DeepGrasp4D: A General Framework for Highly-Accelerated Real-Time 4D Golden-Angle Radial MRI Using Deep Learning

Motivation: Time-resolved real-time 4D MRI demands high imaging speed to achieve high spatial and temporal resolution. While conventional iterative reconstruction methods can accomplish this, they require substantial temporal correlations and impose a significant computational burden. Goal(s): This study proposes DeepGrasp4D, a deep learning technique tailored to efficiently reconstruct real-time 4D MR images with reduced temporal correlations and shortened scan times. Approach: DeepGrasp4D was developed based on an unrolled network that incorporates an explicit low-rank constraint and a temporal total variation constraint, enabling efficient reconstruction of 4D images from continuously acquired golden-angle radial k-space. Results: DeepGrasp4D enables accurate 4D MRI reconstruction at high acceleration rates. Impact: The proposed DeepGrasp4D technique enables efficient and reliable 4D MRI reconstruction from golden-angle radial data acquired with shortened scan times and reduced temporal correlations. This can be useful in various applications such as DCE-MRI or MRI-guided radiotherapy.