Search

Your search keyword '"Xiong, Huahua"' showing total 37 results
Start Over Author "Xiong, Huahua"
37 results on '"Xiong, Huahua"'

6. Carotid Artery Wall Motion Estimated from Ultrasound Imaging Sequences Using a Nonlinear State Space Approach

Author

Gao, Zhifan, Sun, Yuanyuan, Zhang, Heye, Ghista, Dhanjoo, Li, Yanjie, Xiong, Huahua, Liu, Xin, Xie, Yaoqin, Wu, Wanqing, Li, Shuo, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Weikum, Gerhard, Series Editor, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Ourselin, Sebastien, editor, Joskowicz, Leo, editor, Sabuncu, Mert R., editor, Unal, Gozde, editor, and Wells, William, editor

Published
2016
Full Text
View/download PDF

7. RL-TEE: Autonomous Probe Guidance for Transesophageal Echocardiography Based on Attention-Augmented Deep Reinforcement Learning

Author

Li, Keyu, Li, Ang, Xu, Yangxin, Xiong, Huahua, and Meng, Max Q.-H.

Abstract

Ultrasound image acquisition in conventional transesophageal echocardiography (TEE) requires complex manual operation of the probe in the esophagus based on the interpretation of ultrasound images and in-depth knowledge of the cardiac anatomy. In this work, we formulate the TEE probe guidance task as a reinforcement learning (RL) problem, and present the first learning-based solution to 3-DOF control of a TEE probe based on the ultrasound image feedback, named RL-TEE, in order to mimic the visual search and navigation strategies of expert echocardiographers. The probe-tissue interaction in TEE is carefully modeled in our framework by considering both the requirements for navigation towards the standard views and compliance in the esophageal environment. Furthermore, we propose a hybrid deep Q-network model that augments a convolutional neural network backbone with self-attention mechanisms to better capture spatial information in ultrasound images to guide navigation decisions. The presented methods are preliminarily validated in a TEE simulation environment built with data from 25 subjects to acquire four standard views of the heart. Our results show that the proposed method can effectively learn to accurately and compliantly guide the probe movement for TEE standard view acquisition tasks and has a good generalization ability to unseen patient data. Note to Practitioners—The motivation of this paper is to realize 3-DOF movement guidance of a TEE probe to acquire the standard views of the heart based on the real-time images, which can be applied to existing robotic control systems or used to assist novice echocardiographers in TEE examination, thereby relieving operator workload and improving ease of use. This paper suggests a novel approach that uses the deep RL technique to achieve automatic interpretation of TEE images and intelligent guidance of the probe movement. The RL framework is designed to take into account both the navigation efficiency and compliance with the esophageal environment for the targeted intracorporeal application. A hybrid deep Q-network model that augments a convolutional neural network with attention mechanisms is designed to better capture spatial information from ultrasound images to predict the probe movement. The effectiveness of the framework is preliminarily validated in extensive experiments in a simulation environment built with real patient data. The proposed method can be applied in clinical use to provide real-time TEE probe guidance for novice echocardiographers, and can be integrated with a robotic system to fully automate the TEE acquisition, thereby relieving the doctors from tedious manual operation to focus on the diagnosis and treatment.

Published
2024
Full Text
View/download PDF

12. Motion Estimation of Common Carotid Artery Wall Using a H ∞ Filter Based Block Matching Method

Author

Gao, Zhifan, Xiong, Huahua, Zhang, Heye, Wu, Dan, Lu, Minhua, Wu, Wanqing, Wong, Kelvin K. L., Zhang, Yuan-Ting, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Navab, Nassir, editor, Hornegger, Joachim, editor, Wells, William M., editor, and Frangi, Alejandro F., editor

Published
2015
Full Text
View/download PDF

13. A Novel Nomogram Based on Imaging Biomarkers of Shear Wave Elastography, Angio Planewave Ultrasensitive Imaging, and Conventional Ultrasound for Preoperative Prediction of Malignancy in Patients with Breast Lesions

Author

Guo, Guoqiang, primary, Feng, Jiaping, additional, Jin, Chunchun, additional, Gong, Xuehao, additional, Chen, Yihao, additional, Chen, Sihan, additional, Wei, Zhanghong, additional, Xiong, Huahua, additional, and Lu, Jianghao, additional

Published
2023
Full Text
View/download PDF

22. Chinese association of ultrasound in medicine and engineering, superficial organs and peripheral vessels committee expert consensus on clinical frequently asked questions in breast ultrasonography, June 2018

Author

Jiang, Tian'an, primary, Jiang, Yuxin, additional, Chen, Wen, additional, Luo, Baoming, additional, Peng, Yulan, additional, Wang, Zhili, additional, Xu, Jinfeng, additional, Zhou, Jianqiao, additional, Zhou, Qi, additional, Bao, Lingyun, additional, Chen, Li, additional, Chen, Lin, additional, Chen, Qin, additional, Cong, Shuzhen, additional, Cui, Kefei, additional, Cui, Xinwu, additional, Fang, Qinmao, additional, Li, Fengsheng, additional, Li, Huiwen, additional, Li, Jing, additional, Li, Jianchu, additional, Li, Junlai, additional, Li, Quanshui, additional, Li, Tianliang, additional, Li, Tao, additional, Li, Yanjiang, additional, Li, Zhengyi, additional, Liu, Jia, additional, Liu, Yong, additional, Luo, Jun, additional, Ma, Buyun, additional, Ma, Zhe, additional, Nie, Fang, additional, Peng, Chengzhong, additional, Sui, Xiufang, additional, Sun, Hongguang, additional, Wang, Hongqiao, additional, Wang, Jing, additional, Wang, Yan, additional, Wu, Changjun, additional, Xiao, Ying, additional, Xiong, Huahua, additional, Xu, Dong, additional, Xue, Ensheng, additional, Xue, Lifang, additional, Yan, Songli, additional, Yang, Lichun, additional, Yong, Qiang, additional, Zhan, Weiwei, additional, Zhang, Sheng, additional, Zhang, Xuezhen, additional, Zhang, Jianxing, additional, Zhou, Xianli, additional, Lin, Xi, additional, Yin, Shanyu, additional, and Zhao, Qiyu, additional

Published
2018
Full Text
View/download PDF

36. 1489: The Study of Ultrasonographic Characteristics in Diagnosis of Thyroid Microcarcinoma

Author

Guo Guoqiang, Su Hui, Li Quanshui, Zou Xia, Zhang Jiating, Chen Shenghua, and Xiong Huahua

Subjects
medicine.medical_specialty, medicine.anatomical_structure, Acoustics and Ultrasonics, Radiological and Ultrasound Technology, business.industry, Thyroid, Biophysics, medicine, Radiology, Nuclear Medicine and imaging, Radiology, Ultrasonography, business
Published
2009

37. Functional assessment of the stenotic carotid artery by CFD-based pressure gradient evaluation.

Author

Liu X, Zhang H, Ren L, Xiong H, Gao Z, Xu P, Huang W, and Wu W

Subjects
Aged, Blood Flow Velocity, Carotid Stenosis diagnostic imaging, Female, Humans, Hydrodynamics, Imaging, Three-Dimensional, Magnetic Resonance Imaging, Male, Middle Aged, Models, Cardiovascular, Ultrasonography, Doppler, Blood Pressure, Carotid Stenosis physiopathology, Hemodynamics, Patient-Specific Modeling
Abstract

The functional assessment of a hemodynamic significant stenosis base on blood pressure variation has been applied for evaluation of the myocardial ischemic event. This functional assessment shows great potential for improving the accuracy of the classification of the severity of carotid stenosis. To explore the value of grading the stenosis using a pressure gradient (PG)-we had reconstructed patient-specific carotid geometries based on MRI images-computational fluid dynamics were performed to analyze the PG in their stenotic arteries. Doppler ultrasound image data and the corresponding MRI image data of 19 patients with carotid stenosis were collected. Based on these, 31 stenotic carotid arterial geometries were reconstructed. A combinatorial boundary condition method was implemented for steady-state computer fluid dynamics simulations. Anatomic parameters, including tortuosity (T), the angle of bifurcation, and the cross-sectional area of the remaining lumen, were collected to investigate the effect on the pressure distribution. The PG is highly correlated with the severe stenosis (r = 0.902), whereas generally, the T and the angle of the bifurcation negatively correlate to the pressure drop of the internal carotid artery stenosis. The calculation required <10 min/case, which made it prepared for the fast diagnosis of the severe stenosis. According to the results, we had proposed a potential threshold value for distinguishing severe stenosis from mild-moderate stenosis (PG = 0.88). In conclusion, the PG could serve as the additional factor for improving the accuracy of grading the severity of the stenosis., (Copyright © 2016 the American Physiological Society.)

Published
2016
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results