WKGM: Weight-K-space Generative Model for Parallel Imaging Reconstruction
This work addresses robustness and flexibility limitations in deep learning-based parallel imaging for MRI, though it appears incremental as it builds on existing generative models.
The authors tackled the problem of robust and flexible calibration-less parallel imaging reconstruction in MRI by proposing a weight-k-space generative model (WKGM), which achieved state-of-the-art reconstruction results across varying sampling patterns and acceleration factors, trained on only 500 images.
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. In addition, WKGM is flexible and thus can be synergistically combined with various traditional k-space PI models, which can make full use of the correlation between multi-coil data and realizecalibration-less PI. Even though our model was trained on only 500 images, experimental results with varying sampling patterns and acceleration factors demonstrate that WKGM can attain state-of-the-art reconstruction results with the well-learned k-space generative prior.