no code implementations • 4 Apr 2024 • Ruoyou Wu, Jian Cheng, Cheng Li, Juan Zou, Jing Yang, Wenxin Fan, Shanshan Wang
The first is the latent feature extractor, which primarily extracts latent space feature maps from LR dMRI and anatomical images while learning structural prior information from the anatomical images.
no code implementations • 5 Feb 2024 • Shanshan Wang, Ruoyou Wu, Sen Jia, Alou Diakite, Cheng Li, Qiegen Liu, Leslie Ying
The traits and trends of these techniques have also been given which have shifted from supervised learning to semi-supervised learning, and finally, to unsupervised learning methods.
no code implementations • 3 Jan 2024 • Wenxin Fan, Jian Cheng, Cheng Li, Xinrui Ma, Jing Yang, Juan Zou, Ruoyou Wu, Qiegen Liu, Shanshan Wang
Deep learning has shown great potential in accelerating diffusion tensor imaging (DTI).
no code implementations • 3 Jan 2024 • Jing Yang, Jian Cheng, Cheng Li, Wenxin Fan, Juan Zou, Ruoyou Wu, Shanshan Wang
Diffusion Magnetic Resonance Imaging (dMRI) plays a crucial role in the noninvasive investigation of tissue microstructural properties and structural connectivity in the \textit{in vivo} human brain.
no code implementations • 27 Aug 2023 • Ruoyou Wu, Cheng Li, Juan Zou, Shanshan Wang
Heterogeneous data captured by different scanning devices and imaging protocols can affect the generalization performance of the deep learning magnetic resonance (MR) reconstruction model.
no code implementations • 21 Jul 2023 • Ruoyou Wu, Cheng Li, Juan Zou, Shanshan Wang
Centralized training methods have shown promising results in MR image reconstruction, but privacy concerns arise when gathering data from multiple institutions.
no code implementations • 10 May 2023 • Juan Zou, Cheng Li, Ruoyou Wu, Tingrui Pei, Hairong Zheng, Shanshan Wang
SSFedMRI explores the physics-based contrastive reconstruction networks in each client to realize cross-site collaborative training in the absence of fully sampled data.
no code implementations • 15 Apr 2023 • Ruoyou Wu, Cheng Li, Juan Zou, Qiegen Liu, Hairong Zheng, Shanshan Wang
However, high heterogeneity exists in the data from different centers, and existing federated learning methods tend to use average aggregation methods to combine the client's information, which limits the performance and generalization capability of the trained models.
no code implementations • 8 Aug 2022 • Juan Zou, Cheng Li, Sen Jia, Ruoyou Wu, Tingrui Pei, Hairong Zheng, Shanshan Wang
Lately, deep learning has been extensively investigated for accelerating dynamic magnetic resonance (MR) imaging, with encouraging progresses achieved.
1 code implementation • 3 Feb 2022 • Shanshan Wang, Ruoyou Wu, Cheng Li, Juan Zou, Ziyao Zhang, Qiegen Liu, Yan Xi, Hairong Zheng
However, in the absence of high-quality, fully sampled datasets for training, the performance of these methods is limited.