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Research Papers

3D Graphical Rendering of Localized Lumps and Arteries for Robotic Assisted MIS

[+] Author and Article Information
Masoud Kalantari

Department of Mechanical and Industrial Engineering of Concordia University and Centre for Intelligent Machines, McGill University, Montreal, QC, H3G 2W1, Canadam_kalan@encs.concordia.ca

Mohammadreza Ramezanifard

Department of Mechanical and Industrial Engineering, Concordia University, Montreal, QC, H3G 2W1, Canadaramezanifard@yahoo.com

Javad Dargahi

Department of Mechanical and Industrial Engineering, Concordia University, Montreal, QC, H3G 2W1, Canadadargahi@encs.concordia.ca

Jozsef Kövecses

Department of Mechanical Engineering and Centre for Intelligent Machines, McGill University, Montreal, QC, H3A 2K6, Canadajozsef.kovecses@mcgill.ca

J. Med. Devices 5(2), 021002 (May 02, 2011) (10 pages) doi:10.1115/1.4003736 History: Received December 10, 2010; Revised February 23, 2011; Published May 02, 2011; Online May 02, 2011

Detection of hard inclusions within soft tissue in robotic assisted minimally invasive surgery (MIS), also referred to as laparoscopic surgery, is of great importance, both in clinical and surgical applications. In clinical applications, surgeons need to detect and precisely identify the location and size of all growths, whether cancerous or benign, that are present within surrounding tissue in order to assess the extent and nature of any future treatment plan. In surgical applications, when any solid matter is being removed, it is important to avoid accidental injury to surrounding tissues and blood vessels since, were this to occur, it could then necessitate the need to resort to open surgery. The present study is aimed at developing a three-dimensional tactile display that provides palpation capability to any surgeon performing robotic assisted MIS. The information is collected from two force sensor/pressure matrices and processed with a new algorithm and graphically rendered. Consequently, the surgeon can determine the presence, location, and the size of any hidden superficial tumor/artery by grasping the target tissue in a quasi-dynamic way. The developed algorithm is presented, and the results for various configurations of embedded tumor/arteries inside the tissue are compared with those of the finite element analysis.

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Copyright © 2011 by American Society of Mechanical Engineers
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Figures

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Figure 1

Schematic view of the relation between components

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Figure 2

(a) View of the fabricated force sensor using Linqstat and its components. (b) Schematic view of the current flow geometry. (c) Photograph of the elastomers, lump, and artery. (d) Photograph of a sensor matrix.

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Figure 3

Locating the lump in one direction and its graphical rendering

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Figure 4

Two-dimensional graphical rendering of the characterized lump. (a) A lump located in a soft material with the upper and lower sensor arrays. (b) 2D intensity graph associated with the sensor array outputs. (c) The relationship between grasped object and intensity matrix. (d) A 7×7 matrix showing the location of the lump. (e) A 60×100 matrix providing better information on location and size of the lump

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Figure 5

(a) Three views of the upper and lower sensor matrices. (b) The output signals for the front view. (c) The output signals for the left view. (d) The output signals for the top view.

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Figure 6

A lump located at the front right of an elastomer sample and its 3D tactile image

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Figure 7

A typical fitting of Mooney–Rivlin model with two, three, and five parameters to the uniaxial compression data of the two elastomers: (a) B1 elastomer and (b) ICF elastomer

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Figure 8

Cross section of the FEM of the embedded lump and artery inside tissue. (a) Embedded lump inside the tissue, ICF elastomer, while applying 1 mm displacement to the upper and the lower jaws. (b) The embedded artery inside tissue, B1 elastomer, while having only 12 mm Hg pressures in the artery. (c) The embedded artery inside tissue, ICF elastomer, while applying 1 mm displacement to the upper and the lower jaws, and having 8 mm Hg pressure in the artery.

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Figure 9

(a) The pressure distribution on the upper surface of the tissue. (b) The pressure distribution on the lower surface of the tissue.

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Figure 10

Results of the 3D software for different configurations: (a) Two embedded lump inside the tissue. (b) Two embedded artery inside tissue. (c) One embedded artery inside tissue and the position of the upper and the lower sensor matrixes according to the position of the artery.

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Figure 11

Results of the 3D image: (a) Location inside the tissue. (b) Recreation of the two arteries in a 3D image.

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