Research Papers

Development of an Automated Steering Mechanism for Bladder Urothelium Surveillance

[+] Author and Article Information
W. Jong Yoon

Department of Mechanical Engineering, University of Washington, Seattle, WA 98195wjyoon@u.washington.edu

Sangtae Park

Section of Urology, Department of Surgery, Pritzker School of Medicine, University of Chicago, Chicago, IL 60637

Per G. Reinhall, Eric J. Seibel

Department of Mechanical Engineering, University of Washington, Seattle, WA 98195

J. Med. Devices 3(1), 011004 (Jan 13, 2009) (9 pages) doi:10.1115/1.3054381 History: Received June 27, 2008; Revised November 11, 2008; Published January 13, 2009

Given the advantages of cystoscopic exams compared with other procedures available for bladder surveillance, it would be beneficial to develop an improved automated cystoscope. We develop and propose an active programmable remote steering mechanism and an efficient motion sequence for bladder cancer detection and postoperative surveillance. The continuous and optimal path of the imaging probe can enable a medical practitioner to readily ensure that images are produced for the entire surface of the bladder in a controlled and uniform manner. Shape memory alloy (SMA) based segmented actuators disposed adjacent to the distal end of the imaging probe are selectively activated to bend the shaft to assist in positioning and orienting the imaging probe at a plurality of points selected to image all the interior of the distended bladder volume. The bending arc, insertion depth, and rotational position of the imaging probe are automatically controlled based on patient-specific data. The initial prototype is tested on a 3D plastic phantom bladder, which is used as a proof-of-concept in vitro model and an electromagnetic motion tracker. The 3D tracked tip trajectory results ensure that the motion sequencing program and the steering mechanism efficiently move the image probe to scan the entire inner tissue layer of the bladder. The compared experimental results shows 5.1% tip positioning error to the designed trajectory given by the simulation tool. The authors believe that further development of this concept will help guarantee that a tumor or other characteristic of the bladder surface is not overlooked during the automated cystoscopic procedure due to a failure to image it.

Copyright © 2009 by American Society of Mechanical Engineers
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Figure 1

Diagram of three different bending motions with the same bend radius, (a) undeflected state, (b)–(d) one, two, and three distal segments are deflected with the same bend radius r, respectively

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Figure 2

Definition of segment coordinates and trigonometric relationship between bending angle/radius and deflection of a multisegmented bending mechanism (for clarity, only two segments in Module No. 2 are activated out of the two module-two segment structure)

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Figure 3

One exemplary approach for manipulating the position and orientation of the imaging probe in a sphere-assumed bladder using a shaft arc, insertion depth, and rotation (black portion in the shaft stands for rigid shaft). Shaded areas illustrate the portion being imaged.

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Figure 4

Tip location decision making flowchart

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Figure 5

A schematic of an exemplary area-of-interest and orthogonality check. Solid squares represent the reference data points, which describe bladder surface, and the shaded area denotes the area of interest.

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Figure 6

Shaft building process

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Figure 7

(a) Original CT image; (b) edge detected, white dots, and calculated coordinates of edges by LABVIEW ; (c) point cloud; (d) mesh object; (e) 3D printed bladder model with ureters and urethra tubes attached; and (f) the reference frame for the position and orientation of the imaging tip and CT images

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Figure 8

Test setup with PDMS phantom bladder

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Figure 9

Schematic of the prototype and test setup (only one module/half circuit shown for clarity)

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Figure 10

Simulation results of the tip locator algorithm: (a) eleven tip locations (red circles) with bladder contour (black solid dots) are shown as the optimal scan trajectory. Radial lines (green) from the probe tip represent the imaging area, while a 45 mm radius circle (blue) is shown for reference. (b) Series of shaft manipulation procedural showing the expected tip coordinates/orientation.

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Figure 11

(a) Captured image of bend radius measurement in a customized LABVIEW program, and (b) captured images of four exemplary shaft bending motions

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Figure 12

Exemplary five simulated probe tip locations verified with experimental measurements (a) in graph, (b) detailed comparison data (e.g., S_2101 versus E_2101: Simulated and experimental base displacements at 21 mm with zero and one segment of modules 1 and 2 being activated, respectively)

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Figure 13

3D tracked tip locations of the prototype




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