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Review
Functional magnetic resonance imaging in the progression of metabolic dysfunction-associated steatotic liver disease and metabolic dysfunction-associated steatohepatitis
ZHANG Jiarui  TANG Shuhong  CHEN Kai  ZHU Xiangqi  SHI Weijun  SUN Hongzan 

Cite this article as: ZHANG J R, TANG S H, CHEN K, et al. Functional magnetic resonance imaging in the progression of metabolic dysfunction-associated steatotic liver disease and metabolic dysfunction-associated steatohepatitis[J]. Chin J Magn Reson Imaging, 2026, 17(4): 192-198, 213. DOI:10.12015/issn.1674-8034.2026.04.027.


[Abstract] Metabolic dysfunction-associated steatotic liver disease (MASLD) and its progressive phenotype, metabolic dysfunction-associated steatohepatitis (MASH), represent a chronic spectrum of liver disorders that requires long-term, risk-stratified management, yet current assessment tools remain limited in precise quantification and longitudinal monitoring. MASLD/MASH is a spectrum of chronic liver diseases requiring long-term, stratified management. Current assessment tools fall short in precisely quantifying the degrees of hepatic steatosis, inflammation, and fibrosis, and lack the sensitivity to monitor the dynamic progression of the disease, which hinders its full-cycle precision management. Magnetic resonance functional imaging techniques have emerged as a research focus to address this clinical need, owing to their potential for non-invasive, quantitative, and multi-parameter assessment. This review systematically summarizes recent advances and the current status of clinical translation of functional magnetic resonance imaging in whole-course management of MASLD/MASH. We outline the principles underlying its noninvasive quantitative assessment of key pathological changes, including hepatic steatosis, inflammation, and fibrosis, and synthesize evidence for its utility in disease screening, risk stratification, treatment-response monitoring, and prognostic evaluation. We further discuss major barriers to broader clinical implementation, such as technical standardization, definition of diagnostic thresholds, and validation against clinical outcomes. Functional magnetic resonance imaging holds promise in establishing an objective and reproducible quantitative framework to support precision management of MASLD/MASH. This framework provides a robust theoretical foundation and practical benchmark for non-invasive disease stratification, standardized clinical workflows, longitudinal follow-up monitoring, and the design of high-quality clinical trials.
[Keywords] metabolic dysfunction-associated steatotic liver disease;metabolic dysfunction-associated steatohepatitis;magnetic resonance functional imaging;proton density fat fraction;magnetic resonance elastography;liver fibrosis

ZHANG Jiarui   TANG Shuhong   CHEN Kai   ZHU Xiangqi   SHI Weijun   SUN Hongzan*  

The Second Clinical College of China Medical University, Shenyang 110004, China

Corresponding author: SUN H Z, E-mail: sunhongzan@126.com

Conflicts of interest   None.

Received  2026-01-30
Accepted  2026-04-10
DOI: 10.12015/issn.1674-8034.2026.04.027
Cite this article as: ZHANG J R, TANG S H, CHEN K, et al. Functional magnetic resonance imaging in the progression of metabolic dysfunction-associated steatotic liver disease and metabolic dysfunction-associated steatohepatitis[J]. Chin J Magn Reson Imaging, 2026, 17(4): 192-198, 213. DOI:10.12015/issn.1674-8034.2026.04.027.

[1]
KONG J N, ZHANG B B, SHI J P. An excerpt of clinical practice guideline of prevention and treatment of metabolic dysfunction-associated (nonalcoholic) fatty liver disease (2024 edition)[J]. J Clin Hepatol, 2024, 40(9): 1767-1770. DOI: 10.12449/JCH240908.
[2]
RINELLA M E, LAZARUS J V, RATZIU V, et al. A multisociety Delphi consensus statement on new fatty liver disease nomenclature[J]. J Hepatol, 2023, 79(6): 1542-1556. DOI: 10.1016/j.jhep.2023.06.003.
[3]
TACKE F, HORN P, WAI-SUN WONG V, et al. EASL–EASD–EASO Clinical Practice Guidelines on the management of metabolic dysfunction-associated steatotic liver disease (MASLD)[J]. J Hepatol, 2024, 81(3): 492-542. DOI: 10.1016/j.jhep.2024.04.031.
[4]
Chinese Society of Hepatology of Chinese Medical Association. Guidelines for the prevention and treatment of metabolic dysfunction-associated (non-alcoholic) fatty liver disease (Version 2024)[J]. Chin J Hepatol, 2024, 32(5): 418-434. DOI: 10.3760/cma.j.cn501113-20240327-00163.
[5]
YOUNOSSI Z M, KALLIGEROS M, HENRY L. Epidemiology of metabolic dysfunction-associated steatotic liver disease[J]. Clin Mol Hepatol, 2025, 31: S32-S50. DOI: 10.3350/cmh.2024.0431.
[6]
ZENG J, FAN J G, FRANCQUE S M. Therapeutic management of metabolic dysfunction associated steatotic liver disease[J]. United European Gastroenterol J, 2024, 12(2): 177-186. DOI: 10.1002/ueg2.12525.
[7]
YU C C, CHEN L F, HU W T, et al. The role of the advanced lung cancer inflammation index (ALI) in the risk of liver fibrosis and mortality among US adult MAFLD patients: a cross-sectional study of NHANES 1999—2018[J/OL]. BMC Gastroenterol, 2025, 25(1): 190 [2026-01-30]. https://pubmed.ncbi.nlm.nih.gov/40114055/. DOI: 10.1186/s12876-025-03762-w.
[8]
YIN Y F, ZHU W J, XU Q L. The systemic inflammation response index as a risk factor for hepatic fibrosis and long-term mortality among individuals with metabolic dysfunction-associated steatotic liver disease[J]. Nutr Metab Cardiovasc Dis, 2024, 34(8): 1922-1931. DOI: 10.1016/j.numecd.2024.04.018.
[9]
TAYLOR R S, TAYLOR R J, BAYLISS S, et al. Association between fibrosis stage and outcomes of patients with nonalcoholic fatty liver disease: a systematic review and meta-analysis[J/OL]. Gastroenterology, 2020, 158(6): 1611-1625.e12 [2026-01-30]. https://pubmed.ncbi.nlm.nih.gov/32027911/. DOI: 10.1053/j.gastro.2020.01.043.
[10]
TADA T, KUMADA T, TOYODA H, et al. Association of liver fibrosis progression with non-liver-related mortality in metabolic dysfunction-associated steatotic liver disease[J]. Hepatol Res, 2025, 55(5): 648-662. DOI: 10.1111/hepr.14164.
[11]
ZHENG K I, LIU W Y, PAN X Y, et al. Combined and sequential non-invasive approach to diagnosing non-alcoholic steatohepatitis in patients with non-alcoholic fatty liver disease and persistently normal alanine aminotransferase levels[J/OL]. BMJ Open Diabetes Res Care, 2020, 8(1): e001174 [2026-01-30]. https://pubmed.ncbi.nlm.nih.gov/32139603/. DOI: 10.1136/bmjdrc-2020-001174.
[12]
CUSI K, ISAACS S, BARB D, et al. American association of clinical endocrinology clinical practice guideline for the diagnosis and management of nonalcoholic fatty liver disease in primary care and endocrinology clinical settings co-sponsored by the American association for the study of liver diseases (AASLD)[J]. Endocr Pract, 2022, 28(5): 528-562. DOI: 10.1016/j.eprac.2022.03.010.
[13]
NIRIELLA M A, KANAGARAJAH D, DE SILVA HEWAVISENTHI J, et al. Mistakes in utilising histopathology for the management of liver disease[J]. Expert Rev Gastroenterol Hepatol, 2024, 18(4/5): 147-153. DOI: 10.1080/17474124.2024.2355168.
[14]
GRAF M, GRAF C, ZIEGELMAYER S, et al. Complications of image-guided liver biopsies: Results of a nationwide database analysis[J/OL]. PLoS One, 2025, 20(5): e0323695 [2026-01-30]. https://pubmed.ncbi.nlm.nih.gov/40455799/. DOI: 10.1371/journal.pone.0323695.
[15]
COREY K E, NAKROUR N, BETHEA E D, et al. Real-world assessment of liver corrected T1 and magnetic resonance elastography in predicting liver disease progression[J/OL]. Liver Int, 2025, 45(9): e70280 [2026-01-30]. https://pubmed.ncbi.nlm.nih.gov/40810289/. DOI: 10.1111/liv.70280.
[16]
LIU G C, ZHANG H X. Progress in MRI evaluation of nonalcoholic fatty liver disease[J]. Chin J Magn Reson Imaging, 2024, 15(9): 201-204. DOI: 10.12015/issn.1674-8034.2024.09.035.
[17]
ZHANG Y X, FENG Y P, YOU C L, et al. The diagnostic value of MRI-PDFF in hepatic steatosis of patients with metabolic dysfunction-associated steatotic liver disease: a systematic review and meta-analysis[J/OL]. BMC Gastroenterol, 2025, 25(1): 451 [2026-01-30]. https://pubmed.ncbi.nlm.nih.gov/40596891/. DOI: 10.1186/s12876-025-04017-4.
[18]
BEYER C, ANDERSSON A, SHUMBAYAWONDA E, et al. Quantitative MRI for monitoring metabolic dysfunction-associated steatotic liver disease: a test-retest repeatability study[J]. J Magn Reson Imaging, 2025, 61(4): 1947-1955. DOI: 10.1002/jmri.29610.
[19]
WU Z Y, ZENG W W, YANG W T, et al. FAP-catalyzed in situ self-assembly of magnetic resonance imaging probe for early and precise staging of liver fibrosis[J/OL]. Sci Adv, 2025, 11(11): eadt6082 [2026-01-30]. https://pubmed.ncbi.nlm.nih.gov/40073128/. DOI: 10.1126/sciadv.adt6082.
[20]
GUGLIELMO F F, BARR R G, YOKOO T, et al. Liver fibrosis, fat, and iron evaluation with MRI and fibrosis and fat evaluation with US: a practical guide for radiologists[J/OL]. Radiographics, 2023, 43(6): e220181 [2026-01-30]. https://pubmed.ncbi.nlm.nih.gov/37227944/. DOI: 10.1148/rg.220181.
[21]
STAREKOVA J, HERNANDO D, PICKHARDT P J, et al. Quantification of liver fat content with CT and MRI: state of the art[J]. Radiology, 2021, 301(2): 250-262. DOI: 10.1148/radiol.2021204288.
[22]
LOOMBA R, NEUSCHWANDER-TETRI B A, SANYAL A, et al. Multicenter validation of association between decline in MRI-PDFF and histologic response in NASH[J]. Hepatology, 2020, 72(4): 1219-1229. DOI: 10.1002/hep.31121.
[23]
MA M Y, WANG J Y, LI X B, et al. Research progress and applications of fat quantification with magnetic resonance imaging[J]. Chin J Magn Reson Imaging, 2023, 14(7): 197-202. DOI: 10.12015/issn.1674-8034.2023.07.036.
[24]
YANG C, YU S H, XU F P, et al. Application of magnetic resonance imaging-proton density fat fraction in liver fat quantification[J]. J Clin Hepatol, 2024, 40(3): 600-605. DOI: 10.12449/JCH240327.
[25]
HERNANDO D, HU H H, REEDER S, et al. MRI-Based PDFF of the Liver, Consensus QIBA Profile[EB/OL]. (2024-06-19) [2026-01-30]. https://qibawiki.rsna.org/images/6/63/MRI-Based_PDFF_of_the_Liver_QIBA_Profile_2024_06-19_CONSENSUS-maintenance.pdf.
[26]
PENG F, LUO C T, NING X J, et al. Streamlining liver iron assessment: accuracy and limitations of 3D qDixon MRI for liver iron overload quantification[J/OL]. Eur J Radiol, 2025, 190: 112237 [2026-01-30]. https://pubmed.ncbi.nlm.nih.gov/40554144/. DOI: 10.1016/j.ejrad.2025.112237.
[27]
YUAN K C, LIU Q Y, HUANGFU X H, et al. Diagnostic accuracy of hepatic MRI-PDFF and R2* for the evaluation of liver steatosis and liver iron overload: a meta-analysis[J]. Acad Radiol, 2025, 32(11): 6541-6554. DOI: 10.1016/j.acra.2025.05.051.
[28]
ZHAO F Y, CHEN Y D, ZHOU T, et al. Application of the magnetic resonance 3D multiecho Dixon sequence for quantifying hepatic iron overload and steatosis in patients with thalassemia[J]. Magn Reson Imaging, 2024, 111: 28-34. DOI: 10.1016/j.mri.2024.03.015.
[29]
ZHANG H, ZOU L Q, ZHONG W X, et al. Quantitative evaluation of liver fibrosis by MRE and Gd-EOB-DTPA-enhanced T1 mapping magnetic resonance imaging in a rabbit model[J]. Chin J Magn Reson Imaging, 2024, 15(8): 172-178. DOI: 10.12015/issn.1674-8034.2024.08.026.
[30]
WANG Z L, LI N, KONG X X, et al. Detection of tumor immune checkpoints: from pathological analysis to functional imaging[J/OL]. Holist Integr Oncol, 2025, 4(1): 76 [2026-01-30]. https://link.springer.com/article/10.1007/s44178-025-00212-1. DOI: 10.1007/s44178-025-00212-1.
[31]
JIANG Z R, ZHANG H L, ZHU Z Y, et al. Advancements and applications of magnetic resonance elastography in chronic liver diseases[J]. Chin J Magn Reson Imaging, 2025, 16(3): 190-195. DOI: 10.12015/issn.1674-8034.2025.03.032.
[32]
MIAO M, ZHAO J. Progress in non-invasive elastography techniques for the diagnosis and assessment of metabolic dysfunction-associated steatotic liver disease[J]. Chin J Magn Reson Imaging, 2025, 16(11): 222-227. DOI: 10.12015/issn.1674-8034.2025.11.034.
[33]
SHUMBAYAWONDA E, FRENCH M, CAROLAN J E, et al. Utility and cost-effectiveness of LiverMultiScan for MASLD diagnosis: a real-world multi-national randomised clinical trial[J/OL]. Commun Med, 2025, 5(1): 74 [2026-01-30]. https://pubmed.ncbi.nlm.nih.gov/40102528/. DOI: 10.1038/s43856-025-00796-9.
[34]
XU H, LI Y G, MU J K, et al. Research on the evaluation of the liver function grading for the patients with hepatitis B cirrhosis using T1 mapping based extracellular volume fraction[J]. Chin J Magn Reson Imaging, 2023, 14(5): 132-138. DOI: 10.12015/issn.1674-8034.2023.05.023.
[35]
HEADLEY A M, GRICE J V, PICKENS D R. Reproducibility of liver iron concentration estimates in MRI through R2* measurement determined by least-squares curve fitting[J]. J Appl Clin Med Phys, 2020, 21(12): 295-303. DOI: 10.1002/acm2.13096.
[36]
ANDERSSON A, KELLY M, IMAJO K, et al. Clinical utility of magnetic resonance imaging biomarkers for identifying nonalcoholic steatohepatitis patients at high risk of progression: A multicenter pooled data and meta-analysis[J/OL]. Clin Gastroenterol Hepatol, 2022, 20(11): 2451-2461.e3 [2026-01-30]. https://pubmed.ncbi.nlm.nih.gov/34626833/. DOI: 10.1016/j.cgh.2021.09.041.
[37]
STAREKOVA J, RUTKOWSKI D, BAE W C, et al. 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[J]. J Magn Reson Imaging, 2025, 62(3): 800-811. DOI: 10.1002/jmri.29775.
[38]
FOWLER K J, VENKATESH S K, OBUCHOWSKI N, et al. Repeatability of MRI biomarkers in nonalcoholic fatty liver disease: the NIMBLE consortium[J/OL]. Radiology, 2023, 309(1): e231092 [2026-01-30]. https://pubmed.ncbi.nlm.nih.gov/37815451/. DOI: 10.1148/radiol.231092.
[39]
LIU B J, SHI Y W, ZHANG Z W, et al. Clinical application of LCBP risk assessment model in risk stratification of pulmonary nodules[J]. J Contemp Med Pract, 2025, 7(2): 150-154. DOI: 10.53469/jcmp.2025.07(02).29.
[40]
HUANG Y Q, CHEN X B, CUI Y F, et al. Enhanced risk stratification for stage II colorectal cancer using deep learning-based CT classifier and pathological markers to optimize adjuvant therapy decision[J]. Ann Oncol, 2025, 36(10): 1178-1189. DOI: 10.1016/j.annonc.2025.05.537.
[41]
KISIEL J B, EBBERT J O, TAYLOR W R, et al. Shifting the cancer screening paradigm: developing a multi-biomarker class approach to multi-cancer early detection testing[J/OL]. Life (Basel), 2024, 14(8): 925 [2026-01-30]. https://pubmed.ncbi.nlm.nih.gov/39202669/. DOI: 10.3390/life14080925.
[42]
WILSON M P, SINGH R, MEHTA S, et al. Comparing FIB-4, VCTE, pSWE, 2D-SWE, and MRE thresholds and diagnostic accuracies for detecting hepatic fibrosis in patients with MASLD: A systematic review and meta-analysis[J/OL]. Diagnostics (Basel), 2025, 15(13): 1598 [2026-01-30]. https://pubmed.ncbi.nlm.nih.gov/40647597/. DOI: 10.3390/diagnostics15131598.
[43]
RINELLA M E, NEUSCHWANDER-TETRI B A, SIDDIQUI M S, et al. AASLD Practice Guidance on the clinical assessment and management of nonalcoholic fatty liver disease[J]. Hepatology, 2023, 77(5): 1797-1835. DOI: 10.1097/hep.0000000000000323.
[44]
LIANG J X, AMPUERO J, NIU H, et al. An individual patient data meta-analysis to determine cut-offs for and confounders of NAFLD-fibrosis staging with magnetic resonance elastography[J]. J Hepatol, 2023, 79(3): 592-604. DOI: 10.1016/j.jhep.2023.04.025.
[45]
CHON Y E, JIN Y J, AN J, et al. Optimal cut-offs of vibration-controlled transient elastography and magnetic resonance elastography in diagnosing advanced liver fibrosis in patients with nonalcoholic fatty liver disease: A systematic review and meta-analysis[J]. Clin Mol Hepatol, 2024, 30: S117-S133. DOI: 10.3350/cmh.2024.0392.
[46]
AJMERA V, KIM B K, YANG K, et al. Liver stiffness on magnetic resonance elastography and the MEFIB index and liver-related outcomes in nonalcoholic fatty liver disease: A systematic review and meta-analysis of individual participants[J/OL]. Gastroenterology, 2022, 163(4): 1079-1089.e5 [2026-01-30]. https://pubmed.ncbi.nlm.nih.gov/35788349/. DOI: 10.1053/j.gastro.2022.06.073.
[47]
QI S, WEI X D, ZHAO J H, et al. Performance of MAST, FAST, and MEFIB in predicting metabolic dysfunction-associated steatohepatitis[J]. J Gastroenterol Hepatol, 2024, 39(8): 1656-1662. DOI: 10.1111/jgh.16589.
[48]
ZHU T T, CHEN Y Y, XIE F C, et al. Non-invasive assessment of liver fibrosis reverse in patients with chronic liver diseases[J]. J Pract Hepatol, 2025, 28(2): 169-172. DOI: 10.3969/j.issn.1672-5069.2025.02.003.
[49]
STINE J G, MUNAGANURU N, BARNARD A, et al. Change in MRI-PDFF and histologic response in patients with nonalcoholic steatohepatitis: A systematic review and meta-analysis[J/OL]. Clin Gastroenterol Hepatol, 2021, 19(11): 2274-2283.e5 [2026-01-30]. https://pubmed.ncbi.nlm.nih.gov/32882428/. DOI: 10.1016/j.cgh.2020.08.061.
[50]
PANSINI M, BEYER C, YALE K, et al. WED-234 Identifying the optimal cut off for relative reduction in liver fat content on MRI-PDFF to predict histologic response in MASH clinical trials[J/OL]. J Hepatol, 2024, 80: S522 [2026-01-30]. https://www.journal-of-hepatology.eu/article/S0168-8278(24)01573-3/abstract. DOI: 10.1016/S0168-8278(24)01573-3.
[51]
ZHOU I Y, CATALANO O A, CARAVAN P. Advances in functional and molecular MRI technologies in chronic liver diseases[J]. J Hepatol, 2020, 73(5): 1241-1254. DOI: 10.1016/j.jhep.2020.06.020.
[52]
LEMOINNE S, FRIEDMAN S L. New and emerging anti-fibrotic therapeutics entering or already in clinical trials in chronic liver diseases[J]. Curr Opin Pharmacol, 2019, 49: 60-70. DOI: 10.1016/j.coph.2019.09.006.
[53]
MÓZES F E, LEE J A, VALI Y, et al. Diagnostic accuracy of non-invasive tests to screen for at-risk MASH-An individual participant data meta-analysis[J]. Liver Int, 2024, 44(8): 1872-1885. DOI: 10.1111/liv.15914.
[54]
JACKSON E, DENNIS A, ALKHOURI N, et al. Cardiac and liver impairment on multiorgan MRI and risk of major adverse cardiovascular and liver events[J]. Nat Med, 2025, 31(7): 2289-2296. DOI: 10.1038/s41591-025-03654-2.
[55]
EXCELLENCE N I F H A C. MRI-based technologies for assessing non-alcoholic fatty liver disease[EB/OL]. (2023-01-12) [2026-01-30]. https://www.nice.org.uk/guidance/htg655.
[56]
HERNANDO D, ZHANG Y X, PIRASTEH A. Quantitative diffusion MRI of the abdomen and pelvis[J]. Med Phys, 2022, 49(4): 2774-2793. DOI: 10.1002/mp.15246.
[57]
HU H H, YOKOO T, BASHIR M R, et al. Linearity and bias of proton density fat fraction as a quantitative imaging biomarker: a multicenter, multiplatform, multivendor phantom study[J]. Radiology, 2021, 298(3): 640-651. DOI: 10.1148/radiol.2021202912.
[58]
POLEI S, LINDNER T, ABSHAGEN K, et al. 7 Tesla MRI liver fat quantification in mice: data quality assessment[J/OL]. Curr Med Imaging, 2024, 20: e15734056263741 [2026-01-30]. https://pubmed.ncbi.nlm.nih.gov/38389373/. DOI: 10.2174/0115734056263741231117112245.
[59]
DI VINCENZO A, CAPONE F, ROSSATO M. 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: comment[J]. Intern Emerg Med, 2025, 20(8): 2613-2614. DOI: 10.1007/s11739-025-04024-z.
[60]
TAMAKI N, MUNAGANURU N, JUNG J, et al. Clinical utility of 30% relative decline in MRI-PDFF in predicting fibrosis regression in non-alcoholic fatty liver disease[J]. Gut, 2022, 71(5): 983-990. DOI: 10.1136/gutjnl-2021-324264.
[61]
ZHU Z, LIANG Z H. Research progress on magnetic resonance imaging for non-invasive evaluation of liver fibrosis[J]. Int J Med Radiol, 2024, 47(6): 683-689. DOI: 10.19300/j.2024.Z21643.
[62]
MENESES J P, ARRIETA C, DELLA MAGGIORA G, et al. Liver PDFF estimation using a multi-decoder water-fat separation neural network with a reduced number of echoes[J]. Eur Radiol, 2023, 33(9): 6557-6568. DOI: 10.1007/s00330-023-09576-2.
[63]
MENESES J P, TEJOS C, MAKALIC E, et al. Non-iterative and uncertainty-aware MRI-based liver fat estimation using an unsupervised deep learning method[J/OL]. Med Image Anal, 2026, 107(Pt A): 103811 [2026-01-30]. https://pubmed.ncbi.nlm.nih.gov/40972442/. DOI: 10.1016/j.media.2025.103811.
[64]
NIEVES-VAZQUEZ H A, OZKAYA E, MEINHOLD W, et al. Deep learning-enabled automated quality control for liver MR elastography: initial results[J]. J Magn Reson Imaging, 2025, 61(2): 985-994. DOI: 10.1002/jmri.29490.
[65]
AYDE R, SENFT T, SALAMEH N, et al. Deep learning for fast low-field MRI acquisitions[J/OL]. Sci Rep, 2022, 12: 11394 [2026-01-30]. https://pubmed.ncbi.nlm.nih.gov/40972442/. DOI: 10.1038/s41598-022-14039-7.

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