Zobrazeno 1 - 10
of 2 365
pro vyhledávání: '"Zheng, HaiRong"'
Objective: The aim of this study is to validate the effectiveness of an energy-modulated scatter correction method in suppressing scatter in photon-counting detector (PCD)-based cone beam CT (CBCT) imaging. Approach: The scatter correction method, na
Externí odkaz:
http://arxiv.org/abs/2412.12862
Autor:
Wang, Shanshan, Yu, Shoujun, Cheng, Jian, Jia, Sen, Tie, Changjun, Zhu, Jiayu, Peng, Haohao, Dong, Yijing, He, Jianzhong, Zhang, Fan, Xing, Yaowen, Jia, Xiuqin, Yang, Qi, Tian, Qiyuan, Guo, Hua, Li, Guobin, Zheng, Hairong
Diffusion magnetic resonance imaging (dMRI) provides critical insights into the microstructural and connectional organization of the human brain. However, the availability of high-field, open-access datasets that include raw k-space data for advanced
Externí odkaz:
http://arxiv.org/abs/2412.06666
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
Neurite Orientation Dispersion and Density Imaging (NODDI) microstructure estimation from diffusion magnetic resonance imaging (dMRI) is of great significance for the discovery and treatment of various neurological diseases. Current deep learning-bas
Externí odkaz:
http://arxiv.org/abs/2411.06444
Autor:
Zhou, Shuo, Zhou, Yihang, Liu, Congcong, Zhu, Yanjie, Zheng, Hairong, Liang, Dong, Wang, Haifeng
Magnetic resonance image reconstruction starting from undersampled k-space data requires the recovery of many potential nonlinear features, which is very difficult for algorithms to recover these features. In recent years, the development of quantum
Externí odkaz:
http://arxiv.org/abs/2410.09406
Autor:
Mo, Zhiguang, Che, Shao, Xiao, Enhua, Chen, Qiaoyan, Du, Feng, Li, Nan, Jia, Sen, Tie, Changjun, Wu, Bing, Zhang, Xiaoliang, Zheng, Hairong, Li, Ye
The performance of radiofrequency (RF) coils has a significant impact on the quality and speed of magnetic resonance imaging (MRI). Consequently, rigid coils with attached cables are commonly employed to achieve optimal SNR performance and parallel i
Externí odkaz:
http://arxiv.org/abs/2409.20095
Autor:
Zhang, Xin, Xie, Jixiong, Su, Ting, Zhu, Jiongtao, Cui, Han, Tan, Yuhang, Xia, Dongmei, Zheng, Hairong, Liang, Dong, Ge, Yongshuai
Background: Recently, the popularity of dual-layer flat-panel detector (DL-FPD) based dual-energy cone-beam CT (DE-CBCT) imaging has been increasing. However, the image quality of DE-CBCT remains constrained by the Compton scattered X-ray photons. Pu
Externí odkaz:
http://arxiv.org/abs/2408.04943
Autor:
Lin, Ling, Zhou, Yihang, Hu, Zhanqi, Jiang, Dian, Liu, Congcong, Zhou, Shuo, Zhu, Yanjie, Liao, Jianxiang, Liang, Dong, Zheng, Hairong, Wang, Haifeng
Tuberous sclerosis complex (TSC) manifests as a multisystem disorder with significant neurological implications. This study addresses the critical need for robust classification models tailored to TSC in pediatric patients, introducing QResNet,a nove
Externí odkaz:
http://arxiv.org/abs/2408.12615
Magnetic resonance diffusion tensor imaging (DTI) is a critical tool for neural disease diagnosis. However, long scan time greatly hinders the widespread clinical use of DTI. To accelerate image acquisition, a feature-enhanced joint diffusion model (
Externí odkaz:
http://arxiv.org/abs/2405.15830
Autor:
Fan, Wenxin, Cheng, Jian, Li, Cheng, Ma, Xinrui, Yang, Jing, Zou, Juan, Wu, Ruoyou, Chen, Zan, Feng, Yuanjing, Zheng, Hairong, Wang, Shanshan
Deep learning has emerged as a promising approach for learning the nonlinear mapping between diffusion-weighted MR images and tissue parameters, which enables automatic and deep understanding of the brain microstructures. However, the efficiency and
Externí odkaz:
http://arxiv.org/abs/2405.03159