Deep learning-based generation of synthetic multiphasic MRI in hepatocellular carcinoma and cirrhosis

BACKGROUND & AIMS: There is growing interest in reducing contrast medium use and the lengthy scan duration in liver imaging. This proof of concept study evaluated the feasibility of deep learning-based generation of synthetic 3D liver contrast-enhanced multiphasic magnetic resonance imaging (MRI) exams, which are similar to ground-truth exams in hepatocellular carcinoma and cirrhosis. METHODS: MRI exams from patients with hepatocellular carcinoma (HCC) or cirrhosis at a single academic center were retrospectively collected. A 3D cycle-consistent generative adversarial network was trained to generate synthetic 3D T1-weighted contrast-enhanced multiphasic liver MRI exams, including arterial, portal venous, delayed, and hepatobiliary phases, using two pre-contrast T1-weighted and T2-weighted input phases. Quantitative performance evaluated similarity, error, and overlap metrics between synthetic and ground-truth exams. For the qualitative multireader study, three blinded radiologists assessed the ground-truth and synthetic MRI exams using a comprehensive questionnaire. Questionnaire tasks 1–5 comprised: visual Turing test (ground-truth vs. synthetic nature), image quality, anatomic accuracy, disease diagnosability, and artifacts. Task 6 comprised Liver Imaging Reporting and Data System features. RESULTS: The study included 3,198 MRI phases from 533 MRI exams from 185 patients with HCC (mean age, 62.1 years ± 9.7 [SD]; 141 men) and 182 patients with cirrhosis (54.4 years ± 10.0; 111 men). Synthetic MRI exams achieved high quantitative and qualitative similarity to ground-truth exams. Quantitative analysis demonstrated high structural similarity index (0.86 ± 0.03), overlap (0.97 ± 0.05), and low symmetric mean absolute percent error (0.63 ± 0.23%). The qualitative multireader study showed no significant difference in tasks 1–5 (p = 0.06–0.50) and high performance metrics in task 6 (accuracy: 0.76–0.86; precision: 0.96–1.00) with moderate to perfect Fleiss’s Kappa inter-rater agreement (0.58–1.00, p <0.001). CONCLUSIONS: Deep learning enabled the generation of synthetic 3D liver contrast-enhanced multiphasic MRI exams from precontrast sequences, achieving high quantitative and qualitative similarity to ground-truth images.