Autofocusing+: Noise-Resilient Motion Correction in Magnetic Resonance Imaging

Ekaterina Kuzmina, Artem Razumov, Oleg Y. Rogov, Elfar Adalsteinsson, Jacob White, Dmitry V. Dylov

Abstract: Image corruption by motion artifacts is an ingrained problem in Magnetic Resonance Imaging (MRI). In this work, we propose a neural network-based regularization term to enhance Autofocusing, a classic optimization-based method to remove rigid motion artifacts. The method takes the best of both worlds: the optimization-based routine iteratively executes the blind demotion and deep learning-based prior penalizes for unrealistic restorations and speeds up the convergence. We validate the method on three models of motion trajectories, using synthetic and real noisy data. The method proves resilient to noise and anatomic structure variation, outperforming the state-of-the-art motion correction methods.