Deep Learning Enables Superior Photoacoustic Imaging at Ultralow Laser Dosages von Zhao, Huangxuan, Ke, Ziwen, Yang, Fan, Li, Ke, Chen, Ningbo, Song, Liang, Zheng, Chuansheng, Liang, Dong, Liu, Chengbo

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DeepcomplexMRI: Exploiting deep residual network for fast parallel MR imaging with complex convolution von Wang, Shanshan, Cheng, Huitao, Ying, Leslie, Xiao, Taohui, Ke, Ziwen, Zheng, Hairong, Liang, Dong

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DIMENSION: Dynamic MR imaging with both k‐space and spatial prior knowledge obtained via multi‐supervised network training von Wang, Shanshan, Ke, Ziwen, Cheng, Huitao, Jia, Sen, Ying, Leslie, Zheng, Hairong, Liang, Dong

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Deep low-Rank plus sparse network for dynamic MR imaging von Huang, Wenqi, Ke, Ziwen, Cui, Zhuo-Xu, Cheng, Jing, Qiu, Zhilang, Jia, Sen, Ying, Leslie, Zhu, Yanjie, Liang, Dong

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An unsupervised deep learning method for multi-coil cine MRI von Ke, Ziwen, Cheng, Jing, Ying, Leslie, Zheng, Hairong, Zhu, Yanjie, Liang, Dong

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Universal generative modeling in dual domains for dynamic MRI von Yu, Chuanming, Guan, Yu, Ke, Ziwen, Lei, Ke, Liang, Dong, Liu, Qiegen

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Learned Low-Rank Priors in Dynamic MR Imaging von Ke, Ziwen, Huang, Wenqi, Cui, Zhuo-Xu, Cheng, Jing, Jia, Sen, Wang, Haifeng, Liu, Xin, Zheng, Hairong, Ying, Leslie, Zhu, Yanjie, Liang, Dong

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Learning Data Consistency and its Application to Dynamic MR Imaging von Cheng, Jing, Cui, Zhuo-Xu, Huang, Wenqi, Ke, Ziwen, Ying, Leslie, Wang, Haifeng, Zhu, Yanjie, Liang, Dong

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A new deep learning method for image deblurring in optical microscopic systems von Zhao, Huangxuan, Ke, Ziwen, Chen, Ningbo, Wang, Songjian, Li, Ke, Wang, Lidai, Gong, Xiaojing, Zheng, Wei, Song, Liang, Liu, Zhicheng, Liang, Dong, Liu, Chengbo

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B 1 mapping using pre-learned subspaces for quantitative brain imaging von Zhang, Tianxiao, Zhao, Yibo, Jin, Wen, Li, Yudu, Guo, Rong, Ke, Ziwen, Luo, Jie, Li, Yao, Liang, Zhi-Pei

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Equilibrated Zeroth-Order Unrolled Deep Network for Parallel MR Imaging von Cui, Zhuo-Xu, Jia, Sen, Cheng, Jing, Zhu, Qingyong, Liu, Yuanyuan, Zhao, Kankan, Ke, Ziwen, Huang, Wenqi, Wang, Haifeng, Zhu, Yanjie, Ying, Leslie, Liang, Dong

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Cascaded Residual Dense Networks for Dynamic MR Imaging with Edge-Enhanced Loss Constraint von Ke, Ziwen, Zhu, Yanjie, Liang, Dong

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B1 mapping using pre‐learned subspaces for quantitative brain imaging von Zhang, Tianxiao, Zhao, Yibo, Jin, Wen, Li, Yudu, Guo, Rong, Ke, Ziwen, Luo, Jie, Li, Yao, Liang, Zhi‐Pei

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Deep Manifold Learning for Dynamic MR Imaging von Ke, Ziwen, Cui, Zhuo-Xu, Huang, Wenqi, Cheng, Jing, Jia, Sen, Ying, Leslie, Zhu, Yanjie, Liang, Dong

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Zero-shot Dynamic MRI Reconstruction with Global-to-local Diffusion Model von Guan, Yu, Zhang, Kunlong, Qi, Qi, Wang, Dong, Ke, Ziwen, Wang, Shaoyu, Liang, Dong, Liu, Qiegen

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Universal Generative Modeling in Dual-domain for Dynamic MR Imaging von Yu, Chuanming, Guan, Yu, Ke, Ziwen, Liang, Dong, Liu, Qiegen

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Deep MRI Reconstruction: Unrolled Optimization Algorithms Meet Neural Networks von Liang, Dong, Cheng, Jing, Ke, Ziwen, Ying, Leslie

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SRR-Net: A Super-Resolution-Involved Reconstruction Method for High Resolution MR Imaging von Huang, Wenqi, Jia, Sen, Ke, Ziwen, Cui, Zhuo-Xu, Cheng, Jing, Zhu, Yanjie, Liang, Dong

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Zero-shot Dynamic MRI Reconstruction with Global-to-local Diffusion Model von Guan, Yu, Zhang, Kunlong, Qi, Qi, Wang, Dong, Ke, Ziwen, Wang, Shaoyu, Liang, Dong, Liu, Qiegen

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Universal Generative Modeling in Dual-domain for Dynamic MR Imaging von Yu, Chuanming, Guan, Yu, Ke, Ziwen, Liang, Dong, Liu, Qiegen

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