MRI-movienet: fast motion-resolved 4D MRI reconstruction exploiting space-time-coil correlations without k-space data consistency

Motion-resolved 4D MRI enables free-breathing imaging and access to important physiological information. However, long reconstruction times for 4D MRI techniques like XD-GRASP have restricted routine clinical use. Even with unrolled convolutional networks, reconstruction enforcing data consistency in a high-dimensional space is still long. This work presents a deep learning approach named MRI-movienet that exploits spatial-time-coil correlations without enforcing data consistency to enable 2-fold scan acceleration compared to XD-GRASP and 4D reconstruction in less than 2 seconds. MRI-movienet uses the intrinsic separation into static and dynamic components to avoid hallucinations. MRI-movienet high performance will promote 4D MRI for routine clinical use.