Research Papers

Design and Evaluation of a Robotic Device for Automated Tail Vein Cannulations in Rodent Models

[+] Author and Article Information
Alex Fromholtz

Department of Biomedical Engineering,
Rutgers University,
Piscataway, NJ 08854
e-mail: afromholtz@gmail.com

Max L. Balter

Department of Biomedical Engineering,
Rutgers University,
Piscataway, NJ 08854
e-mail: balterm53@gmail.com

Alvin I. Chen

Department of Biomedical Engineering,
Rutgers University,
Piscataway, NJ 08854
e-mail: alv6688@gmail.com

Josh M. Leipheimer

Department of Biomedical Engineering,
Rutgers University,
Piscataway, NJ 08854
e-mail: joshleiph@gmail.com

Anil Shrirao

Department of Biomedical Engineering,
Rutgers University,
Piscataway, NJ 08854
e-mail: anilshrirao@gmail.com

Timothy J. Maguire

Department of Biomedical Engineering,
Rutgers University,
Piscataway, NJ 08854
e-mail: timjm@rutgers.edu

Martin L. Yarmush

Paul and Mary Monroe Distinguished Professor
Department of Biomedical Engineering,
Rutgers University,
Piscataway, NJ 08854
e-mail: yarmush@rci.rutgers.edu

1Corresponding author.

Manuscript received April 13, 2017; final manuscript received September 14, 2017; published online October 16, 2017. Assoc. Editor: Carl Nelson.

J. Med. Devices 11(4), 041008 (Oct 16, 2017) (7 pages) Paper No: MED-17-1204; doi: 10.1115/1.4038011 History: Received April 13, 2017; Revised September 14, 2017

Preclinical testing in rodent models is a ubiquitous part of modern biomedical research and commonly involves accessing the venous bloodstream for blood sampling and drug delivery. Manual tail vein cannulation is a time-consuming process and requires significant skill and training, particularly since improperly inserted needles can affect the experimental results and study outcomes. In this paper, we present a miniaturized, robotic medical device for automated, image-guided tail vein cannulations in rodent models. The device is composed of an actuated three degrees-of-freedom (DOFs) needle manipulator, three-dimensional (3D) near-infrared (NIR) stereo cameras, and an animal holding platform. Evaluating the system through a series of workspace simulations and free-space positioning tests, the device exhibited a sufficient work volume for the needle insertion task and submillimeter accuracy over the calibration targets. The results indicate that the device is capable of cannulating tail veins in rodent models as small as 0.3 mm in diameter, the smallest diameter vein required to target.

Copyright © 2017 by ASME
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Grahic Jump Location
Fig. 1

Design of the robotic device for automated tail vein insertions in rodent models: (a) isometric view illustrating the needle manipulator assembly, imaging unit, rodent model, and animal holding platform and (b) side view of the device highlighting the imaging unit, LED array and motor controllers hidden under the holding platform

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Fig. 2

Depiction of the three degrees of motion. (a) θFG and its relative coordinate frame on the holding platform. The axis of rotation is denoted as AoR. Additional components such as the light-emitting diode (LED) array and the tail clamp arm are also labeled. (b) Side and (c) front view of ZNM. This DOF is achieved using a rack and pinion mechanism with a helical pinion at 2 deg to align the motors with the insertion angle. (d) Dins and its chain of mechanisms linked together, which directly controls the needle insertion, and allows the needle path to remain parallel to the drive motor axle. Inset shows the slot design for finer control over mechanism backlash.

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Fig. 3

Key components of the imaging system: (a) cross-sectional illustration of a rodent tail anatomy, (b) front (i) and side (ii) views of the imaging unit, with labeled camera angles, and (c) NIR tail vein imaging and segmentation. Image acquired under visible light (i), corresponding image acquired with the NIR imaging system using the transillumination method (ii), and tail vein segmentation applied to the NIR image (iii).

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Fig. 4

Depiction of the actual (left) versus desired (right) workspace for the needle insertion task

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Fig. 5

Free-space needle positioning tests: (a) the experimental setup using the cylindrical calibration rig and a secondary set of stereo cameras situated above the device as labeled in the figure, (b) stereo images from the left and right cameras during testing, and (c) images magnified 5×, and the needle tip manually selected to compute the 3D position

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Fig. 6

Maps of mean absolute error interpolated over 25 calibration targets in (a) x, (b) y, (c) z, and (d) magnitude averaged across ten trials (units in millimeter). Origin corresponds to top left circle in calibration grid, and axes correspond to the global device coordinate frame.

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Fig. 7

Low-level motor errors during position testing over 30 s interval, showing desired (solid line) versus actual (dotted line) signals for each DOF: (a) θFG, (b) ZNM, and (c) Dins



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