Contrast Enhanced Spectral Mammography (CESM) is a dual-energy mammographic imaging technique that first needs intravenously administration of an iodinated contrast medium; then, it collects both a low-energy image, comparable to standard mammography, and a high-energy image. The two scans are then combined to get a recombined image showing contrast enhancement. Despite CESM diagnostic advantages for breast cancer diagnosis, the use of contrast medium can cause side effects, and CESM also beams patients with a higher radiation dose compared to standard mammography. To address these limitations this work proposes to use deep generative models for virtual contrast enhancement on CESM, aiming to make the CESM contrast-free as well as to reduce the radiation dose. Our deep networks, consisting of an autoencoder and two Generative Adversarial Networks, the Pix2Pix, and the CycleGAN, generate synthetic recombined images solely from low-energy images. We perform an extensive quantitative and qualitative analysis of the model's performance, also exploiting radiologists' assessments, on a novel CESM dataset that includes 1138 images that, as a further contribution of this work, we make publicly available. The results show that CycleGAN is the most promising deep network to generate synthetic recombined images, highlighting the potential of artificial intelligence techniques for virtual contrast enhancement in this field.

翻译：对比增强能谱乳腺摄影（CESM）是一种双能乳腺成像技术，其过程首先需静脉注射含碘对比剂，随后分别采集低能图像（与标准乳腺摄影相当）和高能图像。将两次扫描结果进行融合，可生成显示对比增强效果的融合图像。尽管CESM在乳腺癌诊断中具有诊断优势，但使用对比剂可能引发副作用，且相较于标准乳腺摄影，患者接受的辐射剂量更高。为解决这些局限性，本研究提出利用深度生成模型实现CESM的虚拟对比增强，旨在消除对对比剂的依赖并降低辐射剂量。我们构建的深度网络由自编码器、Pix2Pix与CycleGAN两种生成对抗网络组成，可仅基于低能图像生成合成融合图像。通过包含1138张图像的CESM新数据集（作为本研究的又一贡献，该数据集已公开发布），我们对模型性能进行了全面的定量与定性分析，并引入放射科医师评估。结果表明，CycleGAN是生成合成融合图像最具潜力的深度网络，凸显了人工智能技术在该领域实现虚拟对比增强的可行性。