글로벌 연구동향
의학물리학
- 2020년 04월호
[IEEE Trans Med Imaging.] k -Space Deep Learning for Accelerated MRI.KAIST / 한요서, 예종철*
- 출처
- IEEE Trans Med Imaging.
- 등재일
- 2020 Feb
- 저널이슈번호
- 39(2):377-386. doi: 10.1109/TMI.2019.2927101. Epub 2019 Jul 5.
- 내용
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.Author information
Han Y, Sunwoo L, Ye JC.
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