mellitus and metabolic-associated steatotic liver disease
Amin, M. A., Sadik, N. A., Saad, H. A., Fawzy, M., & Elsheimy, H. A. (2025) · Internal and Emergency Medicine · doi:10.1007/s11739-025-03902-w · Cited by 21 · see citing works
“The MRI-PDFF showed a strong correlation with liver fat content that has been established by histopathologic examination, with a correlation coefficient of r = 0.743 ( P < 0.001) [22].”
Influential
Imaging of Alcohol-Associated Liver Disease
Maheshwari, S., Gu, C. N., Caserta, M. P., Kezer, C. A., Shah, V. H., Torbenson, M. S., Menias, C. O., Fidler, J. L., & Venkatesh, S. K. (2023) · American Journal of Roentgenology · doi:10.2214/ajr.23.29917 · Cited by 18 · see citing works
Multiorgan Imaging for Interorgan Crosstalk in Cardiometabolic Diseases
Devesa, A., Delgado, V., Valkovič, L., Lima, J. A. C., Nagel, E. C., Ibáñez, B., & Raman, B. (2025) · Circulation Research · doi:10.1161/circresaha.125.325517 · Cited by 18 · see citing works
“188,189 However, while liver fat is strongly tied to cardiometabolic conditions, its direct association with cardiac outcomes remains unclear, with inflammation likely playing a more significant role.”
Promoting Surgical Resection through Future Liver Remnant Hypertrophy
Entezari, P., Toskich, B. B., Kim, E., Padia, S. A., Christopher, D. A., Sher, A., Thornburg, B. G., Hohlastos, E. S., Salem, R., Collins, J. D., et al. (2022) · Radiographics · doi:10.1148/rg.220050 · Cited by 16 · see citing works
Automated Integration of AI Results into Radiology Reports Using Common Data Elements
Mehdiratta, G., Duda, J., Elahi, A., Borthakur, A., Chatterjee, N. R., Gee, J. C., Sagreiya, H., Witschey, W. R. T., & Kahn, C. E. (2025) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-025-01414-9 · Cited by 16 · see citing works
“The system applies a deep learning convolutional neural network for anatomic segmentation of CT; its initial clinical application has been volumetry of liver and spleen and opportunistic detection of hepatic steatosis in patients undergoing non-contrast CT examination of the abdomen and pelvis [10].”
Role of metabolic dysfunction-associated fatty liver disease in atrial fibrillation and heart failure: molecular and clinical aspects
Cheung, J., Cheung, B. M. Y., Yiu, K., Tse, H., & Chan, Y. (2025) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2025.1573841 · Cited by 15 · see citing works
“As well, computed tomography is not the golden standard to examine MASLD, the sensitivity and speci ﬁ city of CT to detect mild steatosis are not high, 57% and 88%, respectively (107).”
Ultrasound-guided attenuation parameter for identifying metabolic dysfunction-associated steatotic liver disease: a prospective study
Huang, Y., Sun, C., Wang, Y., Cheng, J., Wang, S.-W., Wei, L., Lu, X.-Y., Cheng, R., Wang, M., Fan, J.-G., et al. (2024) · ULTRASONOGRAPHY · doi:10.14366/usg.24204 · Cited by 15 · see citing works
“The MRI-PDFF cutoff values for each steatosis grade were as follows: <5% indicated absent hepatic steatosis (S0); ≥5% denoted mild hepatic steatosis (≥S1); ≥15% represented moderate hepatic steatosis (≥S2); and ≥25% indicated severe hepatic steatosis (S3) [16].”
Automated liver volumetry and hepatic steatosis quantification with magnetic resonance imaging proton density fat fraction
Chang, Y.-C., Yen, K.-C., Liang, P., Ho, M., Ho, C., Hsiao, C., Hsiao, C., Lu, C.-H., & Wu, C. (2024) · Journal of the Formosan Medical Association · doi:10.1016/j.jfma.2024.04.012 · Cited by 14 · see citing works
Obesity Is Associated with Fatty Liver and Fat Changes in the Kidneys in Humans as Assessed by MRI
Raphael, H., Klang, E., Konen, E., Inbar, Y., Leibowitz, A., Frenkel‐Nir, Y., Apter, S., & Grossman, E. (2024) · Nutrients · doi:10.3390/nu16091387 · Cited by 14 · see citing works
“This definition is based on histologic studies on liver steatosis [12 Since a fa tt y kidney is not clearly de ﬁ ned in the literature and since there is no research on the correlation between MRI ﬁ ndings and histologic structure in this regard, we decided to set a threshold of a fat fraction of…”
BackgroundMethodInfluential
Association Between the Hepatic Steatosis Index and Risk of Incident Type 2 Diabetes Mellitus in the Normoglycemic Population:A Longitudinal Prospective Study in Japan
Si, R., Xiao, J., Zheng, K., Yin, Y., & Li, Y. (2024) · Diabetes Metabolic Syndrome and Obesity · doi:10.2147/dmso.s462459 · Cited by 14 · see citing works
“30,31 In recent years, scholars have also discovered the value of HSI in other chronic diseases.”
Background
Photon-counting CT–derived Quantification of Hepatic Fat Fraction: A Clinical Validation Study
Dell, T., Mesropyan, N., Layer, Y. C., Tischler, V., Weinhold, L., Chang, J., Jansen, C., Schmidt, B. T., Jürgens, M., Isaak, A., et al. (2025) · Radiology · doi:10.1148/radiol.241677 · Cited by 13 · see citing works
Liver MRI–derived Proton Density Fat Fraction Predicts Diabetes Risk in Patients with Obesity: A Dose-Response Study
Deng, M., Li, Z., Sun, L., Wang, H., Tang, J., Liu, Y., Chen, X., Xu, H., & Dai, Z. (2025) · Radiology · doi:10.1148/radiol.250026 · Cited by 13 · see citing works
Current Update on Nomenclature, Diagnosis, and Management of Metabolic Dysfunction–associated Steatotic Liver Disease: Radiologists’ Perspective
Osta, E., Rios, F. S., Jaganathan, S., Thomas, C., Tsai, E., Surabhi, V. R., Prasad, S. R., & Katabathina, V. S. (2025) · Radiographics · doi:10.1148/rg.240221 · Cited by 13 · see citing works
Development and validation of multivariable quantitative ultrasound for diagnosing hepatic steatosis
Jeon, S. K., Lee, J. M., Cho, S. J., Byun, Y.-H., Jee, J. H., & Kang, M. (2023) · Scientific Reports · doi:10.1038/s41598-023-42463-w · Cited by 13 · see citing works
“Mild, moderate, and severe hepatic steatosis were defined using MRI thresholds of 5%, 15%, and 25%, respectively 22 .”
Background
Evaluation and Prognostication of Gd‐EOB‐DTPA MRI and CT in Patients With Macrotrabecular‐Massive Hepatocellular Carcinoma
Cheng, J., Li, X., Wang, L., Chen, F., Li, Y., Zuo, G., Pei, M., Zhang, H., Yu, L., Liu, C., et al. (2023) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.29052 · Cited by 13 · see citing works
“34 A previous study has shown that intratumor fat is more common in well-differentiated HCC due to transient hypoxia in the early HCC formation.”
Background
Utilizing fully-automated 3D organ segmentation for hepatic steatosis assessment with CT attenuation-based parameters
Yoo, J., Joo, I., Jeon, S. K., Park, J., & Yoon, S. H. (2024) · European Radiology · doi:10.1007/s00330-024-10660-4 · Cited by 13 · see citing works
“These MRI scans encompassed the measurement of MRS-derived PDFF, a non-invasive gold standard for quantifying liver fat [2].”
BackgroundMethod
Emerging mechanisms of non-alcoholic steatohepatitis and novel drug therapies
Hao, C. H. E. N., Zhou, Y., Hao, H., & XIONG, J. (2024) · Chinese Journal of Natural Medicines · doi:10.1016/s1875-5364(24)60690-4 · Cited by 15 · see citing works
Fatty Liver Disease Along Cushing Syndrome Evolution
Marengo, M., Briet, C., Munier, M., Boursier, J., Rodien, P., & Suteau, V. (2024) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/clinem/dgae568 · Cited by 13 · see citing works
Hepatic steatosis using ultrasound-derived fat fraction: First technical and clinical evaluation
Huang, Y., Cheng, J., Wang, Y., Xu, X., Wang, S., Li, W., & Dong, Y. (2023) · Clinical Hemorheology and Microcirculation · doi:10.3233/ch-238102 · Cited by 12 · see citing works
Fat-only Dixon: how to use it in body MRI
Salari, R., Ballard, D. H., Hoegger, M. J., Young, D., & Shetty, A. S. (2022) · Abdominal Radiology · doi:10.1007/s00261-022-03546-w · Cited by 12 · see citing works
Hepatic Steatosis: CT-Based Prevalence in Adults in China and the United States and Associations With Age, Sex, and Body Mass Index
Guo, Z., Blake, G. M., Graffy, P. M., Sandfort, V., Summers, R. M., Li, K., Xu, S., Chen, Y., Zhou, J., Shao, J., et al. (2021) · American Journal of Roentgenology · doi:10.2214/ajr.21.26728 · Cited by 12 · see citing works
Body composition analysis by radiological imaging – methods, applications, and prospects
Linder, N., Denecke, T., & Busse, H. (2024) · RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren · doi:10.1055/a-2263-1501 · Cited by 11 · see citing works
“In the future, quantitative CT could also play a greater role in the characterization of MASLD [52].”
Automated hepatic steatosis assessment on dual-energy CT-derived virtual non-contrast images through fully-automated 3D organ segmentation
Jeon, S. K., Joo, I., Park, J., & Yoo, J. (2024) · La radiologia medica · doi:10.1007/s11547-024-01833-8 · Cited by 11 · see citing works
“Hepatic steatosis, characterized by excessive fat accumulation in hepatocytes, is a key histological feature of steatotic liver diseases, including metabolic dysfunction-associated steatotic liver disease and alcohol-associated liver disease [1, 2].”
BackgroundMethod
Ultrasound-estimated hepatorenal index: diagnostic performance and interobserver agreement for pediatric liver fat quantification
Hajibonabi, F., Riedesel, E. L., Taylor, S. D., Linam, L. E., Alazraki, A. L., Zhang, C., & Khanna, G. (2024) · Pediatric Radiology · doi:10.1007/s00247-024-06021-4 · Cited by 11 · see citing works
“Magnetic resonance imaging (MRI)–based liver fat quantification techniques like magnetic resonance (MR) spectroscopy and proton density fat fraction (PDFF) have essentially replaced the need for hepatic biopsy for liver fat estimation [8, 9].”
Clinical spectrum transition and prediction model of nonalcoholic fatty liver disease in children with obesity
Zhou, X. J., Lin, X., Chen, J., Pu, J., Wu, W., Wu, Z., Lin, H., Huang, K., Zhang, L. R., Dai, Y.-L., et al. (2022) · Frontiers in Endocrinology · doi:10.3389/fendo.2022.986841 · Cited by 11 · see citing works
“Hydrogen proton magnetic resonance spectroscopy ((1)H-MRS) can quantitatively measure liver fat content in a highly accurate and precise manner (17).”
Background
Fat quantification: Imaging methods and clinical applications in cancer.
Yan, S.-Y., Yang, Y.-W., Jiang, X.-Y., Hu, S., Su, Y.-Y., Yao, H., & Hu, C.-H. (2023) · European Journal of Radiology · doi:10.1016/j.ejrad.2023.110851 · Cited by 8 · see citing works
Perirenal Fat Thickness is Associated with Metabolic Dysfunction-Associated Fatty Liver Disease in Type 2 Diabetes Mellitus
Yang, J., Li, C. W., Zhang, J. R., Qiu, H., Guo, X. L., & Wang, W. (2023) · Diabetes Metabolic Syndrome and Obesity · doi:10.2147/dmso.s415477 · Cited by 10 · see citing works
“The diagnosis of fatty liver was based on the CTL-S, which was reported to be the most accurate CT index for assessing fatty liver.(16) Participants with 0.”
Method
Two-point Dixon and six-point Dixon magnetic resonance techniques in the detection, quantification and grading of hepatic steatosis
Elfaal, M., Supersad, A., Ferguson, C., Locas, S., Manolea, F., Wilson, M. P., Sam, M., Tu, W., & Low, G. (2023) · World Journal of Radiology · doi:10.4329/wjr.v15.i10.293 · Cited by 10 · see citing works
Malnutrition-Related Liver Steatosis, CONUT Score and Poor Clinical Outcomes in an Internal Medicine Department
Miano, N., Todaro, G., Marco, M. D., Scilletta, S., Bosco, G., Barbagallo, F. D. G., Scicali, R., Piro, S., Purrello, F., & Pino, A. D. (2024) · Nutrients · doi:10.3390/nu16121925 · Cited by 10 · see citing works
“…from steatosis (fatty liver) to steatohepatitis, acute alcohol-associated hepatitis, and liver cirrhosis, is associated with malnutrition, especially among hospitalized patients due to an inadequate intake of both macro-and micro-nutrients, leading to higher mortality and complications [8,9].”
Background
Automated abdominal organ segmentation algorithms for non-enhanced CT for volumetry and 3D radiomics analysis
Park, J., Joo, I., Jeon, S. K., Kim, J., Park, S. J., & Yoon, S. H. (2024) · Abdominal Radiology · doi:10.1007/s00261-024-04581-5 · Cited by 10 · see citing works
“Indeed, CT attenuation values, one of the most widely used radiomics features, have been shown to provide a more accurate diagnosis of hepatic steatosis in non-enhanced CT compared to contrast-enhanced CT [10, 11].”
CT assessment of liver fat fraction and abdominal fat composition can predict postoperative liver metastasis of colorectal cancer
Yin, K., Liao, G., Peng, H., Lai, S., & Guo, J. (2024) · European Journal of Radiology · doi:10.1016/j.ejrad.2024.111814 · Cited by 10 · see citing works
Quantification of Fat Metaplasia in the Sacroiliac Joints of Patients With Axial Spondyloarthritis by Chemical Shift-Encoded MRI: A Diagnostic Trial
Liu, D., Lin, C., Liu, B., Qi, J., Wen, H., Tu, L., Wei, Q., Kong, Q., Xie, Y., & Gu, J. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2021.811672 · Cited by 10 · see citing works
Liver cirrhosis in children – the role of imaging in the diagnostic pathway
Herrmann, J., Petit, P., Grabhorn, E. F., Lenz, A., Jürgens, J. H. W., & Franchi‐Abella, S. (2022) · Pediatric Radiology · doi:10.1007/s00247-022-05480-x · Cited by 9 · see citing works
“Chemical shift-encoded MRI is the most accurate method to measure fat content [42].”
Method
Performance of the Pooled Cohort Equations in non‐alcoholic fatty liver disease: The Multi‐Ethnic Study of Atherosclerosis
Henson, J. B., Budoff, M. J., & Muir, A. J. (2022) · Liver International · doi:10.1111/liv.15480 · Cited by 9 · see citing works
Mechanisms underpinning the effect of exercise on the non-alcoholic fatty liver disease: review
Bekheit, M., Kamera, B. S., Colacino, L., Dropmann, A., Delibegović, M., Almadhoob, F., Hanafy, N. A. N., Bermano, G., & Hammad, S. (2024) · EXCLI journal · doi:10.17179/excli2024-7718 · Cited by 11 · see citing works
Multi‐Center, Multi‐Vendor Validation of Simultaneous MRI ‐Based Proton Density Fat Fraction and R2 * Mapping Using a Combined Proton Density Fat Fraction‐ R2 * Phantom
Starekova, J., Rutkowski, D. R., Bae, W. C., Do, H. P., Madhuranthakam, A. J., Malis, V., Lin, S. Q., Serai, S. D., Yokoo, T., Reeder, S. B., et al. (2025) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.29775 · Cited by 10 · see citing works
Surveillance of the progression and assessment of treatment endpoints for nonalcoholic steatohepatitis
Shi, Y., & Fan, J. (2022) · Clinical and Molecular Hepatology · doi:10.3350/cmh.2022.0401 · Cited by 9 · see citing works
“0401 steatosis grade and fibrosis stage.(111) As early as in the FLINT trial of obeticholic acid (OCA), MRI-PDFF was used as a surrogate marker of steatosis.”
Method
Non-Invasive Diagnostic of NAFLD in Type 2 Diabetes Mellitus and Risk Stratification: Strengths and Limitations
Boeriu, A. M., Dobru, D. E., & Fofiu, C. (2023) · Life · doi:10.3390/life13122262 · Cited by 9 · see citing works
“The methods for liver steatosis assessment are based on the chemical shift between water and fat resonance frequencies: magnetic resonance spectroscopy (MRS) and chemical shift encoded MRI (CSE MRI) [124,127].”
BackgroundMethod
Gelatin-Based Liver Phantoms for Training Purposes: A Cookbook Approach
Elisei, R. C., Graur, F., Szold, A., Melzer, A., Moldovan, S. C., Motrescu, M., Moiș, E. I., Popa, C., Pisla, D. L., Vaida, C. L., et al. (2024) · Journal of Clinical Medicine · doi:10.3390/jcm13123440 · Cited by 9 · see citing works
An Optimized Strategy Based on Conventional Ultrasound for Diagnosing Metabolic Dysfunction-Associated Steatotic Liver Disease
Feng, X., Ye, J., Deng, H. X., Li, X., Xu, L., Feng, S., Dong, Z., Liao, B., Dong, Z., & Zhong, B. (2023) · Diagnostics · doi:10.3390/diagnostics13233503 · Cited by 8 · see citing works
“As a result, CT is not recommended as a primary modality for measuring liver steatosis [22].”
Background
Motion‐Insensitive Flip Angle Modulated Liver Proton Density Fat‐Fraction and R2 * Mapping During Free‐Breathing MRI in a Clinical Setting
Heidenreich, J. F., Tang, J., Tamada, D., Müller, L., Grunz, J., Souza, R. D. V., Anagnostopoulos, A. A., Pirasteh, A., Reeder, S. B., & Hernando, D. (2025) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.70033 · Cited by 8 · see citing works
“It is important to recognize that cut-off values for PDFF are not standardized as much as for LIC [2, 41], and both parameters are inherently continuous variables.”
Influential
Association of total and regional fat-to-muscle ratio with the risk of metabolic dysfunction-associated fatty liver disease and other chronic liver diseases
Xi, Y., Liang, Z., Jin, H., Jiang, Y., Tan, R., Zhang, Y., Wang, J., Sui, W., Liu, B., Zhang, H., et al. (2026) · Hepatology International · doi:10.1007/s12072-026-11064-w · Cited by 8 · see citing works
“Liver PDFF and cT1 were obtained from the UK Biobank imaging sub-study [14].”
Diagnostic performance of quantitative ultrasonography for hepatic steatosis in a health screening program: a prospective single-center study
Pyo, J. H., Cho, S. J., Choi, S. C., Jee, J. H., Yun, J., Hwang, J. A., Park, G., Kim, K., Kang, W., Kang, M., et al. (2024) · ULTRASONOGRAPHY · doi:10.14366/usg.24040 · Cited by 8 · see citing works
Recent advances in noninvasive assessment of liver steatosis
Ferraioli, G., & Barr, R. G. (2024) · Polskie Archiwum Medycyny Wewnętrznej · doi:10.20452/pamw.16703 · Cited by 8 · see citing works
Detection of moderate hepatic steatosis on contrast-enhanced dual-source dual-energy CT: Role and accuracy of virtual non-contrast CT
Catania, R., Jia, L., Haghshomar, M., Miller, F. H., & Borhani, A. A. (2024) · European Journal of Radiology · doi:10.1016/j.ejrad.2024.111328 · Cited by 7 · see citing works
Noninvasive tools for the assessment of fibrosis in metabolic dysfunction-associated steatotic liver disease
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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 › Epidemiology › 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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