Uneri Ali, Nithiananthan Sajendra, Schafer Sebastian, Otake Yoshito, Stayman J Webster, Kleinszig Gerhard, Sussman Marc S, Taylor Russell H, Prince Jerry L, Siewerdsen Jeffrey H
Dept. of Computer Science, Johns Hopkins Univ., Baltimore, MD USA.
Dept. of Biomedical Engineering, Johns Hopkins Univ., Baltimore, MD USA.
Proc SPIE Int Soc Opt Eng. 2012 Feb 4;8316. doi: 10.1117/12.911440.
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.
术中锥形束CT(CBCT)可为胸外科医生在手术中直接定位触诊不到的结节提供重要进展。一种图像引导系统正在研发中,该系统利用移动C形臂CBCT在手术室中直接定位肿瘤,有可能降低传统术前定位的成本和后勤负担,并通过可视化手术目标周围的关键结构(如肺动脉、气道等)来促进更安全的手术。为了利用丰富的术前图像/规划数据,并在肿瘤可能无法直接可视化的情况下指导靶向定位,已开发出一种可变形配准方法,该方法从几何上解析肺膨胀(即吸气或呼气)和收缩状态的图像。这项新技术采用一种基于粗糙模型驱动的方法,利用肺表面和支气管气道进行快速配准,随后采用基于图像驱动的配准,使用一种改进的戴蒙斯算法将目标定位精度提高到约1毫米以内。本文介绍并比较了两种模型驱动配准方法——第一种涉及收缩和膨胀肺表面的点对应关系,第二种是网格演化方法。利用先验肺密度修正来考虑由于肺部空气排出导致的强度变化(即收缩肺中图像强度较高),并证明了其对强度驱动的戴蒙斯算法性能的改善。模型驱动和强度驱动联合配准过程的初步结果表明,在目标定位和关键结构避让方面的精度符合微创胸外科手术的要求。