Zobrazeno 1 - 10
of 2 299
pro vyhledávání: '"Zhuo XU"'
Autor:
Wang, Yue, Zhou, Tian, Cui, Zhuo-xu, Huang, Bingsheng, Zheng, Hairong, Liang, Dong, Zhu, Yanjie
Magnetic Resonance Imaging (MRI) is a multi-contrast imaging technique in which different contrast images share similar structural information. However, conventional diffusion models struggle to effectively leverage this structural similarity. Recent
Externí odkaz:
http://arxiv.org/abs/2411.14269
Dynamic MR images possess various transformation symmetries,including the rotation symmetry of local features within the image and along the temporal dimension. Utilizing these symmetries as prior knowledge can facilitate dynamic MR imaging with high
Externí odkaz:
http://arxiv.org/abs/2409.08537
Autor:
Liu, Yuanyuan, Xie, Jinwen, Cui, Zhuo-Xu, Zhu, Qingyong, Cheng, Jing, Liang, Dong, Zhu, Yanjie
Quantitative T1rho mapping has shown promise in clinical and research studies. However, it suffers from long scan times. Deep learning-based techniques have been successfully applied in accelerated quantitative MR parameter mapping. However, most met
Externí odkaz:
http://arxiv.org/abs/2407.05617
Autor:
Xie, Taofeng, Cui, Zhuo-Xu, Luo, Chen, Wang, Huayu, Liu, Congcong, Zhang, Yuanzhi, Wang, Xuemei, Zhu, Yanjie, Chen, Guoqing, Liang, Dong, Jin, Qiyu, Zhou, Yihang, Wang, Haifeng
Positron Emission Tomography and Magnetic Resonance Imaging (PET-MRI) systems can obtain functional and anatomical scans. PET suffers from a low signal-to-noise ratio. Meanwhile, the k-space data acquisition process in MRI is time-consuming. The stud
Externí odkaz:
http://arxiv.org/abs/2311.14473
Autor:
Wang, Wenxin, Cui, Zhuo-Xu, Cheng, Guanxun, Cao, Chentao, Xu, Xi, Liu, Ziwei, Wang, Haifeng, Qi, Yulong, Liang, Dong, Zhu, Yanjie
Accurate detection and segmentation of brain tumors is critical for medical diagnosis. However, current supervised learning methods require extensively annotated images and the state-of-the-art generative models used in unsupervised methods often hav
Externí odkaz:
http://arxiv.org/abs/2311.03074
Autor:
Chaorui Qiu, Zhiqiang Zhang, Zhiqiang Xu, Liao Qiao, Li Ning, Shujun Zhang, Min Su, Weichang Wu, Kexin Song, Zhuo Xu, Long-Qing Chen, Hairong Zheng, Chengbo Liu, Weibao Qiu, Fei Li
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-14 (2024)
Abstract Photoacoustic imaging is a promising non-invasive functional imaging modality for fundamental research and clinical diagnosis. However, achieving capillary-level resolution, wide field-of-view, and high frame rates remains challenging. To ad
Externí odkaz:
https://doaj.org/article/a2f3ad046def4246af2cb83554518bcc
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-17 (2024)
Abstract This paper presents a modal analysis conducted under seabed dynamic pressure conditions. The polyethylene (PE) pipeline was approximated as a thin-walled long cylindrical shell. The impact of pressure on the pipeline structure was considered
Externí odkaz:
https://doaj.org/article/8207e183020240f8beae9c078bcdcd12
Autor:
Liu, Yuanyuan, Cui, Zhuo-Xu, Qin, Shucong, Liu, Congcong, Zheng, Hairong, Wang, Haifeng, Zhou, Yihang, Liang, Dong, Zhu, Yanjie
Long scan time significantly hinders the widespread applications of three-dimensional multi-contrast cardiac magnetic resonance (3D-MC-CMR) imaging. This study aims to accelerate 3D-MC-CMR acquisition by a novel method based on score-based diffusion
Externí odkaz:
http://arxiv.org/abs/2310.04669
Recently, regularization model-driven deep learning (DL) has gained significant attention due to its ability to leverage the potent representational capabilities of DL while retaining the theoretical guarantees of regularization models. However, most
Externí odkaz:
http://arxiv.org/abs/2309.13571
Recently, data-driven techniques have demonstrated remarkable effectiveness in addressing challenges related to MR imaging inverse problems. However, these methods still exhibit certain limitations in terms of interpretability and robustness. In resp
Externí odkaz:
http://arxiv.org/abs/2309.09250