Kalman-Filter-Based, Dynamic 3-D Shape Reconstruction for Steerable Needles With Fiber Bragg Gratings in Multicore Fibers

Abstract

Steerable needles are a promising technology to provide safe deployment of tools through complex anatomy in minimally invasive surgery, including tumor-related diagnoses and therapies. For the 3-D localization of these instruments in soft tissue, fiber Bragg gratings (FBGs)-based reconstruction methods have gained in popularity because of the inherent advantages of optical fibers in a clinical setting, such as flexibility, immunity to electromagnetic interference, non-toxicity, the absence of line of sight issues. However, methods proposed thus far focus on shape reconstruction of the steerable needle itself, where accuracy is susceptible to errors in interpolation and curve fitting methods used to estimate the curvature vectors along the needle. In this study, we propose reconstructing the shape of the path created by the steerable needle tip based on the follow-the-leader nature of many of its variants. By assuming that the path made by the tip is equivalent to the shape of the needle, this novel approach paves the way for shape reconstruction through a single set of FBGs at the needle tip, which provides curvature information about every section of the path during navigation. We propose a Kalman Filter-based sensor fusion method to update the curvature information about the sections as they are continually estimated during the insertion process. The proposed method is validated through simulation, in vitro and ex vivo experiments employing a programmable bevel-tip steerable needle (PBN). The results show clinically acceptable accuracy, with 2.87 mm mean PBN tip position error, and a standard deviation of 1.63 mm for a 120 mm 3-D insertion.

3-D Path-Following Control for Steerable Needles With Fiber Bragg Gratings in Multi-Core Fibers

“…FBG-based curvature estimation and shape reconstruction methods are summarized in this section. A detailed explanation, including 3-D validation, of these methods are given in [31], where the mean and maximum PBN tip absolute position errors, and the standard deviation are found with a 120-mm insertion to be 2.87 mm, 5.76 mm, and 1.63 mm, respectively. The assumptions mentioned in this section are not discussed in this study.…”

“…The assumptions mentioned in this section are not discussed in this study. Readers may refer to [31] for the details.…”

“…Therefore, this equation set is solved for κ(s), β(s), and ε 0 (s). Although the explanation is given for 3 off-centered cores for brevity, in the presence of more than 3 off-centered cores, as in the validation experiments in this study, the equations from redundant cores are used to increase accuracy [31]. Left: FBG-inscribed MCF including 3 off-centered cores.…”

“…Among steerable needle controllers, a significant majority of studies used electromagnetic sensors, ultrasound sensors, and cameras for the localization of the needle tip. On the other hand, although FBG-based shape sensing has not been used as much as the other approaches, it is now a promising technology, especially after the advent of multi-core optical fibers (MCFs) [31], [32], which eliminates the issues arising from size and positioning problems of the single-core optical fibers. In addition, the advantages of FBG-based shape sensing include the absence of line of sight issues, being small in size, immunity to electromagnetic interference, flexibility, nontoxicity, and suitability for dynamic real-time applications.…”

Organ Shape Sensing using Pneumatically Attachable Flexible Rails in Robotic-Assisted Laparoscopic Surgery

“…Sefati et al calculated the position of the tip of a continuum manipulator from FBG-based shape sensing and optimized the control algorithm that steers the manipulator around obstacles [27]. Most recently, FBGs have been integrated into steerable needles to facilitate dynamic 3-D shape reconstruction of the needle tip [28]. In this instance, a novel shape reconstruction method is proposed to reduce the errors reported in more conventional interpolation and curve fitting methods.…”