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| Optimizing k-Space Subsampling Pattern toward MRI Reconstruction |
| XUAN Kai1, WANG Qian1 |
| 1. School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240 |
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Abstract Imaging velocity is a major factor affecting clinical applications of magnetic resonance(MR) imaging. And an effective solution of reducing scanning time is to under-sample in k-space and reconstruct the image from under-sampled MR signals. In this paper, the impact of under-sampling pattern on reconstruction quality is analyzed and a joint optimization strategy is proposed to update the under-sampling pattern with image reconstruction model in the context of deep-learning. To optimize the non-continuous under-sampling pattern, it is firstly initialized with full-sampling pattern. Then, relatively less important phase-encodings are gradually pruned until the sparsity requirement in k-space is satisfied. And the optimization of k-space under-sampling pattern is conducted alternatively with that of the reconstruction model. Moreover, the relative importance is estimated with the weight by assigning weight to each phase-coding. Experiments demonstrate that the proposed method improves the quality of the reconstructed MR image compared with the proposed method.
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Received: 04 August 2020
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| Fund:Science and Technology Project of Science and Technology Commission of Shanghai Municipality(No.19QC1400600) |
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Corresponding Authors:
WANG Qian, Ph.D., professor. His research interests include medical image processing.
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| About author:: XUAN Kai, Ph.D. candidate. His research interests include medical image processing. |
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2021, Vol. 34 
