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

• URL: http://arxiv.org/abs/2205.03883v1
• Date: Sun, 8 May 2022 14:28:20 GMT
• Title: WKGM: Weight-K-space Generative Model for Parallel Imaging Reconstruction
• Authors: Zongjiang Tu, Die Liu, Xiaoqing Wang, Chen Jiang, Minghui Zhang, Qiegen Liu, Dong Liang
• Abstract summary: We propose to explore the k-space domain via robust generative modeling for flexible PI reconstruction, coined weight-k-space generative model (WKGM) WKGM is a generalized k-space domain model, where the k-space weighting technology and high-dimensional space strategy are efficiently incorporated for score-based generative model training, resulting in good and robust reconstruction. Experimental results on datasets with varying sampling patterns and acceleration factors demonstrate that WKGM can attain state-of-the-art reconstruction results.
• Score: 15.555999296521476
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Parallel Imaging (PI) is one of the most im-portant and successful developments in accelerating magnetic resonance imaging (MRI). Recently deep learning PI has emerged as an effective technique to accelerate MRI. Nevertheless, most approaches have so far been based image domain. In this work, we propose to explore the k-space domain via robust generative modeling for flexible 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 strategy are efficiently incorporated for score-based generative model training, resulting in good and robust reconstruction. In addition, WKGM is flexible and thus can synergistically combine various traditional k-space PI models, generating learning-based priors to produce high-fidelity reconstructions. Experimental results on datasets with varying sampling patterns and acceleration factors demonstrate that WKGM can attain state-of-the-art reconstruction results under the well-learned k-space generative prior.

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