Global k-Space Interpolation for Dynamic MRI Reconstruction using Masked Image Modeling

• URL: http://arxiv.org/abs/2307.12672v2
• Date: Wed, 18 Oct 2023 16:05:32 GMT
• Title: Global k-Space Interpolation for Dynamic MRI Reconstruction using Masked Image Modeling
• Authors: Jiazhen Pan, Suprosanna Shit, \"Ozg\"un Turgut, Wenqi Huang, Hongwei Bran Li, Nil Stolt-Ans\'o, Thomas K\"ustner, Kerstin Hammernik, Daniel Rueckert
• Abstract summary: In dynamic Magnetic Imaging (MRI), k-space is typically undersampled due to limited scan time. We propose a novel Transformer-based k-space Global Interpolation Network, termed k-GIN. Our k-GIN learns global dependencies among low- and high-frequency components of 2D+t k-space and uses it to interpolate unsampled data.
• Score: 10.74920257710449
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In dynamic Magnetic Resonance Imaging (MRI), k-space is typically undersampled due to limited scan time, resulting in aliasing artifacts in the image domain. Hence, dynamic MR reconstruction requires not only modeling spatial frequency components in the x and y directions of k-space but also considering temporal redundancy. Most previous works rely on image-domain regularizers (priors) to conduct MR reconstruction. In contrast, we focus on interpolating the undersampled k-space before obtaining images with Fourier transform. In this work, we connect masked image modeling with k-space interpolation and propose a novel Transformer-based k-space Global Interpolation Network, termed k-GIN. Our k-GIN learns global dependencies among low- and high-frequency components of 2D+t k-space and uses it to interpolate unsampled data. Further, we propose a novel k-space Iterative Refinement Module (k-IRM) to enhance the high-frequency components learning. We evaluate our approach on 92 in-house 2D+t cardiac MR subjects and compare it to MR reconstruction methods with image-domain regularizers. Experiments show that our proposed k-space interpolation method quantitatively and qualitatively outperforms baseline methods. Importantly, the proposed approach achieves substantially higher robustness and generalizability in cases of highly-undersampled MR data. For video presentation, poster, GIF results and code please check our project page: https://jzpeterpan.github.io/k-gin.github.io/.

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