Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
466-02-006 ISMRM Abstract

Multiparametric MRI combined with machine learning algorithm for Evaluating Disease Progression in Rats with NASH

Primary: Diffusion - DWI/DTI/DKI
Secondary: Body - Liver
466-02-006 · Quantitative Applications in Body MRI: In Numbers We Trust · Tuesday, 12 May, 9:15 AM–10:10 AM · Digital Posters Row G
Keywords: Diffusion Tensor Imaging Machine learning (ML) Fatty liver disease T1 and T2 mapping 9.4T MRI
Accepted
jiayi jiang 1, songying pi2, Yaqin Zhang3
1Department of Radiology, The fifth Affiliated Hospital, Sun Yat-Sen University, zhuhai, China
2The fifth Affiliated Hospital, Sun Yat-Sen University, zhuhai, China
3Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
Presenting Author: jiayi jiang

Synopsis

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References

1. Diehl AM, Day C. Cause, Pathogenesis, and Treatment of Nonalcoholic Steatohepatitis. N Engl J Med. 2017;377(21):2063-72.
2. Younossi ZM, Koenig AB, Abdelatif D, Fazel Y, Henry L, Wymer M. Global epidemiology of nonalcoholic fatty liver disease-Meta-analytic assessment of prevalence, incidence, and outcomes. Hepatology. 2016;64(1):73-84.
3. Sheka AC, Adeyi O, Thompson J, Hameed B, Crawford PA, Ikramuddin S. Nonalcoholic Steatohepatitis. Jama. 2020;323(12).
4. Ratziu V, Charlotte F, Heurtier A, Gombert S, Giral P, Bruckert E, et al. Sampling variability of liver biopsy in nonalcoholic fatty liver disease. Gastroenterology. 2005;128(7):1898-906.
5. Nalbantoglu IL, Brunt EM. Role of liver biopsy in nonalcoholic fatty liver disease. World J Gastroenterol. 2014;20(27):9026-37.
6. Kellman P, Bandettini WP, Mancini C, Hammer-Hansen S, Hansen MS, Arai AE. Characterization of myocardial T1-mapping bias caused by intramyocardial fat in inversion recovery and saturation recovery techniques. J Cardiovasc Magn Reson. 2015;17(1):33.
7. Mozes FE, Tunnicliffe EM, Pavlides M, Robson MD. Influence of fat on liver T1 measurements using modified Look-Locker inversion recovery (MOLLI) methods at 3T. J Magn Reson Imaging. 2016;44(1):105-11.
8. Abdel Razek AAK, Abdalla A, Elfar R, Ashmalla GA, Ali K, Barakat T. Assessment of Diffusion Tensor Imaging Parameters of Hepatic Parenchyma for Differentiation of Biliary Atresia from Alagille Syndrome. Korean J Radiol. 2020;21(12):1367-73.
9. Cheung JS, Fan SJ, Gao DS, Chow AM, Man K, Wu EX. Diffusion tensor imaging of liver fibrosis in an experimental model. J Magn Reson Imaging. 2010;32(5):1141-8.
10. Dinani AM, Kowdley KV, Noureddin M. Application of Artificial Intelligence for Diagnosis and Risk Stratification in NAFLD and NASH: The State of the Art. Hepatology. 2021;74(4):2233-40.
11. Chang D, Truong E, Mena EA, Pacheco F, Wong M, Guindi M, et al. Machine learning models are superior to noninvasive tests in identifying clinically significant stages of NAFLD and NAFLD-related cirrhosis. Hepatology. 2023;77(2):546-57.
12. Lee J, Westphal M, Vali Y, Boursier J, Petta S, Ostroff R, et al. Machine learning algorithm improves the detection of NASH (NAS-based) and at-risk NASH: A development and validation study. Hepatology. 2023;78(1):258-71.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/466-02-006