Compensation of in-plane rigid motion for in vivo intracoronary ultrasound image sequence

Sun, Zaihong; Wang, Jianjian

doi:10.1016/j.compbiomed.2013.05.004

Cited by 8 publications

(3 citation statements)

References 28 publications

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“…We compare the proposed method with the method proposed by Zheng et al [13] which is a typical representative of the geometric center-based cardiac dynamics compensation method. It also the latest study of conventional algorithms that can simultaneously preserve all data of IVUS sequences and irregular geometry of blood vessels.…”

Section: Resultsmentioning

confidence: 99%

“…As shown in Fig. 6, (a) is a line graph of the mean value of 𝛥𝑑 and (b) is a line graph of the standard deviation of 𝛥𝑑, where the solid blue line is ∆𝑑 𝑏 , the solid green line is ∆𝑑 𝑎 obtained by the existing method [13], and the solid red line is ∆𝑑 𝑎 obtained by our method. It can be observed that our method has lower values for both means and standard deviations, which means that both the overall performance and robustness of the proposed method are better.…”

Section: Resultsmentioning

confidence: 99%

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Cardiac dynamics compensation for intravascular ultrasound sequences

Xiao

2023

Medical Imaging 2023: Image Processing

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Intravascular ultrasound (IVUS) is a well-established imaging technique for the assessment of coronary atherosclerotic plaque. IVUS has the ability to identify the arterial wall morphology, fibrous plaque locations and thicknesses, and internal lumen area. However, the motion of the imaging catheter with respect to the coronary wall caused by cardiac contraction during image acquisition impairs the subsequent visualization and quantification of IVUS image pullbacks. In this study, we propose a method to compensate for cardiac dynamics in IVUS. Keeping the original sensor field center origin unchanged, the vessel geometric centers are first extracted based on lumen segmentation. Subsequently, the periodic fluctuations of the geometric center sequence are used to formulate a heartbeat motion filtering algorithm based on the comb filter. Cardiac dynamics are then compensated by effectively filtering heartbeat deflection from both translational and rotational motion components. We evaluated 35 in vivo IVUS pullbacks and the experimental results showed an average percentage of artifact suppression of 77.82(± 7.55) %, demonstrating the reliability of the presented method in clinical cases. The method eliminates the need for image center correction and helps to maintain the original geometry of the vessel axes. Compared with existing methods, the presented method can more simply extract the heartbeat frequency and effectively filter out the higher order harmonic components of the heartbeat, resulting in more accurate cardiac dynamics compensation.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Cardiac dynamics compensation for intravascular ultrasound sequences

Xiao

2023

Medical Imaging 2023: Image Processing

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“…Periodic cardiac beats also cause longitudinal oscillations of IVOCT imaging catheters, resulting in repeated sampling at a certain location of the vascular lumen, known as motion artifacts in coronary IVOCT image sequences. The methods to compensate the motion artifacts caused by heart beat in coronary image sequences include ECG-gating, off-line image-based gating, rigid body motion model and image matching 12 . This paper uses the gray matching method to compensate the shift of the vascular lumen, and the gray value curve method to compensate the rotation of the vascular lumen, so as to eliminate motion artifacts 13 .…”

Section: Preprocessingmentioning

confidence: 99%

Application of IVOCT to coronary atherosclerotic plaque

Wei

Lin

Zheng

et al. 2022

Optics in Health Care and Biomedical Optics XII

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Coronary atherosclerotic heart disease is one of the main causes of death from cardiovascular diseases. Early detection of atherosclerotic lesions can help clinicians understand the condition of cardiovascular patients and provide reference for better treatment measures. Compared with other detection technologies, intravascular optical coherence tomography (IVOCT) has the advantages of no radiation, high resolution, and high imaging speed. Therefore, it plays an important role in the detection and evaluation of atherosclerotic plaque. Although IVOCT has been widely used in the detection of plaque in coronary vessels, the imaging system could not directly provide effective plaque feature identification information, and clinicians can only judge the characteristics of plaque according to their own experience. Based on a brief introduction of the application of IVOCT in detecting coronary atherosclerotic plaque, this paper introduces the method of eliminating the vascular motion artifact caused by cardiac pulsation. The automatic segmentation and classification of IVOCT images are studied by using machine learning method. And the plaque features of calcified plaque, lipid plaque and fibrous plaque in IVOCT images are extracted. The deep learning algorithm is used to analyze the characteristics of vulnerable plaque and put forward quantitative evaluation indicators. It is very important to develop the intelligent recognition system of IVOCT in plaque type, that provide objective, intuitive and accurate plaque classification marks, display and rupture risk assessment for the clinic. So that clinicians can get rid of the current situation of relying solely on experience for lesion recognition, and save patients' lives in time.

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