WKGM: Weight-K-space Generative Model for Parallel Imaging Reconstruction

Deep learning based parallel Imaging (PI) has made great progresses in recent years to accelerate magnetic resonance imaging (MRI). Nevertheless, the performanc-es and robustness of existing methods can still be im-proved. In this work, we propose to explore the k-space domain learning via robust generative modeling for flexible PI reconstruction, coined weight-k-space genera-tive model (WKGM). Specifically, WKGM is a general-ized k-space domain model, where the k-space weighting technology and high-dimensional space augmentation design are efficiently incorporated for score-based gen-erative model training, resulting in good and robust re-constructions. In addition, WKGM is flexible and thus can be synergistically combined with various traditional k-space PI models, generating learning-based priors to produce high-fidelity reconstructions. Experimental re-sults on datasets with varying sampling patterns and ac-celeration factors demonstrate that WKGM can attain state-of-the-art reconstruction results with the well-learned k-space generative prior.