Left Ventricle Motion Estimation in Computed Tomography Imaging

Simon, Antoine; Garreau, Mireille; Delaunay, Régis; Boulmier, Dominique; Donal, Erwan; Leclercq, Christophe

doi:10.1002/9781118574362.ch8

Cited by 1 publication

(1 citation statement)

References 52 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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