The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Fatty Liver Disease: MR Imaging Techniques for the Detection and Quantification of Liver Steatosis
Abstract
Fatty liver disease is the most common cause of chronic liver disease in the United States. Noninvasive detection and quantification of fat is becoming more and more important clinically, due in large part to the growing prevalence of nonalcoholic fatty liver disease. Steatosis, the accumulation of fat-containing vacuoles within hepatocytes, is a key histologic feature of fatty liver disease. Liver biopsy, the current standard of reference for the assessment of steatosis, is invasive, has sampling errors, and is not appropriate in some settings. Several magnetic resonance (MR) imaging-based techniques--including chemical shift imaging, frequency-selective imaging, and MR spectroscopy--are currently in clinical use for the detection and quantification of fat-water admixtures, with each technique having important advantages, disadvantages, and limitations. These techniques permit the breakdown of the net MR signal into fat and water signal components, allowing the quantification of fat in liver tissue, and are increasingly being used in the diagnosis, treatment, and follow-up of fatty liver disease.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A placebo-controlled trial of pioglitazone in subjects with nonalcoholic steatohepatitis
Belfort, R., Harrison, S. A., Brown, K., Darland, C., Finch, J., Hardies, J., Balas, B., Gastaldelli, A., Tio, F., Pulcini, J., et al. (2006) · New England Journal of Medicine · doi:10.1056/nejmoa060326 · Cited by 1,827
Quantification of liver fat using magnetic resonance spectroscopy
Thomsen, C., Becker, U., Winkler, K., Christoffersen, P., Jensen, M., & Henriksen, O. (1994) · Magnetic Resonance Imaging · doi:10.1016/0730-725x(94)92543-7 · Cited by 332
MITCHELL, D. G., KIM, I., CHANG, T. S., VINITSKI, S., SAPONARO, S. A., EHRLICH, S. M., RIFKIN, M. D., & RUBIN, R. (1991) · Investigative Radiology · doi:10.1097/00004424-199112000-00002 · Cited by 131
Cited by
Bacchi, E., Negri, C., Targher, G., Faccioli, N., Lanza, M., Zoppini, G., Zanolin, E., Schena, F., Bonora, E., & Moghetti, P. (2013) · Hepatology · doi:10.1002/hep.26393 · Cited by 350
Kleiner, D. E., & Makhlouf, H. R. (2016) · Clinics in Liver Disease · doi:10.1016/j.cld.2015.10.011 · Cited by 303
Crawford, R. J., Filli, L., Elliott, J. M., Nanz, D., Fischer, M. A., Marcon, M., & Ulbrich, E. J. (2016) · American Journal of Neuroradiology · doi:10.3174/ajnr.a4596 · Cited by 257
Pirmoazen, A. M., Khurana, A., El Kaffas, A., & Kamaya, A. (2020) · Theranostics · doi:10.7150/thno.40249 · Cited by 165
Fischer, M. A., Pfirrmann, C. W. A., Espinosa, N., Raptis, D. A., & Buck, F. M. (2014) · European Radiology · doi:10.1007/s00330-014-3121-1 · Cited by 112
Metadata
- Authors
- Cassidy, F. H., Yokoo, T., Aganovic, L., Hanna, R. F., Bydder, M., Middleton, M. S., Hamilton, G., Chavez, A. D., Schwimmer, J. B., & Sirlin, C. B.
- Venue
- RadioGraphics
- Published
- 2009-01
- Volume
- 29
- Issue
- 1
- Pages
- 231-260
- ISSN
- 0271-5333 (ISSN-L)
- 1527-1323
- OpenAlex
- W2148948654
- Cited by
- 271
- References
- 3
- FWCI
- 14.81
- Type
- journal article
- Language
- en
- Open access
- closed
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Fatty Liver Disease: MR Imaging Techniques for the Detection and Quantification of Liver Steatosis. https://app.cochat.ai/papers/10.1148/rg.291075123
- The paper
- Cassidy, F. H., Yokoo, T., Aganovic, L., Hanna, R. F., Bydder, M., Middleton, M. S., Hamilton, G., Chavez, A. D., Schwimmer, J. B., & Sirlin, C. B. (2009). Fatty Liver Disease: MR Imaging Techniques for the Detection and Quantification of Liver Steatosis. RadioGraphics. https://doi.org/10.1148/rg.291075123
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.