Trajectory Generation of FBG-Sensorized Needles for Insertions into Multi-Layer Tissue
Dimitri A Lezcano1, Iulian I Iordachita1, Jin Seob Kim1
1Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD, USA.
Abstract:
Several models incorporate needle shape prediction, however prediction in multi-layer tissue for complex needle shape remains an issue. In this work, we present a method for trajectory generation of flexible needles that allows for complex curvatures, extending upon a previous sensor-based model. This model combines curvature measurements from fiber Bragg grating (FBG) sensors and the mechanics of an inextensible elastic rod for shape-sensing. We evaluate the method's effectiveness in single- and double-layer isotropic tissue prediction. The results illustrate a valid trajectory generation method accounting for complex curvatures in flexible needles.
