Research Papers

Modeling and Simulation of Flexible Needle Insertion Into Soft Tissue Using Modified Local Constraints

[+] Author and Article Information
Dedong Gao

The State Key Laboratory of Fluid Power
and Mechatronic Systems,
Zhejiang University,
Hangzhou 310027, China;
School of Mechanical Engineering,
Qinghai University,
Xining 810016, China
e-mail: gaodd@zju.edu.cn

Yong Lei

The State Key Laboratory of Fluid Power
and Mechatronic Systems,
Zhejiang University,
Hangzhou 310027, China
e-mail: ylei@zju.edu.cn

Bin Lian

The State Key Laboratory of Fluid Power
and Mechatronic Systems,
Zhejiang University,
Hangzhou 310027, China

Bin Yao

The State Key Laboratory of Fluid Power
and Mechatronic Systems,
Zhejiang University,
Hangzhou 310027, China;
School of Mechanical Engineering,
Purdue University,
West Lafayette, IN 47907
e-mail: byao@zju.edu.cn

1Corresponding author.

Manuscript received December 10, 2015; final manuscript received June 24, 2016; published online August 8, 2016. Assoc. Editor: Yong Huang.

J. Manuf. Sci. Eng 138(12), 121012 (Aug 08, 2016) (10 pages) Paper No: MANU-15-1651; doi: 10.1115/1.4034134 History: Received December 10, 2015; Revised June 24, 2016

Needle insertion is a widely used medical procedure in various minimally invasive surgeries. The estimation of the coupled needle deflection and tissue deformation during the needle insertion procedure is crucial to the success of the surgery. In this work, a novel needle deflection–tissue deformation coupling model is proposed for flexible needle insertion into soft tissue. Based on the assumption that the needle deflection is small comparing to the length of the insertion, the needle–tissue interaction model is developed based on the modified local constraint method, where the interactive forces between the needle and the tissue are balanced through integration of needle–force and tissue–force relationships. A testbed is constructed and the experiments are designed to validate the proposed method using artificial phantom with markers. Based on the experimental analysis, the cutting and friction forces are separated from the force–time curves and used as the inputs into the proposed model. The trajectories of the markers inside the soft tissue are recorded by a CCD camera to compare with the simulation trajectories. The errors between the experimental and simulation trajectories are less than 0.8 mm. The results demonstrate that the proposed method is effective to model the needle insertion procedure.

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Fig. 1

The cantilever beam model of the needle

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Fig. 2

The four processes of the needle insertion. (a) Process 1, the needle touches the surface of tissue and does not penetrate into the tissue. (b) Process 2, the needle penetrates into the tissue at a certain depth. (c) Process 3, the needle holds its position relative to the tissue. (d) Process 4, the needle is retreated from the tissue.

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Fig. 3

Illustration of typical force–time curves of needle insertion

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Fig. 4

The spring model in radial direction. The point in the middle denotes the contact point of tissue and needle.

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Fig. 5

The relationship between force and displacement in different state

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Fig. 6

The schematic diagram of positional relationship between needle and tissue's surface

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Fig. 7

Diagram for the strengthened stiffness of needle and tissue, “Δ” denotes the needle node point and “○” denotes the tissue node point

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Fig. 8

Schematic plot of track. Δ denotes the needle nodes, they are also the intersected points of the needle and tissue elements. “●” denotes the tissue nodes who is near the needle.

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Fig. 9

General process of needle insertion, “○” denotes the tissue node whose element is pierced by the needle, “Δ” denotes the tissue node whose element is fixed

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Fig. 10

Algorithm of needle–tissue interactive simulation

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Fig. 11

Experimental setup for needle insertion, including two translational stages, a CCD camera, a force/torque sensor, a PVA-H phantom, a percutaneous transhepatic cholangiogram needle, and a light-emitting diode light source. Left: experimental setup. Right: diagram for imaging acquisition.

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Fig. 12

Force–time curves for six different PVA-H phantoms when insertion at the same position

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Fig. 13

Locations of the markers inside the phantom

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Fig. 14

A snapshot of the simulation output, and “ * ” points are the targets

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Fig. 15

Picture of the phantom with markers embedded

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Fig. 16

The comparison of the trajectory of target 1 (left) and 3 (right), “▷” denotes the simulation results, “*” denotes the experimental results

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Fig. 17

The comparison of the trajectories of targets 5 (left) and 10 (right)

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Fig. 18

The comparison of the trajectory of target 8 (left) and 9 (right)




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