k-t NEXT is a deep neural network that reconstructs dynamic MR images by iteratively alternating between x-f and image domains to exploit spatio-temporal redundancies.
k-Space Deep Learning for Accelerated MRI
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abstract
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 Hankel matrix 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 learning method consistently outperforms the existing image-domain deep learning approaches.
fields
eess.IV 2years
2019 2verdicts
UNVERDICTED 2representative citing papers
VS-Net unrolls variable splitting optimization into an end-to-end trainable network for accelerated parallel MRI reconstruction and reports improved accuracy and perceptual quality over prior deep learning methods on knee images at 4x and 6x acceleration.
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k-t NEXT: Dynamic MR Image Reconstruction Exploiting Spatio-temporal Correlations
k-t NEXT is a deep neural network that reconstructs dynamic MR images by iteratively alternating between x-f and image domains to exploit spatio-temporal redundancies.
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VS-Net: Variable splitting network for accelerated parallel MRI reconstruction
VS-Net unrolls variable splitting optimization into an end-to-end trainable network for accelerated parallel MRI reconstruction and reports improved accuracy and perceptual quality over prior deep learning methods on knee images at 4x and 6x acceleration.