用早期MRI生成肝胆期图像，缩短扫描时间。
HepatoGEN: Generating Hepatobiliary Phase MRI with Perceptual and Adversarial Models
- 基于早期对比相位，用感知U-Net、GAN和扩散模型生成肝胆期图像。
- pGAN定量表现最好，但分布外数据出现对比不均；U-Net更稳定，伪影少。
- 适合想减少扫描时间又保持诊断质量的临床医生和影像算法研究者。
动态对比增强磁共振成像（DCE-MRI）在肝局灶性病灶的检测与定性中起关键作用，其中肝胆期（HBP）提供重要诊断信息。然而，获取HBP图像需延长扫描时间，可能影响患者舒适度和设备利用率。本研究提出一种基于深度学习的方法，从早期对比阶段（无对比和过渡期）合成HBP图像，并比较三种生成模型：感知U-Net、感知生成对抗网络（pGAN）和去噪扩散概率模型（DDPM）。我们构建了来自多种临床场景的多中心DCE-MRI数据集，并引入对比演化评分（CES）评估训练数据质量，提升模型性能。通过像素级与感知指标的定量评估，结合盲法放射科医生的定性评价发现，pGAN在定量上表现最优，但在分布外情况下引入不一致的对比度；U-Net生成的肝脏强化更一致，伪影更少；而DDPM因难以保留细结构细节表现较差。结果表明，合成HBP图像可有效缩短扫描时间且不影响诊断价值，凸显深度学习在肝脏DCE-MRI动态增强中的临床潜力。项目演示见：https://jhooge.github.io/hepatogen
原文摘要 · Abstract (English)
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
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