Towards motion-robust MRI – Autonomous motion timing and correction during MR scanning using multi-coil data and a deep-learning neural network

We propose a method for timing and correcting for rigid-body in-plane patient motion during an MRI scan. The motion is detected using differences between coil-intensity-corrected images from different coils in the receiver array together with the scan-order information. The method allows for the detection and timing of multiple movements during the scan. For each scan where motion was detected, k-space data are divided into different motion states, which are used as input to a deep neural network whose output is a motion-corrected image. The system shows promising results on MR data containing simulated and real motion.