Zobrazeno 1 - 10
of 8 107
pro vyhledávání: '"Qin CHEN"'
Magnetic Resonance Imaging (MRI) is a powerful, non-invasive diagnostic tool; however, its clinical applicability is constrained by prolonged acquisition times. Whilst present deep learning-based approaches have demonstrated potential in expediting M
Externí odkaz:
http://arxiv.org/abs/2409.14479
Magnetic Resonance Imaging (MRI) is a leading diagnostic modality for a wide range of exams, where multiple contrast images are often acquired for characterizing different tissues. However, acquiring high-resolution MRI typically extends scan time, w
Externí odkaz:
http://arxiv.org/abs/2408.03194
Multi-contrast image registration is a challenging task due to the complex intensity relationships between different imaging contrasts. Conventional image registration methods are typically based on iterative optimizations for each input image pair,
Externí odkaz:
http://arxiv.org/abs/2408.05341
Images and structured tables are essential parts of real-world databases. Though tabular-image representation learning is promising to create new insights, it remains a challenging task, as tabular data is typically heterogeneous and incomplete, pres
Externí odkaz:
http://arxiv.org/abs/2407.07582
Autor:
Wang, Zi, Wang, Fanwen, Qin, Chen, Lyu, Jun, Cheng, Ouyang, Wang, Shuo, Li, Yan, Yu, Mengyao, Zhang, Haoyu, Guo, Kunyuan, Shi, Zhang, Li, Qirong, Xu, Ziqiang, Zhang, Yajing, Li, Hao, Hua, Sha, Chen, Binghua, Sun, Longyu, Sun, Mengting, Li, Qin, Chu, Ying-Hua, Bai, Wenjia, Qin, Jing, Zhuang, Xiahai, Prieto, Claudia, Young, Alistair, Markl, Michael, Wang, He, Wu, Lianming, Yang, Guang, Qu, Xiaobo, Wang, Chengyan
Cardiac magnetic resonance imaging (MRI) has emerged as a clinically gold-standard technique for diagnosing cardiac diseases, thanks to its ability to provide diverse information with multiple modalities and anatomical views. Accelerated cardiac MRI
Externí odkaz:
http://arxiv.org/abs/2406.19043
Autor:
Le, Huynh Anh N., Qin, Chen, Xue, Yongquan, Zhu, Shifu, Nguyen, Kim Ngan N., Xia, Ruisong, Lin, Xiaozhi
We introduce our project, AGNSTRONG (Active Galactic Nuclei and STaR fOrmation in Nearby Galaxies). Our research goals encompass investigating the kinematic properties of ionized and molecular gas outflows, understanding the impact of AGN feedback, a
Externí odkaz:
http://arxiv.org/abs/2405.09478
Deformable image registration (DIR) is crucial in medical image analysis, enabling the exploration of biological dynamics such as organ motions and longitudinal changes in imaging. Leveraging Neural Ordinary Differential Equations (ODE) for registrat
Externí odkaz:
http://arxiv.org/abs/2404.02106
Autor:
Lyu, Jun, Qin, Chen, Wang, Shuo, Wang, Fanwen, Li, Yan, Wang, Zi, Guo, Kunyuan, Ouyang, Cheng, Tänzer, Michael, Liu, Meng, Sun, Longyu, Sun, Mengting, Li, Qin, Shi, Zhang, Hua, Sha, Li, Hao, Chen, Zhensen, Zhang, Zhenlin, Xin, Bingyu, Metaxas, Dimitris N., Yiasemis, George, Teuwen, Jonas, Zhang, Liping, Chen, Weitian, Zhao, Yidong, Tao, Qian, Pang, Yanwei, Liu, Xiaohan, Razumov, Artem, Dylov, Dmitry V., Dou, Quan, Yan, Kang, Xue, Yuyang, Du, Yuning, Dietlmeier, Julia, Garcia-Cabrera, Carles, Hemidi, Ziad Al-Haj, Vogt, Nora, Xu, Ziqiang, Zhang, Yajing, Chu, Ying-Hua, Chen, Weibo, Bai, Wenjia, Zhuang, Xiahai, Qin, Jing, Wu, Lianmin, Yang, Guang, Qu, Xiaobo, Wang, He, Wang, Chengyan
Cardiac MRI, crucial for evaluating heart structure and function, faces limitations like slow imaging and motion artifacts. Undersampling reconstruction, especially data-driven algorithms, has emerged as a promising solution to accelerate scans and e
Externí odkaz:
http://arxiv.org/abs/2404.01082
Reconstruction of magnetic resonance imaging (MRI) data has been positively affected by deep learning. A key challenge remains: to improve generalisation to distribution shifts between the training and testing data. Most approaches aim to address thi
Externí odkaz:
http://arxiv.org/abs/2402.08692
Artifacts are a common problem in physiological time series collected from intensive care units (ICU) and other settings. They affect the quality and reliability of clinical research and patient care. Manual annotation of artifacts is costly and time
Externí odkaz:
http://arxiv.org/abs/2312.05959