Abstract：
Contrast media are widely diffused in biomedical imaging, due to their relevance in the diagnosis of numerous disorders. However, the risk of adverse reactions, the concern of potential damage to sensitive organs, and the recently described brain deposition of gadolinium salts, limit the use of contrast media in clinical practice. In recent years, the application of artificial intelligence (AI) techniques to biomedical imaging has led to the development of \'virtual\' and \'augmented\' contrasts. The idea behind these applications is to generate synthetic post-contrast images through AI computational modeling starting from the information available on other images acquired during the same scan. In these AI models, non-contrast images (virtual contrast) or low-dose post-contrast images (augmented contrast) are used as input data to generate synthetic post-contrast images, which are often undistinguishable from the native ones. In this review, we discuss the most recent advances of AI applications to biomedical imaging relative to synthetic contrast media.
摘要：
造影剂在生物医学成像中广泛扩散，由于它们在许多疾病诊断中的相关性。然而,不良反应的风险，对敏感器官的潜在损害的关注，以及最近描述的钆盐的大脑沉积，在临床实践中限制造影剂的使用。近年来,人工智能（AI）技术在生物医学成像中的应用导致了“虚拟”和“增强”对比的发展。这些应用背后的想法是通过AI计算建模，从同一扫描期间获取的其他图像的可用信息开始，生成合成对比后图像。在这些AI模型中，非对比图像（虚拟对比）或低剂量对比后图像（增强对比）用作输入数据以生成合成对比后图像，通常与本地的无法区分。在这次审查中,我们讨论了人工智能应用于生物医学成像相对于合成造影剂的最新进展。
