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 bot 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，需要首先静脉注射含碘对比剂，然后采集低能图像（与标准乳腺摄影相当）和高能图像。两幅图像融合后生成显示对比增强的叠加图像。尽管CESM在乳腺癌诊断方面具有优势，但使用对比剂可能引起副作用，且与标准乳腺摄影相比，患者接受更高辐射剂量。为解决这些局限，本研究提出将深度生成模型用于CESM的虚拟对比度增强，旨在实现无对比剂CESM并降低辐射剂量。我们的深度网络由自编码器、Pix2Pix和CycleGAN两种生成对抗网络组成，仅从低能图像生成合成叠加图像。我们对模型性能进行了广泛的定量与定性分析，并利用放射科医师的评估意见，基于一个包含1138张图像的新型CESM数据集（作为本研究的另一贡献，该数据集已公开提供）。结果表明，CycleGAN是生成合成叠加图像最有前景的深度网络，凸显了人工智能技术在该领域实现虚拟对比度增强的潜力。