Deep Learning for Inference of Hepatic Proton Density Fat Fraction From T1-Weighted In-Phase and Opposed-Phase MRI: Retrospective Analysis of Population-Based Trial Data-Reference-Cited by-同舟云学术

Deep Learning for Inference of Hepatic Proton Density Fat Fraction From T1-Weighted In-Phase and Opposed-Phase MRI: Retrospective Analysis of Population-Based Trial Data

Published:2023-09-20 Issue: Volume: Page:1-12
ISSN:0361-803X
Container-title:American Journal of Roentgenology
language:en
Short-container-title:American Journal of Roentgenology

Author:

Wang Kang¹²³,Cunha Guilherme Moura⁴,Hasenstab Kyle¹²⁵,Henderson Walter C.²,Middleton Michael S.²,Cole Shelley A.⁶,Umans Jason G.⁷⁸,Ali Tauqeer⁹,Hsiao Albert¹,Sirlin Claude B.²

Affiliation:

1. Department of Radiology, Artificial Intelligence and Data Analytic Laboratory, University of California, San Diego, La Jolla, CA.

2. Department of Radiology, Liver Imaging Group, University of California, San Diego, La Jolla, CA.

3. Department of Radiology, Stanford University, 500 Pasteur Dr, Palo Alto, CA 94304.

4. Department of Radiology, University of Washington Medicine, Seattle, WA.

5. Department of Mathematics and Statistics, San Diego State University, San Diego, CA.

6. Population Health, Texas Biomedical Research Institute, San Antonio, TX.

7. MedStar Health Research Institute, Field Studies Division, Hyattsville, MD.

8. Georgetown-Howard Universities Center for Clinical and Translational Science, Washington, DC.

9. Department of Biostatistics and Epidemiology, Center for American Indian Health Research, Hudson College of Public Health, University of Oklahoma Health Sciences Center, Oklahoma City, OK.

Publisher

American Roentgen Ray Society

Subject

Radiology, Nuclear Medicine and imaging,General Medicine

Reference39 articles.

1. Pathogenesis of non-alcoholic fatty liver disease

2. Non-alcoholic fatty liver disease: An expanded review

3. Estimation of Hepatic Proton-Density Fat Fraction by Using MR Imaging at 3.0 T

4. Quantification of Hepatic Steatosis with T1-independent, T2*-corrected MR Imaging with Spectral Modeling of Fat: Blinded Comparison with MR Spectroscopy

5. Magnetic resonance imaging and spectroscopy for monitoring liver steatosis

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1. Editor's Notebook: November 2023;American Journal of Roentgenology;2023-11