Multiparametric magnetic resonance imaging of deep learning-based super-resolution reconstruction for predicting histopathologic grade in hepatocellular carcinoma

doi:10.3748/wjg.v31.i34.111541

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World Journal of Gastroenterology

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1007-9327

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

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World J Gastroenterol. Sep 14, 2025; 31(34): 111541
Published online Sep 14, 2025. doi: 10.3748/wjg.v31.i34.111541

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Figure 1 Diagram of generative adversarial networks used to generate super-resolution images from original normal-resolution images. MRI: Magnetic resonance imaging.

Citation: Wang ZZ, Song SM, Zhang G, Chen RQ, Zhang ZC, Liu R. Multiparametric magnetic resonance imaging of deep learning-based super-resolution reconstruction for predicting histopathologic grade in hepatocellular carcinoma. World J Gastroenterol 2025; 31(34): 111541
URL: https://www.wjgnet.com/1007-9327/full/v31/i34/111541.htm
DOI: https://dx.doi.org/10.3748/wjg.v31.i34.111541

Wang ZZ, Song SM, Zhang G, Chen RQ, Zhang ZC, Liu R. Multiparametric magnetic resonance imaging of deep learning-based super-resolution reconstruction for predicting histopathologic grade in hepatocellular carcinoma. World J Gastroenterol 2025; 31(34): 111541 [PMID: 40937458 DOI: 10.3748/wjg.v31.i34.111541]

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