25.04.2022The Menze group develops a convolutional neural network for residual motion correction in fast whole-brain MRI

In routine clinical quantitative magnetic resonance imaging (MRI), motion artifacts affect parameter estimation and thus data quality. In this work, the Menze group presents a multiscale 3D convolutional neural network (CNN) that learns the nonlinear relationship between motion-influenced quantitative parameter maps and the residual error to their motion-free reference. A physically informed simulation is proposed for supervised model training, which generates independent paired data sets from a priori motion-free data. The proposed motion correction CNN outperforms the current state-of-the-art and reliably provides high, clinically relevant image quality for mild to pronounced patient motion.

See Pirkl et al., Med. Image Anal.