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k-Space Deep Learning for Accelerated MRI
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DOI：10.1109/TMI.2019.2927101.png)
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摘要
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The annihilating filter-based low-rank Hankel matrix approach (ALOHA) is one of the state-of-the-art compressed sensing approaches that directly interpolates the missing k-space data using low-rank Hankelmatrix completion. The success of ALOHA is due to the concise signal representation in the k-space domain, thanks to the duality between structured low-rankness in the k-space domain and the image domain sparsity. Inspired by the recent mathematical discovery that links convolutional neural networks to Hankel matrix decomposition using data-driven framelet basis, here we propose a fully data-driven deep learning algorithm for k-space interpolation. Our network can be also easily applied to non-Cartesian k-space trajectories by simply adding an additional regridding layer. Extensive numerical experiments show that the proposed deep learningmethod consistently outperforms the existing image-domain deep learning approaches.
Keyword:
Compressed sensing MRI
deep learning
Hankel structured low-rank completion
convolution framelets
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