Synthesized Gd-EOB-DTPA-enhanced hepatobiliary phase MR images via generative adversarial learning

Motivation: Gd-EOB-DTPA-enhanced hepatobiliary phase (HBP) imaging is clinical routine for liver lesion identification, and is usually empirically conducted at 20 minutes after bolus injection. Goal(s): Our goal was to demonstrate the feasibility of optimizing clinical workflow by synthesizing Gd-EOB-DTPA-enhanced HBP images via machine learning. Approach: Precontrast and early-enhanced T1WIs (5-min after bolus injection) acquired at 3 T were used to synthesize HBP images via a generative adversarial network in 490 subjects. Results: Our preliminary results showed that synthesized HBP images are visually comparable to acquired HBP images with high SSIM(0.87±0.08) and PSNR(29.6±2.25). Impact: Machine learning synthesized HBP images could provide comparable diagnostic information to acquired HBP images, suggesting that machine learning might be used to optimize clinical workflow and greatly shorten acquisition time for Gd-EOB-DTPA-enhanced MRI.