This paper presents a method for virtual contrast enhancement in breast MRI, offering a promising non-invasive alternative to traditional contrast agent-based DCE-MRI acquisition. Using a conditional generative adversarial network, we predict DCE-MRI images, including jointly-generated sequences of multiple corresponding DCE-MRI timepoints, from non-contrast-enhanced MRIs, enabling tumor localization and characterization without the associated health risks. Furthermore, we qualitatively and quantitatively evaluate the synthetic DCE-MRI images, proposing a multi-metric Scaled Aggregate Measure (SAMe), assessing their utility in a tumor segmentation downstream task, and conclude with an analysis of the temporal patterns in multi-sequence DCE-MRI generation. Our approach demonstrates promising results in generating realistic and useful DCE-MRI sequences, highlighting the potential of virtual contrast enhancement for improving breast cancer diagnosis and treatment, particularly for patients where contrast agent administration is contraindicated.

翻译：本文提出了一种乳腺磁共振成像（MRI）虚拟对比增强方法，为传统的基于对比剂的动态对比增强磁共振成像（DCE-MRI）采集提供了一种有前景的无创替代方案。我们利用条件生成对抗网络，从非对比增强的MRI图像中预测DCE-MRI图像，包括联合生成的多个对应DCE-MRI时间点序列，从而在不带来相关健康风险的情况下实现肿瘤定位与特征描述。此外，我们对合成的DCE-MRI图像进行了定性与定量评估，提出了一种多指标标度聚合度量（SAMe），评估了其在肿瘤分割下游任务中的效用，并最终分析了多序列DCE-MRI生成中的时间模式。我们的方法在生成真实且有用的DCE-MRI序列方面展现出有前景的结果，凸显了虚拟对比增强在改善乳腺癌诊断与治疗方面的潜力，尤其适用于禁用对比剂的患者。