A novel structural features-based approach to automatically extract multiple motion parameters from single-arm X-ray angiography. (February 2017)

• Record Type: Journal Article
• Authors: Zhang, Tianxu; Huang, Zhenghua; Huang, Yining; Wang, Guozhu; Sun, Xiangping; Sang, Nong; Chen, Wufan
• Is Part Of: Biomedical signal processing and control. Volume 32(2017)
• Page Start: 29
• Page End: 43
• Publication Date: 2017-02
• Subjects: Single-arm X-ray angiography -- Short duration signal separation -- Multi-motion parameter model -- Compensated DFT; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2016.09.012
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract : Graphical abstract: Abstract : Highlights: A novel multi-motion parameter model of single-arm angiography sequence. Automatic detecting and tracking robust vascular branch points. A novel time-frequency domains iteration separation algorithm on multi-parameters. Providing clinical support for clinicians to quantitatively analyze heart disease. Abstract: It is essential to extract dynamic information about a patient's heart from a medical X-ray angiography image sequence for a quantitative medical diagnosis. As the motions included in the angiography sequences are the mixture of various motion signals, such as the body's integral translation, respiratory motion, cardiac impulse, and tremor, automatic separation of these signals challenges the effectiveness of the image information processing method. This paper has proposed an optimal time-frequency domains iteration separation algorithm for multi-motion parameters (TFISA-MMP) to obtain the physiological parameters including the translation. The main procedures of the TFISA-MMP algorithm include three parts. First, the algorithm automatically extracted a set of relatively stable branch points from the coronary artery angiography image, and then automatically tracked these branch points in the sequence image to obtain their motion curves that changed with the time. Second, with the guidance of the multi-motion parameter model, the initial values of each component were estimated based on Discrete Fourier Transformation (DFT). Moreover, the initial values of each motion component were optimized using the global mean square minimum error between the estimated reconstructed signal and original signal and the local mean square minimum error in the frequency domain for each frequency component. Finally, these motion components were estimated by minimizing the residual signal between the original signal and the reconstructed signal via the loop iteration to obtain the estimated optimal motion components, such as two-dimensional (2D) heartbeat, tremor, respiratory motion, and translational motion. Both visible human coronary model simulation experiments and the clinical experiments of single-arm X-ray angiography images of many individuals verified the correctness, validity, and clinical applicability of the separation method.