Multi-Objective Higher Order Polynomial Networks to Model Insertion Force of Bevel-Tip Needles

Needle insertion has been a very popular minimal invasive surgery method in cancer detection, soft tissue properties recognition and many other surgical operations. Its applications were observed in brain biopsy, prostate brachytherapy and many percutaneous therapies. In this study the authors would like to provide a model of needle force in soft tissue insertion. This model has been developed using higher order polynomial networks. In order to provide a predictive model one-dimensional force sensed on enacting end of bevel-tip needles. The speeds of penetration for quasi-static processes have chosen to be in the range of between 5 mm/min and 300 mm/min. Second and third orders of polynomials employed in the network which contains displacement and speed as their main affecting parameters in the simplified model. Results of fitting functions showed a reliable accuracy in force-displacement graph.

[1]  Denis Laurendeau,et al.  Modelling liver tissue properties using a non-linear visco-elastic model for surgery simulation , 2005, Medical Image Anal..

[2]  Edward L. Chaney,et al.  Automated Finite-Element Analysis for Deformable Registration of Prostate Images , 2007, IEEE Transactions on Medical Imaging.

[3]  Septimiu E. Salcudean,et al.  Needle insertion modeling and simulation , 2003, IEEE Trans. Robotics Autom..

[4]  D. Das,et al.  Effect of Force of Microneedle Insertion on the Permeability of Insulin in Skin , 2014, Journal of diabetes science and technology.

[5]  Joydeep Ghosh,et al.  Ridge polynomial networks , 1995, IEEE Trans. Neural Networks.

[6]  Pierre E. Dupont,et al.  Mechanics of Dynamic Needle Insertion into a Biological Material , 2010, IEEE Transactions on Biomedical Engineering.

[7]  Cheng-Lung Huang,et al.  A GA-based feature selection and parameters optimizationfor support vector machines , 2006, Expert Syst. Appl..

[8]  J. Webster,et al.  Large-volume radiofrequency ablation of ex vivo bovine liver with multiple cooled cluster electrodes. , 2005, Radiology.

[9]  Allison M. Okamura,et al.  Planning for Steerable Bevel-tip Needle Insertion Through 2D Soft Tissue with Obstacles , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[10]  Gabor Fichtinger,et al.  Needle deflection estimation: prostate brachytherapy phantom experiments , 2014, International Journal of Computer Assisted Radiology and Surgery.

[11]  Pierre E. Dupont,et al.  Fast needle insertion to minimize tissue deformation and damage , 2009, 2009 IEEE International Conference on Robotics and Automation.

[12]  Jan J W Lagendijk,et al.  A new robotic needle insertion method to minimise attendant prostate motion. , 2006, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[14]  Mohammad Hassan Moradi,et al.  A Hybrid Higher Order Neural Classifier for handling classification problems , 2011, Expert Syst. Appl..

[15]  Gary B. Lamont,et al.  Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation) , 2006 .

[16]  K J Macura,et al.  The importance of organ geometry and boundary constraints for planning of medical interventions. , 2009, Medical engineering & physics.

[17]  Vincent Hayward,et al.  Estimation of the Fracture Toughness of Soft Tissue from Needle Insertion , 2008, ISBMS.

[18]  J B Ra,et al.  Spine needle biopsy simulator using visual and force feedback. , 2002, Computer aided surgery : official journal of the International Society for Computer Aided Surgery.

[19]  T Christian Gasser,et al.  Failure mechanisms of ventricular tissue due to deep penetration. , 2009, Journal of biomechanics.

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

[21]  Martin T. Hagan,et al.  Neural network design , 1995 .

[22]  Allison M. Okamura,et al.  Force modeling for needle insertion into soft tissue , 2004, IEEE Transactions on Biomedical Engineering.

[23]  Mostafa Rostami,et al.  Primary design of MRI compatible needle for the purpose of soft tissue insertion , 2010, 2010 17th Iranian Conference of Biomedical Engineering (ICBME).

[24]  H Iseki,et al.  Development of an MRI-compatible needle insertion manipulator for stereotactic neurosurgery. , 1995, Journal of image guided surgery.

[25]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[26]  P. Rizun,et al.  Robot-Assisted Neurosurgery , 2004, Seminars in laparoscopic surgery.

[27]  Gang Wan,et al.  Robot-assisted 3D-TRUS guided prostate brachytherapy: system integration and validation. , 2004, Medical physics.

[28]  Rajni V. Patel,et al.  Needle insertion into soft tissue: a survey. , 2007, Medical engineering & physics.

[29]  Ron Alterovitz,et al.  Needle path planning and steering in a three-dimensional non-static environment using two-dimensional ultrasound images , 2014, Int. J. Robotics Res..

[30]  Joydeep Ghosh,et al.  The pi-sigma network: an efficient higher-order neural network for pattern classification and function approximation , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.

[31]  Bernard Bayle,et al.  Needle insertions modeling: Identifiability and limitations , 2007, Biomed. Signal Process. Control..

[32]  Peter Kazanzides,et al.  Medical Robotics and Computer-Integrated Interventional Medicine , 2008, Adv. Comput..

[33]  Mehrdad R. Kermani,et al.  Dynamics of Translational Friction in Needle–Tissue Interaction During Needle Insertion , 2013, Annals of Biomedical Engineering.

[34]  Hashem Yousefi,et al.  Applications of needle insertion with rotating capability in manipulator , 2010, 2010 17th Iranian Conference of Biomedical Engineering (ICBME).