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

Understand

Deep learning based parallel imaging (PI) has made great progresses in recent years to accelerate magnetic resonance imaging (MRI).

• Nevertheless, it still has some limitations, such as the robustness and flexibility of existing methods have great deficiency.
• In this work, we propose a method to explore the k-space domain learning via robust generative modeling for flexible calibration-less PI reconstruction, coined weight-k-space generative model (WKGM).
• Specifically, WKGM is a generalized k-space domain model, where the k-space weighting technology and high-dimensional space augmentation design are efficiently incorporated for score-based generative model training, resulting in good and robust reconstructions.