Validation of a deep-learning-based method for accelerating susceptibility-weighted imaging in clinical subjects

In this study, we validated a deep-learning-based method for accelerating susceptibility-weighted imaging (SWI) in 31 clinical subjects. Compared to the fully sampled images, the accelerated SWI images had less noise and imaging artifacts. Although the images had decreased sharpness, the anatomical details of the lesions were mostly kept, and we had not observed false negative lesions. This method could be useful for clinical situations that need timely imaging results.