Advanced sensors placement for accurate 3D needle shape reconstruction

Needles are tools widely used in minimally invasive surgery. During such procedures the localization of the needle and its tip is a challenging situation because of the needle deformations due to its interactions with tissues. To tackle this problem, instrumented needles with sensors have been currently developed to allow needle reconstruction and tip localization. In conventional surgery this difficulty is overcome by medical imaging. The interest in using an instrumented needle resides in the possible dispense of medical imaging. This papers develops new methods to reconstruct needles in three dimensions and to find the locations of sensors which minimizes the error of reconstruction of the needle. A notable feature of our method is that input data are based on real needle data, that should assure a better representation of reality. Reconstructions simulated with 22 gauge 200 mm long needles show that the localization of the needle tip is more accurate by 18% to 52% with optimal sensors positions compared to equidistant sensors positions.

[1]  C. Reinsch.  Smoothing by spline functions , 1967 .

[2]  Ivan Bricault,et al.  A generic three-dimensional static force distribution basis for a medical needle inserted into soft tissue. , 2013, Journal of the mechanical behavior of biomedical materials.

[3]  R. J. Black,et al.  Real-Time Estimation of 3-D Needle Shape and Deflection for MRI-Guided Interventions , 2010, IEEE/ASME Transactions on Mechatronics.

[4]  Bert Jüttler,et al.  Computation of rotation minimizing frames , 2008, TOGS.

[5]  Alexandre Moreau-Gaudry,et al.  Modelling and characterization of an instrumented medical needle in sight of new microsensor design for its insertion guidance , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[6]  Christopher H. Mitchell,et al.  A Review of the Benefits and Pitfalls of Phantoms in Ultrasound-Guided Regional Anesthesia , 2010, Regional Anesthesia & Pain Medicine.

[7]  Yohan Payan,et al.  SOFT TISSUE BIOMECHANICAL MODELING FOR COMPUTER ASSISTED SURGERY: CHALLENGES AND PERSPECTIVES , 2016 .

[8]  Rida T. Farouki,et al.  Rational approximation schemes for rotation-minimizing frames on Pythagorean-hodograph curves , 2003, Comput. Aided Geom. Des..

[9]  Sarthak Misra,et al.  3D flexible needle steering in soft-tissue phantoms using Fiber Bragg Grating sensors , 2013, 2013 IEEE International Conference on Robotics and Automation.

[10]  Céline Fouard,et al.  CamiTK: A Modular Framework Integrating Visualization, Image Processing and Biomechanical Modeling , 2012 .

[11]  Sébastien Le Digabel,et al.  Algorithm xxx : NOMAD : Nonlinear Optimization with the MADS algorithm , 2010 .

[12]  Gabor Fichtinger,et al.  Real-time tracking of a bevel-tip needle with varying insertion depth: Toward teleoperated MRI-guided needle steering , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[13]  S. Misra,et al.  Three-Dimensional Needle Shape Reconstruction Using an Array of Fiber Bragg Grating Sensors , 2014, IEEE/ASME Transactions on Mechatronics.

[14]  D. Downey,et al.  Brachytherapy needle deflection evaluation and correction. , 2005, Medical physics.

[15]  R. Bishop.  There is More than One Way to Frame a Curve , 1975 .