k-Space Deep Learning for Accelerated MRI
- Resource Type
- Working Paper
- Authors
- Han, Yoseob; Sunwoo, Leonard; Ye, Jong Chul
- Source
- Subject
- Computer Science - Computer Vision and Pattern Recognition
Computer Science - Machine Learning
Statistics - Machine Learning
- Language
The annihilating filter-based low-rank Hankel matrix approach (ALOHA) is one of the state-of-the-art compressed sensing approaches that directly interpolates the missing k-space data using low-rank Hankel matrix completion. The success of ALOHA is due to the concise signal representation in the k-space domain thanks to the duality between structured low-rankness in the k-space domain and the image domain sparsity. Inspired by the recent mathematical discovery that links convolutional neural networks to Hankel matrix decomposition using data-driven framelet basis, here we propose a fully data-driven deep learning algorithm for k-space interpolation. Our network can be also easily applied to non-Cartesian k-space trajectories by simply adding an additional regridding layer. Extensive numerical experiments show that the proposed deep learning method consistently outperforms the existing image-domain deep learning approaches.
Comment: Accepted to IEEE Transactions on Medical Imaging