Research Papers

Modeling, Simulation, and Optimal Initiation Planning For Needle Insertion Into the Liver

[+] Author and Article Information
R. Sharifi Sedeh

Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139rsharifi@mit.edu

M. T. Ahmadian

Department of Mechanical Engineering, Sharif University of Technology, Tehran 11155–8639, Iranahmadian@sharif.edu

F. Janabi-Sharifi1

Department of Mechanical and Industrial Engineering, Ryerson University, 350 Victoria Street, Toronto, ON, M5B2K3, Canadafsharifi@ryerson.ca


Corresponding author.

J Biomech Eng 132(4), 041001 (Mar 08, 2010) (11 pages) doi:10.1115/1.4000953 History: Received August 19, 2008; Revised December 13, 2009; Posted January 06, 2010; Published March 08, 2010; Online March 08, 2010

Needle insertion simulation and planning systems (SPSs) will play an important role in diminishing inappropriate insertions into soft tissues and resultant complications. Difficulties in SPS development are due in large part to the computational requirements of the extensive calculations in finite element (FE) models of tissue. For clinical feasibility, the computational speed of SPSs must be improved. At the same time, a realistic model of tissue properties that reflects large and velocity-dependent deformations must be employed. The purpose of this study is to address the aforementioned difficulties by presenting a cost-effective SPS platform for needle insertions into the liver. The study was constrained to planar (2D) cases, but can be extended to 3D insertions. To accommodate large and velocity-dependent deformations, a hyperviscoelastic model was devised to produce an FE model of liver tissue. Material constants were identified by a genetic algorithm applied to the experimental results of unconfined compressions of bovine liver. The approach for SPS involves B-spline interpolations of sample data generated from the FE model of liver. Two interpolation-based models are introduced to approximate puncture times and to approximate the coordinates of FE model nodes interacting with the needle tip as a function of the needle initiation pose; the latter was also a function of postpuncture time. A real-time simulation framework is provided, and its computational benefit is highlighted by comparing its performance with the FE method. A planning algorithm for optimal needle initiation was designed, and its effectiveness was evaluated by analyzing its accuracy in reaching a random set of targets at different resolutions of sampled data using the FE model. The proposed simulation framework can easily surpass haptic rates (>500Hz), even with a high pose resolution level (30). The computational time required to update the coordinates of the node at the needle tip in the provided example was reduced from 177 s to 0.8069 ms. The planning accuracy was acceptable even with moderate resolution levels: root-mean-square and maximum errors were 1 mm and 1.2 mm, respectively, for a pose and PPT resolution levels of 17 and 20, respectively. The proposed interpolation-based models significantly improve the computational speed of needle insertion simulation and planning, based on the discretized (FE) model of the liver and can be utilized to establish a cost-effective planning platform. This modeling approach can also be extended for use in other surgical simulations.

Copyright © 2010 by American Society of Mechanical Engineers
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Figure 5

Planning error for different position-orientation and time resolutions: (a) RMSE and (b) maximum error

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Figure 6

The new positions of the node 10790 (initially at x=5 cm, y=6.45 cm) versus needle insertion state at the PPT of 2.5 s: (a) x-coordinate and Δx displacement from original position (plane of x=5 cm); (b) y-coordinate and Δy displacement from original position

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Figure 1

Stress-strain curves of bovine liver. Results of experiments, theoretical model, and FEM simulations obtained at different strain rates: (a) 0.0011 s−1, (b) 0.0088 s−1, and (c) 0.4167 s−1.

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Figure 2

Schematic model for simulating the needle insertion into a bovine liver

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Figure 3

PT error for different position and orientation resolutions: (a) RMSE and (b) maximum error

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Figure 4

The initial positions of the nodes lying at the needle tip versus needle insertion state at the PPT of 2.5 s: (a) x-coordinate and (b) y-coordinate



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