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Proceedings Paper

Deformable registration of the inflated and deflated lung for cone-beam CT-guided thoracic surgery
Author(s): Ali Uneri; Sajendra Nithiananthan; Sebastian Schafer; Yoshito Otake; J. Webster Stayman; Gerhard Kleinszig; Marc S. Sussman; Russell H. Taylor; Jerry L. Prince; Jeffrey H. Siewerdsen
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Paper Abstract

Intraoperative cone-beam CT (CBCT) could offer an important advance to thoracic surgeons in directly localizing subpalpable nodules during surgery. An image-guidance system is under development using mobile C-arm CBCT to directly localize tumors in the OR, potentially reducing the cost and logistical burden of conventional preoperative localization and facilitating safer surgery by visualizing critical structures surrounding the surgical target (e.g., pulmonary artery, airways, etc.). To utilize the wealth of preoperative image/planning data and to guide targeting under conditions in which the tumor may not be directly visualized, a deformable registration approach has been developed that geometrically resolves images of the inflated (i.e., inhale or exhale) and deflated states of the lung. This novel technique employs a coarse model-driven approach using lung surface and bronchial airways for fast registration, followed by an image-driven registration using a variant of the Demons algorithm to improve target localization to within ~1 mm. Two approaches to model-driven registration are presented and compared - the first involving point correspondences on the surface of the deflated and inflated lung and the second a mesh evolution approach. Intensity variations (i.e., higher image intensity in the deflated lung) due to expulsion of air from the lungs are accounted for using an a priori lung density modification, and its improvement on the performance of the intensity-driven Demons algorithm is demonstrated. Preliminary results of the combined model-driven and intensity-driven registration process demonstrate accuracy consistent with requirements in minimally invasive thoracic surgery in both target localization and critical structure avoidance.

Paper Details

Date Published: 13 February 2012
PDF: 8 pages
Proc. SPIE 8316, Medical Imaging 2012: Image-Guided Procedures, Robotic Interventions, and Modeling, 831602 (13 February 2012); doi: 10.1117/12.911440
Show Author Affiliations
Ali Uneri, The Johns Hopkins Univ. (United States)
Sajendra Nithiananthan, The Johns Hopkins Univ. (United States)
Sebastian Schafer, The Johns Hopkins Univ. (United States)
Yoshito Otake, The Johns Hopkins Univ. (United States)
J. Webster Stayman, The Johns Hopkins Univ. (United States)
Gerhard Kleinszig, Siemens Healthcare (Germany)
Marc S. Sussman, The Johns Hopkins Bayview Medical Ctr. (United States)
Russell H. Taylor, The Johns Hopkins Outpatient Ctr. (United States)
Jerry L. Prince, The Johns Hopkins Univ. (United States)
Jeffrey H. Siewerdsen, The Johns Hopkins Univ. (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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