Virtual Multi‐Phase Contrast Enhanced Liver MRI Using Deep Learning for Evaluating Hepatocellular Carcinoma

A deep learning (DL) model was developed to generate contrast‐enhanced MRI ( CE ‐MRI) at multiple enhancement phases (arterial, portal venous, transitional, and hepatobiliary phases) for detecting hepatocellular carcinoma (HCC). In total, 717 patients with HCC or other non‐HCC liver diseases were included, and the DL model was trained to synthesize CE ‐MRI. Three radiologists were invited to assess image quality, diagnostic performance, utility for the Liver Imaging Reporting and Data System (LI‐RADS), and image artifacts. The image quality of DL‐synthesized CE ‐MRI was non‐inferior to that of actual CE ‐MRI ( p ‐values < 0.001), and there was excellent agreement between DL‐synthesized and actual CE ‐MRI for visualizing the LI‐RADS major features. The diagnostic performance of DL‐synthesized CE ‐MRI for HCC (sensitivity = 0.880, specificity = 0.950, AUC = 0.915) was non‐inferior to that of actual CE ‐MRI. Notably, the proposed DL model required only 0.20–0.60 s to obtain multi‐phase CE ‐MRI, compared with more than 20 min needed for actual multi‐phase CE ‐MRI. Overall, the observed advantages of the proposed DL‐based strategy included eliminating the need for GBCAs, high time efficiency, comparable image quality, high diagnostic performance, validity for LI‐RADS, and robustness to image artifacts, indicating potential for clinical translation that may benefit both patients and healthcare providers.