期刊
PHARMACEUTICS
卷 14, 期 11, 页码 -出版社
MDPI
DOI: 10.3390/pharmaceutics14112378
关键词
artificial intelligence; synthetic imaging; virtual contrast; augmented contrast; MRI; CT; gadolinium-based contrast agents; iodinated contrast agents; neuroimaging; cardiac imaging
The development of 'virtual' and 'augmented' contrasts in biomedical imaging using artificial intelligence techniques has enabled the generation of synthetic post-contrast images through computational modeling, reducing the risks and limitations associated with traditional contrast media in clinical practice.
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.
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