Keywords: Machine Learning/Artificial Intelligence, Machine Learning/Artificial IntelligenceCurrently, MR-based ventilation imaging relying on radial 3D-stack-of-stars spoiled gradient echo sequence requires a fairly long acquisition time of 8 minutes, which may impact clinical translation. Therefore, a shorter acquisition time is desired. In this study, a novel deep learning approach called transformer was evaluated for image restauration of radial undersampled lung images from 16 post-COVID-19 patients. For each patient, images resulting from 4- and 8 minutes acquisitions were provided. A transformer was trained to translate the 4-minute-version to the corresponding 8-minute-version and led to a significant image quality improvement, demonstrated by three complementary image similarity metrics.
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