Convolutional Neural Network based Stack-of-Star Imaging with Noise and Artifacts Removal

Stack of Star acquisition is one of the most frequently used non-Cartesian k-space sampling methods due to its fast speed and robustness to motion. To further reduce the scan time or increase the temporal resolution, Stack of Star is often down-sampled with fewer spokes and advanced sampling patterns, such as golden angle acquisition. However, this makes Stack of Star prone to noise and streak artifacts, limiting the in-plane resolution and degrading diagnostic quality. In this work, we evaluated a deep-learning based stack-of-star method for free-breathing abdominal imaging and it shows improved diagnostic quality.