Cardiac Motion Estimation in Magnetic Resonance Images Using Optical Flow

Medina, Rubén; Ibarra, Emiro; Morocho, Villie; Vanegas, Pablo

doi:10.1109/tla.2016.7555259

Cited by 5 publications

(5 citation statements)

References 24 publications

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“…The topic of heart motion estimation has already been tackled in several prior works. In general, the majority of techniques employed for this task rely on one the following imaging modalities: a) fluoroscopy [9], [10], b) ultrasound [11], [12], c) computed tomography (CT) [13], [14], d) camera vision [15], [16], or e) ECG-gated magnetic resonance (MR) [17]- [19]. While the underlying techniques and algorithms may differ between the aforementioned imaging modalities, the general work-flow is similar.…”

Section: B Prior Workmentioning

confidence: 99%

Local One-Dimensional Motion Estimation Using FBG-Based Shape Sensing for Cardiac Applications

Al-Ahmad

Ourak

Vlekken³

et al. 2022

IEEE Robot. Autom. Lett.

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The human heart is a fragile and dynamic organ that requires careful approach during catheter intervention. It is essential for physicians to have a high degree of awareness of the anatomy and the relative pose of the catheter when operating inside the heart. One of the key aspects that can aid physicians during such interventions is knowledge of the heart's motion profile. Accordingly, this work addresses the objective of estimating local predominantly one-dimensional heart motions by making use of FBG-inscribed multi-core fibers and shape sensing. An FBG-fiber embedded in a cardiac catheter is propagated towards a designated region in the heart, where a force sensor at the tip of the catheter is used to identify contact with the heart tissue. The catheter's tip position is continuously monitored during contact with the moving tissue. The proposed method is able to determine the direction of the local surface motion and estimates the evolution of the motion profile through time. An Unscented Kalman Filter (UKF) is thus employed to provide continuous quasi-periodic estimation of this motion. Experiments on a bench-top laboratory heart mock-up were carried out to validate the proposed approach. Results show that the proposed method can provide accurate estimations of the heart motion profile with an absolute mean error of 1.1 ± 0.4 mm (9.2%) for motions with average peak-to-peak amplitudes of 12 mm.

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Section: B Prior Workmentioning

confidence: 99%

Local One-Dimensional Motion Estimation Using FBG-Based Shape Sensing for Cardiac Applications

Al-Ahmad

Ourak

Vlekken³

et al. 2022

IEEE Robot. Autom. Lett.

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“…In this paper presents a semi-automatic approach in this study that is capable of identifying the endocardial borders robustly from cine magnetic resonance images using deformation flow and optical flow [15]. This paper reports a quantification of cardiac motion in Short-Axis (SAX) MRI images using the optical flow method [16]. In this paper presents estimates the motion of cardiac ultrasound imaging and compare with several classical optical flow estimation algorithms [17,18].…”

Section: Introductionmentioning

confidence: 99%

Improved echocardiography segmentation using active shape model and optical flow

et al. 2019

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Heart disease is one of the most dangerous diseases that threaten human life. The doctor uses echocardiography to analyze heart disease. The result of echocardiography test is a video that shows the movement of the heart rate. The result of echocardiography test indicates whether the patient's heart is normal or not by identifying a heart cavity area. Commonly it is determined by a doctor based on his own accuracy and experience. Therefore, many methods to do heart segmentation is appearing. But, the methods are a bit slow and less precise. Thus, a system that can help the doctor to analyze it better is needed. This research will develop a system that can analyze the heart rate-motion and automatically measure heart cavity area better than the existing method. This paper proposes an improved system for cardiac segmentation using median high boost filter to increase image quality, followed by the use of an active shape model and optical flow. The segmentation of the heart rate-motion and auto measurement of the heart cavity area is expected to help the doctor to analyze the condition of the patient with better accuracy. Experimental result validated our approach.

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“…15 Guided by the method, Bertrand et al studied the movement of the heart structure from the perspective of tracking the spot light field. 14 One of them is based on the optical flow field estimation; the cardiac motion is quantified in a short-axis slice located at the midcavity of the left ventricle.…”

Section: Introductionmentioning

confidence: 99%

“…The core of this cardiac motion quantification method is a sparse-based algorithm for optical flow estimation. 15 Guided by the method, Bertrand et al studied the movement of the heart structure from the perspective of tracking the spot light field. 16 Another common method is called speckle tracking and the results are more prominent with speckle tracking.…”

Section: Introductionmentioning

confidence: 99%

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Visualization analysis of heart diseases using two‐dimensional electrocardiogram sequences

Fang

Ying

Lai

et al. 2019

Adaptive Control & Signal

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During the last years, there have been large advances in the understanding of cardiac mechanics, and in particular shape, motion and deformation. However, it is important to comprehend how these features interrelate and how they can be interpreted to refine the clinical diagnosis. The goal of our study is to illustrate the use of two-dimensional ultrasound cardiac motion sequences on some typical cardiac pathologies. The method is applied to preprocessed data from two-dimensional ultrasound sequences of four different subjects, namely, two healthy volunteers, one patient with left ventricular mechanical dyssynchrony from the left bundle branch block, and one patient with hypertrophic cardiomyopathy. The interpretation based on these features is corroborated with other analyzing techniques such as electrocardiogram and visual interpretation by experienced clinical observers. The proposed method realizes a computer program, which allows the user to easily obtain and visualize information about myocardial shape, motion (displacement and velocity), and deformation (strain) along the left ventricle myocardium wall during the cardiac cycle.

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Cardiac Motion Estimation in Magnetic Resonance Images Using Optical Flow

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Cited by 5 publications

References 24 publications

Local One-Dimensional Motion Estimation Using FBG-Based Shape Sensing for Cardiac Applications

Local One-Dimensional Motion Estimation Using FBG-Based Shape Sensing for Cardiac Applications

Improved echocardiography segmentation using active shape model and optical flow

Visualization analysis of heart diseases using two‐dimensional electrocardiogram sequences

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