Advances in imaging diagnosis of metabolic dysfunction-associated fatty liver disease_CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Authors：

PENG Jinhui ^1,2 , WANG Jingjing ² ,  YANGDAN Cairang ²

1. Medical College of Qinghai University, Qinghai 810001, P. R. China;
2. Department of Hepatobiliary-Pancreatic Surgery, Affiliated Hospital of Qinghai University, Qinghai 810001, P. R. China;

Corresponding?author：

YANGDAN Cairang, Email: yangdancairang@163.com

Keywords：

metabolic dysfunction-associated fatty liver disease; imaging techniques; diagnosis; advancement

DOI：

10.7507/1007-9424.202506083

Video：

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Abstract Full text Figures/Tables Video References Cited by

Objective To evaluate the application value of imaging techniques in the noninvasive diagnosis of metabolic dysfunction-associated fatty liver disease (MAFLD), with a view to providing a reference basis for early screening and precise diagnosis and treatment. Methods The literature on research progress of ultrasound, CT, MRI, and other imaging techniques in the diagnosis of MAFLD in recent years was reviewed. And the advantages, limitations, and clinical application prospects of different methods were analyzed. Results The core of MAFLD diagnosis lies in the early noninvasive assessment of hepatic steatosis and fibrosis. Current imaging techniques such as ultrasound, CT, and MRI are used in the diagnosis of MAFLD. Ultrasound is convenient and cost-effective, but its accuracy is operator-dependent; CT can quantify liver fat content, but carries radiation risks; MRI-derived proton density fat fraction is currently the reference standard for noninvasive diagnosis, offering high accuracy, yet it is more costly and has limited accessibility. Emerging technologies like artificial intelligence-assisted image analysis can improve diagnostic performance, but their clinical utility still requires further validation. Conclusions Current imaging techniques such as ultrasound, CT, and MRI play significant roles in the noninvasive diagnosis of MAFLD, with each method having its own advantages. Clinical selection should consider accuracy, accessibility, and economic factors. Future research should focus on optimizing existing technologies, exploring multimodal imaging integration, and developing artificial intelligence assisted diagnostics to enhance early detection rate and guide personalized treatment.

Citation： PENG Jinhui, WANG Jingjing, YANGDAN Cairang. Advances in imaging diagnosis of metabolic dysfunction-associated fatty liver disease. CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY, 2025, 32(11): 1487-1492. doi: 10.7507/1007-9424.202506083 Copy

1.	Devarbhavi H, Asrani SK, Arab JP, et al. Global burden of liver disease: 2023 update. J Hepatol, 2023, 79(2): 516-537.
2.	En Li Cho E, Ang CZ, Quek J, et al. Global prevalence of non-alcoholic fatty liver disease in type 2 diabetes mellitus: an updated systematic review and meta-analysis. Gut, 2023, 72(11): 2138-2148.
3.	Lee HH, Lee HA, Kim EJ, et al. Metabolic dysfunction-associated steatotic liver disease and risk of cardiovascular disease. Gut, 2024, 73(3): 533-540.
4.	Chew NWS, Ng CH, Tan DJH, et al. The global burden of metabolic disease: data from 2000 to 2019. Cell Metab, 2023, 35(3): 414-428.
5.	Duell PB, Welty FK, Miller M, et al. Nonalcoholic fatty liver disease and cardiovascular risk: a scientific statement from the American Heart Association. Arterioscler Thromb Vasc Biol, 2022, 42(6): e168-e185. doi: 10.1161/ATV.0000000000000153.
6.	Chang Y, Kim JI, Lee B, et al. Clinical application of ultrasonography-guided percutaneous liver biopsy and its safety over 18 years. Clin Mol Hepatol, 2020, 26(3): 318-327.
7.	Eslam M, Sarin SK, Wong VW, et al. The Asian Pacific Association for the study of the liver clinical practice guidelines for the diagnosis and management of metabolic associated fatty liver disease. Hepatol Int, 2020, 14(6): 889-919.
8.	Ozturk A, Kumar V, Pierce TT, et al. The future is beyond bright: the evolving role of quantitative US for fatty liver disease. Radiology, 2023, 309(2): e223146. doi: 10.1148/radiol.223146.
9.	Paige JS, Bernstein GS, Heba E, et al. A pilot comparative study of quantitative ultrasound, conventional ultrasound, and MRI for predicting histology-determined steatosis grade in adult nonalcoholic fatty liver disease. AJR AM J Roentgenol, 2017, 208(5): W168-W177. doi: 10.2214/AJR.16.16726.
10.	Kjaergaard M, Lindvig KP, Hansen CD, et al. Hepatorenal index by B-mode ratio versus imaging and fatty liver index to diagnose steatosis in alcohol-related and nonalcoholic fatty liver disease. J Ultrasound Med, 2023, 42(2): 487-496.
11.	Marshall RH, Eissa M, Bluth EI, et al. Hepatorenal index as an accurate, simple, and effective tool in screening for steatosis. AJR Am J Roentgenol, 2012, 199(5): 997-1002.
12.	Srigandan S, Zelesco M, Abbott S, et al. Correlation between hepatorenal index and attenuation imaging for assessing hepatic steatosis. Australas J Ultrasound Med, 2022, 25(3): 107-115.
13.	Zsombor Z, Rónaszéki AD, Csongrády B, et al. Evaluation of artificial intelligence-calculated hepatorenal index for diagnosing mild and moderate hepatic steatosis in non-alcoholic fatty liver disease. Medicina (Kaunas), 2023, 59(3): 469. doi: 10.3390/medicina59030469.
14.	董怡, 王文平. 肝脂肪變性的超聲檢測技術及其應用前景. 腫瘤影像學, 2023, 32(4): 317-323.
15.	Pirmoazen AM, Khurana A, Loening AM, et al. Diagnostic performance of 9 quantitative ultrasound parameters for detection and classification of hepatic steatosis in nonalcoholic fatty liver disease. Invest Radiol, 2022, 57(1): 23-32.
16.	Baumeler S, Jochum W, Neuweiler J, et al. Controlled attenuation parameter for the assessment of liver steatosis in comparison with liver histology: a single-centre real life experience. Swiss Med Wkly, 2019, 149: w20077. doi: 10.4414/smw.2019.20077.
17.	de Lédinghen V, Wong GL, Vergniol J, et al. Controlled attenuation parameter for the diagnosis of steatosis in non-alcoholic fatty liver disease. J Gastroenterol Hepatol, 2016, 31(4): 848-855.
18.	Ferraioli G, Berzigotti A, Barr RG, et al. Quantification of liver fat content with ultrasound: a WFUMB position paper. Ultrasound Med Biol, 2021, 47(10): 2803-2820.
19.	Pu K, Wang Y, Bai S, et al. Diagnostic accuracy of controlled attenuation parameter (CAP) as a non-invasive test for steatosis in suspected non-alcoholic fatty liver disease: a systematic review and meta-analysis. BMC Gastroenterol, 2019, 19(1): 51. doi: 10.1186/s12876-019-0961-9.
20.	Bae JS, Lee DH, Lee JY, et al. Assessment of hepatic steatosis by using attenuation imaging: a quantitative, easy-to-perform ultrasound technique. Eur Radiol, 2019, 29(12): 6499-6507.
21.	Liu GT, Ni QF, Zhang YH, et al. Application of noninvasive test (acoustic attenuation imaging and ultrasonic shear wave elastography) to grade nonalcoholic fatty liver disease: an observational study. Medicine (Baltimore), 2023, 102(32): e34550. doi: 10.1097/MD.0000000000034550.
22.	Petroff D, Blank V, Newsome PN, et al. Assessment of hepatic steatosis by controlled attenuation parameter using the M and XL probes: an individual patient data meta-analysis. Lancet Gastroenterol Hepatol, 2021, 6(3): 185-198.
23.	Zamanian H, Mostaar A, Azadeh P, et al. Implementation of combinational deep learning algorithm for non-alcoholic fatty liver classification in ultrasound images. J Biomed Phys Eng, 2021, 11(1): 73-84.
24.	Nduma BN, Al-Ajlouni YA, Njei B. The application of artificial intelligence (AI)-based ultrasound for the diagnosis of fatty liver disease: a systematic review. Cureus, 2023, 15(12): e50601. doi: 10.7759/cureus.50601.
25.	Pickhardt PJ, Graffy PM, Reeder SB, et al. Quantification of liver fat content with unenhanced MDCT: phantom and clinical correlation with MRI proton density fat fraction. AJR Am J Roentgenol, 2018, 211(3): W151-W157. doi: 10.2214/AJR.17.19391.
26.	Starekova J, Hernando D, Pickhardt PJ, et al. Quantification of liver fat content with CT and MRI: state of the art. Radiology, 2021, 301(2): 250-262.
27.	Pickhardt PJ, Park SH, Hahn L, et al. Specificity of unenhanced CT for non-invasive diagnosis of hepatic steatosis: implications for the investigation of the natural history of incidental steatosis. Eur Radiol, 2012, 22(5): 1075-1082.
28.	Kodama Y, Ng CS, Wu TT, et al. Comparison of CT methods for determining the fat content of the liver. AJR Am J Roentgenol, 2007, 188(5): 1307-1312.
29.	Meloni A, Frijia F, Panetta D, et al. Photon-counting computed tomography (PCCT): technical background and cardio-vascular applications. Diagnostics (Basel), 2023, 13(4): 645. doi: 10.3390/diagnostics13040645.
30.	Niehoff JH, Woeltjen MM, Saeed S, et al. Assessment of hepatic steatosis based on virtual non-contrast computed tomography: initial experiences with a photon counting scanner approved for clinical use. Eur J Radiol, 2022, 149: 110185. doi: 10.1016/j.ejrad.2022.110185.
31.	Kullberg J, Hedstr?m A, Brandberg J, et al. Automated analysis of liver fat, muscle and adipose tissue distribution from CT suitable for large-scale studies. Sci Rep, 2017, 7(1): 10425. doi: 10.1038/s41598-017-08925-8.
32.	Huo Y, Terry JG, Wang J, et al. Fully automatic liver attenuation estimation combing CNN segmentation and morphological operations. Med Phys, 2019, 46(8): 3508-3519.
33.	Nachit M, Horsmans Y, Summers RM, et al. AI-based CT body composition identifies myosteatosis as key mortality predictor in asymptomatic adults. Radiology, 2023, 307(5): e222008. doi: 10.1148/radiol.222008.
34.	Tahmasebi A, Wessner CE, Guglielmo FF, et al. Comparison of magnetic resonance-based elastography and ultrasound shear wave elastography in patients with suspicion of nonalcoholic fatty liver disease. Ultrasound Q, 2023, 39(2): 100-108.
35.	Selvaraj EA, Mózes FE, Jayaswal ANA, et al. Diagnostic accuracy of elastography and magnetic resonance imaging in patients with NAFLD: a systematic review and meta-analysis. J Hepatol, 2021, 75(4): 770-785.
36.	Idilman IS, Yildiz AE, Karaosmanoglu AD, et al. Proton density fat fraction: magnetic resonance imaging applications beyond the liver. Diagn Interv Radiol, 2022, 28(1): 83-91.
37.	Azizi N, Naghibi H, Shakiba M, et al. Evaluation of MRI proton density fat fraction in hepatic steatosis: a systematic review and meta-analysis. Eur Radiol, 2025, 35(4): 1794-1807.
38.	Gu J, Liu S, Du S, et al. Diagnostic value of MRI-PDFF for hepatic steatosis in patients with non-alcoholic fatty liver disease: a meta-analysis. Eur Radiol, 2019, 29(7): 3564-3573.
39.	Gu Q, Cen L, Lai J, et al. A meta-analysis on the diagnostic performance of magnetic resonance imaging and transient elastography in nonalcoholic fatty liver disease. Eur J Clin Invest, 2021, 51(2): e13446. doi: 10.1111/eci.13446.
40.	Yuan K, Liu Q, Huangfu X, et al. Diagnostic accuracy of hepatic MRI-PDFF and R2^* for the evaluation of liver steatosis and liver iron overload: a meta-analysis. Acad Radiol, 2025: S1076-6332(25)00528-8. doi: 10.1016/j.acra.2025.05.051.
41.	Zhang YX, Feng YP, You CL, 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. BMC Gastroenterol, 2025, 25(1): 451. doi: 10.1186/s12876-025-04017-4.
42.	Jung J, Han A, Madamba E, et al. Direct comparison of quantitative US versus controlled attenuation parameter for liver fat assessment using MRI proton density fat fraction as the reference standard in patients suspected of having NAFLD. Radiology, 2022, 304(1): 75-82.
43.	中華醫學會肝病學分會, 范建高, 南月敏, 等. 代謝相關(非酒精性)脂肪性肝病防治指南(2024年版). 實用肝臟病雜志, 2024, 27(4): 494-510.
44.	Montalt-Tordera J, Quail M, Steeden JA, et al. Reducing contrast agent dose in cardiovascular MR angiography with deep learning. J Magn Reson Imaging, 2021, 54(3): 795-805.
45.	McDonald JS, Larson NB, Schmitz JJ, et al. Acute adverse events after iodinated contrast agent administration of 359 977 injections: a single-center retrospective study. Mayo Clin Proc, 2023, 98(12): 1820-1830.
46.	Luo H, Zhang T, Gong NJ, et al. Deep learning-based methods may minimize GBCA dosage in brain MRI. Eur Radiol, 2021, 31(9): 6419-6428.
47.	Pasumarthi S, Tamir JI, Christensen S, et al. A generic deep learning model for reduced gadolinium dose in contrast-enhanced brain MRI. Magn Reson Med, 2021, 86(3): 1687-1700.
48.	Müller-Franzes G, Huck L, Bode M, et al. Diffusion probabilistic versus generative adversarial models to reduce contrast agent dose in breast MRI. Eur Radiol Exp, 2024, 8(1): 53. doi: 10.1186/s41747-024-00451-3.
49.	黃子星. 影像結構化報告: 放射科醫生從影像描述者到臨床決策賦能者. 中國普外基礎與臨床雜志, 2025, 32(5): 529-533.

1. Devarbhavi H, Asrani SK, Arab JP, et al. Global burden of liver disease: 2023 update. J Hepatol, 2023, 79(2): 516-537.
2. En Li Cho E, Ang CZ, Quek J, et al. Global prevalence of non-alcoholic fatty liver disease in type 2 diabetes mellitus: an updated systematic review and meta-analysis. Gut, 2023, 72(11): 2138-2148.
3. Lee HH, Lee HA, Kim EJ, et al. Metabolic dysfunction-associated steatotic liver disease and risk of cardiovascular disease. Gut, 2024, 73(3): 533-540.
4. Chew NWS, Ng CH, Tan DJH, et al. The global burden of metabolic disease: data from 2000 to 2019. Cell Metab, 2023, 35(3): 414-428.
5. Duell PB, Welty FK, Miller M, et al. Nonalcoholic fatty liver disease and cardiovascular risk: a scientific statement from the American Heart Association. Arterioscler Thromb Vasc Biol, 2022, 42(6): e168-e185. doi: 10.1161/ATV.0000000000000153.
6. Chang Y, Kim JI, Lee B, et al. Clinical application of ultrasonography-guided percutaneous liver biopsy and its safety over 18 years. Clin Mol Hepatol, 2020, 26(3): 318-327.
7. Eslam M, Sarin SK, Wong VW, et al. The Asian Pacific Association for the study of the liver clinical practice guidelines for the diagnosis and management of metabolic associated fatty liver disease. Hepatol Int, 2020, 14(6): 889-919.
8. Ozturk A, Kumar V, Pierce TT, et al. The future is beyond bright: the evolving role of quantitative US for fatty liver disease. Radiology, 2023, 309(2): e223146. doi: 10.1148/radiol.223146.
9. Paige JS, Bernstein GS, Heba E, et al. A pilot comparative study of quantitative ultrasound, conventional ultrasound, and MRI for predicting histology-determined steatosis grade in adult nonalcoholic fatty liver disease. AJR AM J Roentgenol, 2017, 208(5): W168-W177. doi: 10.2214/AJR.16.16726.
10. Kjaergaard M, Lindvig KP, Hansen CD, et al. Hepatorenal index by B-mode ratio versus imaging and fatty liver index to diagnose steatosis in alcohol-related and nonalcoholic fatty liver disease. J Ultrasound Med, 2023, 42(2): 487-496.
11. Marshall RH, Eissa M, Bluth EI, et al. Hepatorenal index as an accurate, simple, and effective tool in screening for steatosis. AJR Am J Roentgenol, 2012, 199(5): 997-1002.
12. Srigandan S, Zelesco M, Abbott S, et al. Correlation between hepatorenal index and attenuation imaging for assessing hepatic steatosis. Australas J Ultrasound Med, 2022, 25(3): 107-115.
13. Zsombor Z, Rónaszéki AD, Csongrády B, et al. Evaluation of artificial intelligence-calculated hepatorenal index for diagnosing mild and moderate hepatic steatosis in non-alcoholic fatty liver disease. Medicina (Kaunas), 2023, 59(3): 469. doi: 10.3390/medicina59030469.
14. 董怡, 王文平. 肝脂肪變性的超聲檢測技術及其應用前景. 腫瘤影像學, 2023, 32(4): 317-323.
15. Pirmoazen AM, Khurana A, Loening AM, et al. Diagnostic performance of 9 quantitative ultrasound parameters for detection and classification of hepatic steatosis in nonalcoholic fatty liver disease. Invest Radiol, 2022, 57(1): 23-32.
16. Baumeler S, Jochum W, Neuweiler J, et al. Controlled attenuation parameter for the assessment of liver steatosis in comparison with liver histology: a single-centre real life experience. Swiss Med Wkly, 2019, 149: w20077. doi: 10.4414/smw.2019.20077.
17. de Lédinghen V, Wong GL, Vergniol J, et al. Controlled attenuation parameter for the diagnosis of steatosis in non-alcoholic fatty liver disease. J Gastroenterol Hepatol, 2016, 31(4): 848-855.
18. Ferraioli G, Berzigotti A, Barr RG, et al. Quantification of liver fat content with ultrasound: a WFUMB position paper. Ultrasound Med Biol, 2021, 47(10): 2803-2820.
19. Pu K, Wang Y, Bai S, et al. Diagnostic accuracy of controlled attenuation parameter (CAP) as a non-invasive test for steatosis in suspected non-alcoholic fatty liver disease: a systematic review and meta-analysis. BMC Gastroenterol, 2019, 19(1): 51. doi: 10.1186/s12876-019-0961-9.
20. Bae JS, Lee DH, Lee JY, et al. Assessment of hepatic steatosis by using attenuation imaging: a quantitative, easy-to-perform ultrasound technique. Eur Radiol, 2019, 29(12): 6499-6507.
21. Liu GT, Ni QF, Zhang YH, et al. Application of noninvasive test (acoustic attenuation imaging and ultrasonic shear wave elastography) to grade nonalcoholic fatty liver disease: an observational study. Medicine (Baltimore), 2023, 102(32): e34550. doi: 10.1097/MD.0000000000034550.
22. Petroff D, Blank V, Newsome PN, et al. Assessment of hepatic steatosis by controlled attenuation parameter using the M and XL probes: an individual patient data meta-analysis. Lancet Gastroenterol Hepatol, 2021, 6(3): 185-198.
23. Zamanian H, Mostaar A, Azadeh P, et al. Implementation of combinational deep learning algorithm for non-alcoholic fatty liver classification in ultrasound images. J Biomed Phys Eng, 2021, 11(1): 73-84.
24. Nduma BN, Al-Ajlouni YA, Njei B. The application of artificial intelligence (AI)-based ultrasound for the diagnosis of fatty liver disease: a systematic review. Cureus, 2023, 15(12): e50601. doi: 10.7759/cureus.50601.
25. Pickhardt PJ, Graffy PM, Reeder SB, et al. Quantification of liver fat content with unenhanced MDCT: phantom and clinical correlation with MRI proton density fat fraction. AJR Am J Roentgenol, 2018, 211(3): W151-W157. doi: 10.2214/AJR.17.19391.
26. Starekova J, Hernando D, Pickhardt PJ, et al. Quantification of liver fat content with CT and MRI: state of the art. Radiology, 2021, 301(2): 250-262.
27. Pickhardt PJ, Park SH, Hahn L, et al. Specificity of unenhanced CT for non-invasive diagnosis of hepatic steatosis: implications for the investigation of the natural history of incidental steatosis. Eur Radiol, 2012, 22(5): 1075-1082.
28. Kodama Y, Ng CS, Wu TT, et al. Comparison of CT methods for determining the fat content of the liver. AJR Am J Roentgenol, 2007, 188(5): 1307-1312.
29. Meloni A, Frijia F, Panetta D, et al. Photon-counting computed tomography (PCCT): technical background and cardio-vascular applications. Diagnostics (Basel), 2023, 13(4): 645. doi: 10.3390/diagnostics13040645.
30. Niehoff JH, Woeltjen MM, Saeed S, et al. Assessment of hepatic steatosis based on virtual non-contrast computed tomography: initial experiences with a photon counting scanner approved for clinical use. Eur J Radiol, 2022, 149: 110185. doi: 10.1016/j.ejrad.2022.110185.
31. Kullberg J, Hedstr?m A, Brandberg J, et al. Automated analysis of liver fat, muscle and adipose tissue distribution from CT suitable for large-scale studies. Sci Rep, 2017, 7(1): 10425. doi: 10.1038/s41598-017-08925-8.
32. Huo Y, Terry JG, Wang J, et al. Fully automatic liver attenuation estimation combing CNN segmentation and morphological operations. Med Phys, 2019, 46(8): 3508-3519.
33. Nachit M, Horsmans Y, Summers RM, et al. AI-based CT body composition identifies myosteatosis as key mortality predictor in asymptomatic adults. Radiology, 2023, 307(5): e222008. doi: 10.1148/radiol.222008.
34. Tahmasebi A, Wessner CE, Guglielmo FF, et al. Comparison of magnetic resonance-based elastography and ultrasound shear wave elastography in patients with suspicion of nonalcoholic fatty liver disease. Ultrasound Q, 2023, 39(2): 100-108.
35. Selvaraj EA, Mózes FE, Jayaswal ANA, et al. Diagnostic accuracy of elastography and magnetic resonance imaging in patients with NAFLD: a systematic review and meta-analysis. J Hepatol, 2021, 75(4): 770-785.
36. Idilman IS, Yildiz AE, Karaosmanoglu AD, et al. Proton density fat fraction: magnetic resonance imaging applications beyond the liver. Diagn Interv Radiol, 2022, 28(1): 83-91.
37. Azizi N, Naghibi H, Shakiba M, et al. Evaluation of MRI proton density fat fraction in hepatic steatosis: a systematic review and meta-analysis. Eur Radiol, 2025, 35(4): 1794-1807.
38. Gu J, Liu S, Du S, et al. Diagnostic value of MRI-PDFF for hepatic steatosis in patients with non-alcoholic fatty liver disease: a meta-analysis. Eur Radiol, 2019, 29(7): 3564-3573.
39. Gu Q, Cen L, Lai J, et al. A meta-analysis on the diagnostic performance of magnetic resonance imaging and transient elastography in nonalcoholic fatty liver disease. Eur J Clin Invest, 2021, 51(2): e13446. doi: 10.1111/eci.13446.
40. Yuan K, Liu Q, Huangfu X, et al. Diagnostic accuracy of hepatic MRI-PDFF and R2^* for the evaluation of liver steatosis and liver iron overload: a meta-analysis. Acad Radiol, 2025: S1076-6332(25)00528-8. doi: 10.1016/j.acra.2025.05.051.
41. Zhang YX, Feng YP, You CL, 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. BMC Gastroenterol, 2025, 25(1): 451. doi: 10.1186/s12876-025-04017-4.
42. Jung J, Han A, Madamba E, et al. Direct comparison of quantitative US versus controlled attenuation parameter for liver fat assessment using MRI proton density fat fraction as the reference standard in patients suspected of having NAFLD. Radiology, 2022, 304(1): 75-82.
43. 中華醫學會肝病學分會, 范建高, 南月敏, 等. 代謝相關(非酒精性)脂肪性肝病防治指南(2024年版). 實用肝臟病雜志, 2024, 27(4): 494-510.
44. Montalt-Tordera J, Quail M, Steeden JA, et al. Reducing contrast agent dose in cardiovascular MR angiography with deep learning. J Magn Reson Imaging, 2021, 54(3): 795-805.
45. McDonald JS, Larson NB, Schmitz JJ, et al. Acute adverse events after iodinated contrast agent administration of 359 977 injections: a single-center retrospective study. Mayo Clin Proc, 2023, 98(12): 1820-1830.
46. Luo H, Zhang T, Gong NJ, et al. Deep learning-based methods may minimize GBCA dosage in brain MRI. Eur Radiol, 2021, 31(9): 6419-6428.
47. Pasumarthi S, Tamir JI, Christensen S, et al. A generic deep learning model for reduced gadolinium dose in contrast-enhanced brain MRI. Magn Reson Med, 2021, 86(3): 1687-1700.
48. Müller-Franzes G, Huck L, Bode M, et al. Diffusion probabilistic versus generative adversarial models to reduce contrast agent dose in breast MRI. Eur Radiol Exp, 2024, 8(1): 53. doi: 10.1186/s41747-024-00451-3.
49. 黃子星. 影像結構化報告: 放射科醫生從影像描述者到臨床決策賦能者. 中國普外基礎與臨床雜志, 2025, 32(5): 529-533.

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Advances in imaging diagnosis of metabolic dysfunction-associated fatty liver disease

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