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

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 these instruments soft tissue, fiber Bragg gratings (FBGs) based reconstruction methods have gained popularity because inherent advantages optical fibers clinical setting, such as flexibility, immunity electromagnetic interference, nontoxicity, absence line-of-sight issues. However, proposed thus far focus on shape steerable needle itself, where accuracy is susceptible errors interpolation curve fitting used estimate curvature vectors along needle. In this study, we propose reconstructing path created by tip follow-the-leader nature many its variants. By assuming that made equivalent needle, novel approach paves way for single set FBGs at tip, which provides information about every section during navigation. We Kalman-filter-based sensor fusion method update sections they continually estimated insertion process. The validated simulation, in vitro xmlns:xlink="http://www.w3.org/1999/xlink">ex vivo experiments employing programmable bevel-tip (PBN). results show clinically acceptable accuracy, with 2.87-mm mean PBN position error, standard deviation 1.63 mm 120-mm insertion.