Sen Jia
Chinese Academy of Sciences
49 Papers
33 Citations
Sen Jia is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Computer science & Iterative reconstruction. The author has an hindex of 6, co-authored 32 publications. Previous affiliations of Sen Jia include Sun Yat-sen University.
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Papers
DIMENSION: Dynamic MR imaging with both k‐space and spatial prior knowledge obtained via multi‐supervised network training
TL;DR: Comparisons with classical k‐t FOCUSS, k‐ t SLR, L+S and the state‐of‐the‐art CNN‐based method on in vivo datasets show the proposed DIMENSION method can achieve improved reconstruction results in shorter time.
•Posted Content
Deep Low-rank plus Sparse Network for Dynamic MR Imaging
Wenqi Huang,Ziwen Ke,Zhuo-Xu Cui,Jing Cheng,Zhilang Qiu,Sen Jia,Leslie Ying,Yanjie Zhu,Dong Liang +8 more
TL;DR: A model-based low-rank plus sparse network, dubbed L+S-Net, is proposed for dynamic MR reconstruction that outperforms state-of-the-art CS and existing deep learning methods and has great potential for extremely high acceleration factors.
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•Posted Content
DIMENSION: Dynamic MR Imaging with Both K-space and Spatial Prior Knowledge Obtained via Multi-Supervised Network Training
TL;DR: In this paper, the authors proposed a multi-supervised network training technique to constrain the frequency domain information and reconstruction results at different levels, which can achieve improved reconstruction results in shorter time.
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Computer-aided diagnosis of early knee osteoarthritis based on MRI T2 mapping.
TL;DR: The knee OA classifier constituted by a weights-directly-determined RBF neural network didn't require any iteration, and demonstrated that the optimal weights, appropriate center and variance could be yielded through simple procedures.
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Analysis of generalized rosette trajectory for compressed sensing MRI.
TL;DR: Compared with spiral trajectories, the arch and curvature characteristics of the generalized rosette trajectories are more feasible to meet the physical requirements of undersampled k-space data acquisition in terms of time shortness and scan area.
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