Reducing depth uncertainty in large surgical workspaces, with applications to veterinary medicine

Michel A. Audette; Ahmad Kolahi; Andinet Enquobahrie; Claudio Gatti; Kevin Cleary

doi:10.1117/12.843608

27 February 2010 Reducing depth uncertainty in large surgical workspaces, with applications to veterinary medicine

Michel A. Audette, Ahmad Kolahi, Andinet Enquobahrie, Claudio Gatti, Kevin Cleary

Proceedings Volume 7625, Medical Imaging 2010: Visualization, Image-Guided Procedures, and Modeling; 762525 (2010) https://doi.org/10.1117/12.843608
Event: SPIE Medical Imaging, 2010, San Diego, California, United States

Abstract

This paper presents on-going research that addresses uncertainty along the Z-axis in image-guided surgery, for applications to large surgical workspaces, including those found in veterinary medicine. Veterinary medicine lags human medicine in using image guidance, despite MR and CT data scanning of animals. The positional uncertainty of a surgical tracking device can be modeled as an octahedron with one long axis coinciding with the depth axis of the sensor, where the short axes are determined by pixel resolution and workspace dimensions. The further a 3D point is from this device, the more elongated is this long axis, and the greater the uncertainty along Z of this point's position, in relation to its components along X and Y. Moreover, for a triangulation-based tracker, its position error degrades with the square of distance. Our approach is to use two or more Micron Trackers to communicate with each other, and combine this feature with flexible positioning. Prior knowledge of the type of surgical procedure, and if applicable, the species of animal that determines the scale of the workspace, would allow the surgeon to pre-operatively configure the trackers in the OR for optimal accuracy. Our research also leverages the open-source Image-guided Surgery Toolkit (IGSTK).

Citation Download Citation

Michel A. Audette, Ahmad Kolahi, Andinet Enquobahrie, Claudio Gatti, and Kevin Cleary "Reducing depth uncertainty in large surgical workspaces, with applications to veterinary medicine", Proc. SPIE 7625, Medical Imaging 2010: Visualization, Image-Guided Procedures, and Modeling, 762525 (27 February 2010); https://doi.org/10.1117/12.843608

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