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

Abstract

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.

Force Control With a Novel Robotic Catheterization System Based on Braided Sleeve Grippers

“…Our previous work on local one-dimensional heart motion estimation based on a multi-core fiber (MCF) with inscribed fiber Bragg gratings (FBGs) [37] is combined with the RACS development to enable dynamic tissue tracking. The work is expanded to obtain a practical method for real-time instrument tip pose tracking to be employed for hysteresis identification and compensation within robotic catheterization procedures.…”

“…While the underlying techniques and algorithms may differ between the aforementioned imaging modalities, the general work-flow is similar. On the other hand, our previous work [37] focused on achieving similar results following a different methodology. The work was concerned with local one-dimensional heart motion estimation based on FBG-inscribed MCFs.…”

“…For brevity, only an overview of the method was provided here. The reader is referred to our previous work for a detailed description [37]. The estimated heart motion, presented in our previous work, would serve as the motion controller's feedforward signal.…”

Physiological Motion Compensation for Neuroscience Research Based on Electrical Bio-Impedance Sensing

“…In these studies, the ECG and respiratory events were used to compensate for motion by an average extracted impulse response of heart rate/breathing, in which a real-time motion estimation was not foreseen and had to be manually scaled in amplitude. In addition to neuroscience, motion compensation is also attracting the attention of different medical applications with various approaches, such as imaging-assisted techniques [21]- [23], novel sensory feedback [24], [25], and machine/deep learning methods [26]- [28].…”

Femtosecond Laser Line-by-Line Inscribed Seven Core Fiber Cascaded Fabry–Perot Cavity and Its Vectorial Bending Sensing Application

“…On the other hand, multi-core fibers (MCFs) have increasingly attracted the attention of researchers in the field of optical communications, due to exponential growth in data transmission requirements. Thanks to the special cross-sectional geometry of MCFs, multiple, one-dimensional bending sensors can be integrated into the single MCF [17][18][19], thus constructing a compact and robust fiber vector bending sensor. In 2022, Qi et al proposed construction of the vectorial bending sensor by splicing a section of quartz capillary fiber between a seven-core fiber (SCF) and a multimode fiber, but with complex structure and high insertion loss [20].…”