Fast imaging techniques can speed up MRI acquisition but can also be corrupted by noise, reconstruction artifacts, and motion artifacts in a clinical setting. A deep learning-based method was developed to reduce imaging noise and artifacts. A network trained with the supervised approach improved the image quality for both simulated and in vivo data.
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