Accurate navigation of flexible medical instruments like catheters require the knowledge of its pose, that is its position and orientation. In this paper multi-core fibers inscribed with fiber Bragg gratings (FBG) are utilized as sensors to measure the pose of a multi-segment catheter. A reconstruction technique that provides the pose of such a fiber is presented. First, the measurement from the Bragg gratings are converted to strain then the curvature is deduced based on those strain calculations. Next, the curvature and the Bishop frame equations are used to reconstruct the fiber. This technique is validated through experiments where the mean error in position and orientation is observed to be less than 4.69 mm and 6.48 degrees, respectively. The main contributions of the paper are the use of Bishop frames in the reconstruction and the experimental validation of the acquired pose.
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.
Over the past 10 years, minimally invasive surgery (MIS) has shown significant benefits compared to conventional surgical techniques, with reduced trauma, shorter hospital stays, and shorter patient recovery times. In neurosurgical MIS procedures, inserting a straight tool (e.g. catheter) is common practice in applications ranging from biopsy and laser ablation, to drug delivery and fluid evacuation. How to handle tissue deformation, target migration and access to deep-seated anatomical structures remain an open challenge, affecting both the preoperative planning phase and eventual surgical intervention. Here, we present the first neurosurgical platform in the literature, able to deliver an implantable steerable needle for a range of diagnostic and therapeutic applications, with a short-term focus on localised drug delivery. This work presents the system’s architecture and first in vivo deployment with an optimised surgical workflow designed for pre-clinical trials with the ovine model, which demonstrate appropriate function and safe implantation.
Steerable needles have the potential for accurate needle tip placement even when the optimal path to a target tissue is curvilinear, thanks to their ability to steer, which is an essential function to avoid piercing through vital anatomical features. Autonomous path-following controllers for steerable needles have already been studied, however they remain challenging, especially because of the complexities associated to needle localization. In this context, the advent of fiber Bragg Grating (FBG)-inscribed multi-core fibers (MCFs) holds promise to overcome these difficulties. Objective: In this study, a closed-loop, 3-D path-following controller for steerable needles is presented. Methods: The control loop is closed via the feedback from FBG-inscribed MCFs embedded within the needle. The nonlinear guidance law, which is a wellknown approach for path-following control of aerial vehicles, is used as the basis for the guidance method. To handle needle-tissue interactions, we propose using Active Disturbance Rejection Control (ADRC) because of its robustness within hard-to-model environments. We investigate both linear and nonlinear ADRC, and validate the approach with a Programmable Bevel-tip Steerable Needle (PBN) in both phantom tissue and ex vivo brain, with some of the experiments involving moving targets. Results: The mean, standard deviation, and maximum absolute position errors are observed to be 1.79 mm, 1.04 mm, and 5.84 mm, respectively, for 3-D, 120 mm deep, path-following experiments. Conclusion: MCFs with FBGs are a promising technology for autonomous steerable needle navigation, as demonstrated here on PBNs. Significance: FBGs in MCFs can be used to provide effective feedback in pathfollowing controllers for steerable needles.
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