Learning residual motion correction for fast and robust 3D multiparametric 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.