A new deep learning method for displacement tracking from ultrasound RF signals of vascular walls. (January 2021)

• Record Type: Journal Article
• Authors: Xiao, Chenhui; Li, Zhenzhou; Lu, Jianfeng; Wang, Jinyan; Zheng, Haoteng; Bi, Zuyue; Chen, Mengyang; Mao, Rui; Lu, Minhua
• Is Part Of: Computerized medical imaging and graphics. Volume 87(2021)
• Publication Date: 2021-01
• Subjects: Pulse wave imaging -- Deep learning -- Displacement tracking -- Single target block matching -- Vascular biomechanics; Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnosis, Radioscopic -- Data processing -- Periodicals; Diagnostic Imaging -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Diagnostic imaging; Periodicals; Electronic journals; Electronic journals; 616.0754
• DOI: 10.1016/j.compmedimag.2020.101819
• Languages: English
• ISSNs: 0895-6111

Abstract

Highlights: A new architecture of motion tracking has been proposed. Deep learning method for displacement tracking of the vessel wall from the ultrasound RF signals. It improves the accuracy in vessel wall motion tracking in comparsion with traditional methods. Abstract: It is necessary to monitor the mechanical properties of arteries which directly related to cardiovascular diseases (CVDs) in the early stages. In this study, we proposed a new method based on deep learning (DL) to track the displacement of the vessel wall from the ultrasound radio-frequency (RF) signals, which is a key technique to achieve quantitative measurement of vascular biomechanics. In comparison with traditional method, both results on simulation and experimental carotid artery data demonstrated that the DL method has higher accuracy for motion tracking of artery walls. Hence, the DL method can be widely applied so that can predict the early pathology of cardiovascular system.