Deformable Registration of the Inflated and Deflated Lung for Cone-Beam CT-Guided Thoracic Surgery

被引:1
|
作者
Uneri, Ali [1 ]
Nithiananthan, Sajendra [2 ]
Schafer, Sebastian [2 ]
Otake, Yoshito [1 ,2 ]
Stayman, J. Webster [2 ]
Kleinszig, Gerhard [3 ]
Sussman, Marc S. [4 ]
Taylor, Russell H. [1 ]
Prince, Jerry L. [5 ]
Siewerdsen, Jeffrey H. [1 ,2 ]
机构
[1] Johns Hopkins Univ, Dept Comp Sci, Baltimore, MD 21218 USA
[2] Johns Hopkins Univ, Dept Biomed Engn, Baltimore, MD 21218 USA
[3] Siemens Healthcare, Erlangen, Germany
[4] Johns Hopkins Bayview Med Ctr, Dept Surg, Baltimore, MD 21218 USA
[5] Johns Hopkins Univ, Dept Elect & Comp Engn, Baltimore, MD 21218 USA
来源
MEDICAL IMAGING 2012: IMAGE-GUIDED PROCEDURES, ROBOTIC INTERVENTIONS, AND MODELING | 2012年 / 8316卷
基金
美国国家卫生研究院;
关键词
CANCER; LOCALIZATION;
D O I
10.1117/12.911440
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
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 similar to 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.
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页数:8
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