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Abstract #4919

Transformer-based image quality improvement of radial undersampled lung MRI data from post-COVID-19 patients

Maximilian Zubke1,2, Robin A Müller1,2, Marius Wernz1,2, Filip Klimeš1,2, Frank Wacker1,2, and Jens Vogel-Claussen1,2
1Institute of Diagnostic and Interventional Radiology, Hannover Medical School, Hannover, Germany, 2Biomedical Research in Endstage and Obstructive Lung Disease Hannover (BREATH), German Center for Lung Research (DZL), Hannover, Germany

Synopsis

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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Keywords