Deep Learning-Based Generation of Synthetic Multiphasic MRI In Hepatocellular Carcinoma and Cirrhosis

This work demonstrates the early feasibility of generating high-quality, three-dimensional contrast-enhanced multiphasic liver MRI exams from precontrast sequences, with synthetic exams showing strong agreement with ground truth across quantitative metrics and key qualitative criteria, including the visual Turing test, image quality, disease diagnosability, anatomic accuracy, artifact severity, and hepatocellular carcinoma Liver Imaging Reporting and Data System features. Despite the model currently representing a proof of concept based on a moderate single-center dataset, with a need for larger multicenter studies and external validation, the results highlight the potential to transform liver MRI workflows by reducing contrast media costs and potential side effects, significantly shortening acquisition time-especially the prolonged 20-minute hepatobiliary phase-and improving accessibility for patients unable to tolerate contrast-enhanced MRI due to renal impairment, contrast agent allergy, or claustrophobia.