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Proceedings Paper

System for robot-assisted real-time laparoscopic ultrasound elastography
Author(s): Seth Billings; Nishikant Deshmukh; Hyun Jae Kang; Russell Taylor; Emad M. Boctor
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Paper Abstract

Surgical robots provide many advantages for surgery, including minimal invasiveness, precise motion, high dexterity, and crisp stereovision. One limitation of current robotic procedures, compared to open surgery, is the loss of haptic information for such purposes as palpation, which can be very important in minimally invasive tumor resection. Numerous studies have reported the use of real-time ultrasound elastography, in conjunction with conventional B-mode ultrasound, to differentiate malignant from benign lesions. Several groups (including our own) have reported integration of ultrasound with the da Vinci robot, and ultrasound elastography is a very promising image guidance method for robotassisted procedures that will further enable the role of robots in interventions where precise knowledge of sub-surface anatomical features is crucial. We present a novel robot-assisted real-time ultrasound elastography system for minimally invasive robot-assisted interventions. Our system combines a da Vinci surgical robot with a non-clinical experimental software interface, a robotically articulated laparoscopic ultrasound probe, and our GPU-based elastography system. Elasticity and B-mode ultrasound images are displayed as picture-in-picture overlays in the da Vinci console. Our system minimizes dependence on human performance factors by incorporating computer-assisted motion control that automatically generates the tissue palpation required for elastography imaging, while leaving high-level control in the hands of the user. In addition to ensuring consistent strain imaging, the elastography assistance mode avoids the cognitive burden of tedious manual palpation. Preliminary tests of the system with an elasticity phantom demonstrate the ability to differentiate simulated lesions of varied stiffness and to clearly delineate lesion boundaries.

Paper Details

Date Published: 17 February 2012
PDF: 8 pages
Proc. SPIE 8316, Medical Imaging 2012: Image-Guided Procedures, Robotic Interventions, and Modeling, 83161W (17 February 2012); doi: 10.1117/12.911086
Show Author Affiliations
Seth Billings, The Johns Hopkins Univ. (United States)
Nishikant Deshmukh, The Johns Hopkins Univ. (United States)
Hyun Jae Kang, The Johns Hopkins Univ. (United States)
Russell Taylor, The Johns Hopkins Univ. (United States)
Emad M. Boctor, The Johns Hopkins Univ. (United States)
Johns Hopkins Medical Institutions (United States)


Published in SPIE Proceedings Vol. 8316:
Medical Imaging 2012: Image-Guided Procedures, Robotic Interventions, and Modeling
David R. Holmes; Kenneth H. Wong, Editor(s)

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