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 • 7 Jan 2024 • Kasra Borazjani, Naji Khosravan, Leslie Ying, Seyyedali Hosseinalipour
Given the frequent presence of diverse data modalities within patient records, leveraging FL in a multi-modal learning framework holds considerable promise for cancer staging.
no code implementations • 7 May 2022 • Peizhou Huang, Chaoyi Zhang, Xiaoliang Zhang, Xiaojuan Li, Liang Dong, Leslie Ying
Experiment results demonstrate that the proposed method requires a reduced amount of training data to achieve high reconstruction quality among the state-of-art of MR reconstruction utilizing the Noise2Noise method.
no code implementations • 16 Oct 2021 • Abhijit Baul, Nian Wang, Choyi Zhang, Leslie Ying, Yuchou Chang, Ukash Nakarmi
Diffusion Magnetic Resonance Imaging (dMRI) is a promising method to analyze the subtle changes in the tissue structure.
1 code implementation • 9 Mar 2021 • Ziwen Ke, Zhuo-Xu Cui, Wenqi Huang, Jing Cheng, Sen Jia, Haifeng Wang, Xin Liu, Hairong Zheng, Leslie Ying, Yanjie Zhu, Dong Liang
The nonlinear manifold is designed to characterize the temporal correlation of dynamic signals.
1 code implementation • 26 Oct 2020 • Wenqi Huang, Ziwen Ke, Zhuo-Xu Cui, Jing Cheng, Zhilang Qiu, Sen Jia, Leslie Ying, Yanjie Zhu, Dong Liang
However, the selection of the parameters of L+S is empirical, and the acceleration rate is limited, which are common failings of iterative compressed sensing MR imaging (CS-MRI) reconstruction methods.
no code implementations • 22 Jun 2020 • Ziwen Ke, Wenqi Huang, Jing Cheng, Zhuoxu Cui, Sen Jia, Haifeng Wang, Xin Liu, Hairong Zheng, Leslie Ying, Yanjie Zhu, Dong Liang
The deep learning methods have achieved attractive performance in dynamic MR cine imaging.
no code implementations • 27 Feb 2020 • Gaurav N. Shetty, Konstantinos Slavakis, Ukash Nakarmi, Gesualdo Scutari, Leslie Ying
This paper establishes a kernel-based framework for reconstructing data on manifolds, tailored to fit the dynamic-(d)MRI-data recovery problem.
no code implementations • 3 Feb 2020 • Hongyu Li, Zifei Liang, Chaoyi Zhang, Ruiying Liu, Jing Li, Weihong Zhang, Dong Liang, Bowen Shen, Xiaoliang Zhang, Yulin Ge, Jiangyang Zhang, Leslie Ying
We also demonstrate that the trained neural network is robust to noise and motion in the testing data, and the network trained using healthy volunteer data can be directly applied to stroke patient data without compromising the lesion detectability.
1 code implementation • 20 Dec 2019 • Ziwen Ke, Jing Cheng, Leslie Ying, Hairong Zheng, Yanjie Zhu, Dong Liang
Although these deep learning methods can improve the reconstruction quality compared with iterative methods without requiring complex parameter selection or lengthy reconstruction time, the following issues still need to be addressed: 1) all these methods are based on big data and require a large amount of fully sampled MRI data, which is always difficult to obtain for cardiac MRI; 2) the effect of coil correlation on reconstruction in deep learning methods for dynamic MR imaging has never been studied.
no code implementations • 7 Aug 2019 • Jing Cheng, Haifeng Wang, Leslie Ying, Dong Liang
Experi-ments on in vivo MR data demonstrate that the proposed method achieves supe-rior MR reconstructions from highly undersampled k-space data over other state-of-the-art image reconstruction methods.
no code implementations • 26 Jul 2019 • Dong Liang, Jing Cheng, Ziwen Ke, Leslie Ying
Image reconstruction from undersampled k-space data has been playing an important role for fast MRI.
no code implementations • 19 Jun 2019 • Jing Cheng, Haifeng Wang, Yanjie Zhu, Qiegen Liu, Qiyang Zhang, Ting Su, Jianwei Chen, Yongshuai Ge, Zhanli Hu, Xin Liu, Hairong Zheng, Leslie Ying, Dong Liang
Usually, acquiring less data is a direct but important strategy to address these issues.
1 code implementation • 11 Jun 2019 • Shan-Shan Wang, Huitao Cheng, Leslie Ying, Taohui Xiao, Ziwen Ke, Xin Liu, Hairong Zheng, Dong Liang
This paper proposes a multi-channel image reconstruction method, named DeepcomplexMRI, to accelerate parallel MR imaging with residual complex convolutional neural network.
no code implementations • 18 Jan 2019 • Ziwen Ke, Shan-Shan Wang, Huitao Cheng, Leslie Ying, Qiegen Liu, Hairong Zheng, Dong Liang
In this work, we propose cascaded residual dense networks for dynamic MR imaging with edge-enhance loss constraint, dubbed as CRDN.
no code implementations • 27 Dec 2018 • Gaurav N. Shetty, Konstantinos Slavakis, Abhishek Bose, Ukash Nakarmi, Gesualdo Scutari, Leslie Ying
This paper puts forth a novel bi-linear modeling framework for data recovery via manifold-learning and sparse-approximation arguments and considers its application to dynamic magnetic-resonance imaging (dMRI).