
Proceedings Paper
High-performance C-arm cone-beam CT guidance of thoracic surgeryFormat | Member Price | Non-Member Price |
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Paper Abstract
Localizing sub-palpable nodules in minimally invasive video-assisted thoracic surgery (VATS) presents a significant
challenge. To overcome inherent problems of preoperative nodule tagging using CT fluoroscopic guidance, an
intraoperative C-arm cone-beam CT (CBCT) image-guidance system has been developed for direct localization of
subpalpable tumors in the OR, including real-time tracking of surgical tools (including thoracoscope), and video-CBCT
registration for augmentation of the thoracoscopic scene. Acquisition protocols for nodule visibility in the inflated and
deflated lung were delineated in phantom and animal/cadaver studies. Motion compensated reconstruction was
implemented to account for motion induced by the ventilated contralateral lung. Experience in CBCT-guided targeting of
simulated lung nodules included phantoms, porcine models, and cadavers. Phantom studies defined low-dose acquisition
protocols providing contrast-to-noise ratio sufficient for lung nodule visualization, confirmed in porcine specimens with
simulated nodules (3-6mm diameter PE spheres, ~100-150HU contrast, 2.1mGy). Nodule visibility in CBCT of the
collapsed lung, with reduced contrast according to air volume retention, was more challenging, but initial studies
confirmed visibility using scan protocols at slightly increased dose (~4.6-11.1mGy). Motion compensated reconstruction
employing a 4D deformation map in the backprojection process reduced artifacts associated with motion blur.
Augmentation of thoracoscopic video with renderings of the target and critical structures (e.g., pulmonary artery) showed
geometric accuracy consistent with camera calibration and the tracking system (2.4mm registration error). Initial results
suggest a potentially valuable role for CBCT guidance in VATS, improving precision in minimally invasive, lungconserving
surgeries, avoid critical structures, obviate the burdens of preoperative localization, and improve patient
safety.
Paper Details
Date Published: 17 February 2012
PDF: 13 pages
Proc. SPIE 8316, Medical Imaging 2012: Image-Guided Procedures, Robotic Interventions, and Modeling, 83161I (17 February 2012); doi: 10.1117/12.911811
Published in SPIE Proceedings Vol. 8316:
Medical Imaging 2012: Image-Guided Procedures, Robotic Interventions, and Modeling
David R. Holmes III; Kenneth H. Wong, Editor(s)
PDF: 13 pages
Proc. SPIE 8316, Medical Imaging 2012: Image-Guided Procedures, Robotic Interventions, and Modeling, 83161I (17 February 2012); doi: 10.1117/12.911811
Show Author Affiliations
Sebastian Schafer, The Johns Hopkins Univ. (United States)
Yoshito Otake, The Johns Hopkins Univ. (United States)
Ali Uneri, The Johns Hopkins Univ. (United States)
Daniel J. Mirota, The Johns Hopkins Univ. (United States)
Sajendra Nithiananthan, The Johns Hopkins Univ. (United States)
J. Webster Stayman, The Johns Hopkins Univ. (United States)
Yoshito Otake, The Johns Hopkins Univ. (United States)
Ali Uneri, The Johns Hopkins Univ. (United States)
Daniel J. Mirota, The Johns Hopkins Univ. (United States)
Sajendra Nithiananthan, The Johns Hopkins Univ. (United States)
J. Webster Stayman, The Johns Hopkins Univ. (United States)
Wojciech Zbijewski, The Johns Hopkins Univ. (United States)
Gerhard Kleinszig, Siemens Healthcare (Germany)
Rainer Graumann, Siemens Healthcare (Germany)
Marc Sussman, Johns Hopkins Medical Institute (United States)
Jeffrey H. Siewerdsen, The Johns Hopkins Univ. (United States)
Gerhard Kleinszig, Siemens Healthcare (Germany)
Rainer Graumann, Siemens Healthcare (Germany)
Marc Sussman, Johns Hopkins Medical Institute (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 III; Kenneth H. Wong, Editor(s)
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