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.

Synthetic Post-Contrast Imaging through Artificial Intelligence: Clinical Applications of Virtual and Augmented Contrast Media / Pasquini, Luca; Napolitano, Antonio; Pignatelli, Matteo; Tagliente, Emanuela; Parrillo, Chiara; Nasta, Francesco; Romano, Andrea; Bozzao, Alessandro; DI NAPOLI, Alberto. - In: PHARMACEUTICS. - ISSN 1999-4923. - 14:11(2022), p. 2378. [10.3390/pharmaceutics14112378]

Synthetic Post-Contrast Imaging through Artificial Intelligence: Clinical Applications of Virtual and Augmented Contrast Media

Luca Pasquini;Antonio Napolitano;Matteo Pignatelli;Chiara Parrillo;Andrea Romano;Alessandro Bozzao;Alberto Di Napoli
2022

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.
2022
CT; MRI; artificial intelligence; augmented contrast; cardiac imaging; gadolinium-based contrast agents; iodinated contrast agents; neuroimaging; synthetic imaging; virtual contrast
01 Pubblicazione su rivista::01d Recensione
Synthetic Post-Contrast Imaging through Artificial Intelligence: Clinical Applications of Virtual and Augmented Contrast Media / Pasquini, Luca; Napolitano, Antonio; Pignatelli, Matteo; Tagliente, Emanuela; Parrillo, Chiara; Nasta, Francesco; Romano, Andrea; Bozzao, Alessandro; DI NAPOLI, Alberto. - In: PHARMACEUTICS. - ISSN 1999-4923. - 14:11(2022), p. 2378. [10.3390/pharmaceutics14112378]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1664723
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