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Research Papers

Graphic Processing Units (GPUs)-Based Haptic Simulator for Dental Implant Surgery

[+] Author and Article Information
Fei Zheng

e-mail: zhengfei@nus.edu.sg

Wen Feng Lu

e-mail: mpelwf@nus.edu.sg

Yoke San Wong

e-mail: mpewys@nus.edu.sg
Department of Mechanical Engineering,
National University of Singapore,
9 Engineering Drive 1,
Block EA,
Singapore 117576, Singapore

Kelvin Weng Chiong Foong

Faculty of Dentistry,
National University of Singapore,
21 Lower Kent Ridge Road,
Singapore 119083, Singapore
e-mail: kelvinfoong@nuhs.edu.sg

Contributed by the Computers and Information Division of ASME for publication in the JOURNAL OF COMPUTERS AND INFORMATION DIVISION IN ENGINEERING. Manuscript received November 6, 2012; final manuscript received June 26, 2013; published online September 12, 2013. Assoc. Editor: Krishnan Suresh.

J. Comput. Inf. Sci. Eng 13(4), 041005 (Sep 12, 2013) (9 pages) Paper No: JCISE-12-1203; doi: 10.1115/1.4024972 History: Received November 06, 2012; Revised June 26, 2013

This paper presents a haptics-based training simulator for dental implant surgery. Most of the previously developed dental simulators are targeted for exploring and drilling purpose only. The penalty-based contact force models with spherical-shaped dental tools are often adopted for simplicity and computational efficiency. In contrast, our simulator is equipped with a more precise force model adapted from the Voxmap-PointShell (VPS) method to capture the essential features of the drilling procedure, with no limitations on drill shape. In addition, a real-time torque model is proposed to simulate the torque resistance in the implant insertion procedure, based on patient-specific tissue properties and implant geometry. To achieve better anatomical accuracy, our oral model is reconstructed from cone beam computed tomography (CBCT) images with a voxel-based method. To enhance the real-time response, the parallel computing power of GPUs is exploited through extra efforts in data structure design, algorithms parallelization, and graphic memory utilization. Results show that the developed system can produce appropriate force feedback at different tissue layers during pilot drilling and can create proper resistance torque responses during implant insertion.

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References

Figures

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Fig. 2

A voxel cell and its nodes

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Fig. 3

Illustration of force model

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State transitions of torque modes

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Fig. 5

Illustration of torque model

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Fig. 6

Flowchart of the force computation kernel

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Fig. 7

The top-down collision detection algorithm

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Fig. 8

Force integration based on reduction tree

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Fig. 9

Graphic rendering results: (a) ROI selection; (b) real-time interaction during drilling; (c) penetration of the maxillary sinus; and (d) real-time interaction during implant insertion

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Fig. 10

Haptic rendering results of drilling forces

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Fig. 11

Haptic rendering results of resistance torques

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Fig. 12

Performance comparison between CPU and GPU

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