no code implementations • 31 Oct 2023 • Di Xu, Qihui Lyu, Dan Ruan, Ke Sheng
Deep learning (DL) methods have shown promise to improve the MMD performance, but typical approaches of conducing DL-MMD in the image domain fail to fully utilize projection information or under iterative setup are computationally inefficient in both training and prediction.
no code implementations • 19 Sep 2023 • Di Xu, Hengjie Liu, Dan Ruan, Ke Sheng
Dynamic magnetic resonance imaging (DMRI) is an effective imaging tool for diagnosis tasks that require motion tracking of a certain anatomy.
no code implementations • 19 Feb 2023 • Di Xu, Qifan Xu, Kevin Nhieu, Dan Ruan, Ke Sheng
Suppression of thoracic bone shadows on chest X-rays (CXRs) has been indicated to improve the diagnosis of pulmonary disease.
no code implementations • 20 Oct 2022 • Chen Qian, Yuncheng Gao, Mingyang Han, Zi Wang, Dan Ruan, Yu Shen, Yaping Wu, Yirong Zhou, Chengyan Wang, Boyu Jiang, Ran Tao, Zhigang Wu, Jiazheng Wang, Liuhong Zhu, Yi Guo, Taishan Kang, Jianzhong Lin, Tao Gong, Chen Yang, Guoqiang Fei, Meijin Lin, Di Guo, Jianjun Zhou, Meiyun Wang, Xiaobo Qu
In conclusion, PIDD presents a novel deep learning framework by exploiting the power of MRI physics, providing a cost-effective and explainable way to break the data bottleneck in deep learning medical imaging.
no code implementations • 15 Feb 2022 • Qifan Xu, Qihui Lyu, Dan Ruan, Ke Sheng
The Plug-and-Play (PnP) framework was recently introduced for low-dose CT reconstruction to leverage the interpretability and the flexibility of model-based methods to incorporate various plugins, such as trained deep learning (DL) neural networks.
no code implementations • 23 Dec 2019 • Yuhua Chen, Dan Ruan, Jiayu Xiao, Lixia Wang, Bin Sun, Rola Saouaf, Wensha Yang, Debiao Li, Zhaoyang Fan
The model takes in multi-slice MR images and generates the output of segmentation results.
no code implementations • 5 Jun 2019 • Ningning Zhao, Nuo Tong, Dan Ruan, Ke Sheng
Motivated by the superior performance reported by renowned region based CNN, in the second stage, another 3D U-Net is trained on the candidate region generated in the first stage.
1 code implementation • 22 Jul 2017 • Ningning Zhao, Daniel O'Connor, Adrian Basarab, Dan Ruan, Peng Hu, Ke Sheng
This paper proposes a novel framework to reconstruct the dynamic magnetic resonance images (DMRI) with motion compensation (MC).