HepatoGEN: Generating Hepatobiliary Phase MRI with Perceptual and Adversarial Models
This work addresses a clinical problem for liver MRI patients and radiologists by potentially reducing scan times without compromising diagnostic utility, though it is incremental as it compares existing generative models on a new medical imaging task.
The study tackled the problem of prolonged scan times for hepatobiliary phase (HBP) MRI by synthesizing HBP images from earlier contrast phases using deep learning models, finding that a perceptual GAN achieved the best quantitative performance but a U-Net produced more consistent liver enhancement with fewer artifacts.
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays a crucial role in the detection and characterization of focal liver lesions, with the hepatobiliary phase (HBP) providing essential diagnostic information. However, acquiring HBP images requires prolonged scan times, which may compromise patient comfort and scanner throughput. In this study, we propose a deep learning based approach for synthesizing HBP images from earlier contrast phases (precontrast and transitional) and compare three generative models: a perceptual U-Net, a perceptual GAN (pGAN), and a denoising diffusion probabilistic model (DDPM). We curated a multi-site DCE-MRI dataset from diverse clinical settings and introduced a contrast evolution score (CES) to assess training data quality, enhancing model performance. Quantitative evaluation using pixel-wise and perceptual metrics, combined with qualitative assessment through blinded radiologist reviews, showed that pGAN achieved the best quantitative performance but introduced heterogeneous contrast in out-of-distribution cases. In contrast, the U-Net produced consistent liver enhancement with fewer artifacts, while DDPM underperformed due to limited preservation of fine structural details. These findings demonstrate the feasibility of synthetic HBP image generation as a means to reduce scan time without compromising diagnostic utility, highlighting the clinical potential of deep learning for dynamic contrast enhancement in liver MRI. A project demo is available at: https://jhooge.github.io/hepatogen