Optimized needle shape reconstruction using experimentally based strain sensors positioning

Needles are tools that are used daily during minimally invasive procedures. During the insertions, needles may be affected by deformations which may threaten the success of the procedure. To tackle this problem, needles with embedded strain sensors have been developed and associated with navigation systems. The localization of the needle in the tissues is then obtained in real time by reconstruction from the strain measurements, allowing the physician to optimize its gesture. As the number of strain sensors embedded is limited in number, their positions on the needle have a great impact on the accuracy of the shape reconstruction. The main contribution of this paper is a novel strain sensor positioning method to improve the reconstruction accuracy. A notable feature of our method is the use of experimental needle insertion data, which increases the relevancy of the resulting sensor optimal locations. To the best of the author’s knowledge, no experimentally based needle sensor positioning method has been presented yet. Reconstruction validations from clinical data show that the localization accuracy of the needle tip is improved by almost 40% with optimal locations compared with equidistant locations when reconstructing with two sensor triplets or more.