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Proceedings Paper

Geometric reconstruction using tracked ultrasound strain imaging
Author(s): Thomas S. Pheiffer; Amber L. Simpson; Janet E. Ondrake; Michael I. Miga
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Paper Abstract

The accurate identification of tumor margins during neurosurgery is a primary concern for the surgeon in order to maximize resection of malignant tissue while preserving normal function. The use of preoperative imaging for guidance is standard of care, but tumor margins are not always clear even when contrast agents are used, and so margins are often determined intraoperatively by visual and tactile feedback. Ultrasound strain imaging creates a quantitative representation of tissue stiffness which can be used in real-time. The information offered by strain imaging can be placed within a conventional image-guidance workflow by tracking the ultrasound probe and calibrating the image plane, which facilitates interpretation of the data by placing it within a common coordinate space with preoperative imaging. Tumor geometry in strain imaging is then directly comparable to the geometry in preoperative imaging. This paper presents a tracked ultrasound strain imaging system capable of co-registering with preoperative tomograms and also of reconstructing a 3D surface using the border of the strain lesion. In a preliminary study using four phantoms with subsurface tumors, tracked strain imaging was registered to preoperative image volumes and then tumor surfaces were reconstructed using contours extracted from strain image slices. The volumes of the phantom tumors reconstructed from tracked strain imaging were approximately between 1.5 to 2.4 cm3, which was similar to the CT volumes of 1.0 to 2.3 cm3. Future work will be done to robustly characterize the reconstruction accuracy of the system.

Paper Details

Date Published: 14 March 2013
PDF: 6 pages
Proc. SPIE 8671, Medical Imaging 2013: Image-Guided Procedures, Robotic Interventions, and Modeling, 86711E (14 March 2013); doi: 10.1117/12.2008045
Show Author Affiliations
Thomas S. Pheiffer, Vanderbilt Univ. (United States)
Amber L. Simpson, Vanderbilt Univ. (United States)
Janet E. Ondrake, Vanderbilt Univ. (United States)
Michael I. Miga, Vanderbilt Univ. (United States)
Vanderbilt Univ. Medical Ctr. (United States)


Published in SPIE Proceedings Vol. 8671:
Medical Imaging 2013: Image-Guided Procedures, Robotic Interventions, and Modeling
David R. Holmes; Ziv R. Yaniv, Editor(s)

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