Deformable Registration of the Inflated and Deflated Lung for Cone-Beam CT-Guided Thoracic Surgery.
Deformable Registration of the Inflated and Deflated Lung for Cone-Beam CT-Guided Thoracic Surgery.
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锥形束 CT 引导胸外科手术中膨胀和收缩肺的变形登记。
DOI：
10.1117/12.911440
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发表时间：
2012
期刊：
影响因子：
--
通讯作者：
Siewerdsen,JeffreyH
中科院分区：
文献类型：
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作者：
Uneri,Ali;Nithiananthan,Sajendra;Schafer,Sebastian;Otake,Yoshito;Stayman,JWebster;Kleinszig,Gerhard;Sussman,MarcS;Taylor,RussellH;Prince,JerryL;Siewerdsen,JeffreyH
术中锥形束 CT (CBCT) 可以为胸外科医生在手术期间直接定位可触及的结节提供重要的进步。正在开发一种图像引导系统，使用移动 C 臂 CBCT 直接定位手术室中的肿瘤，可能会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（例如肺动脉、气道等）来促进更安全的手术。为了利用丰富的术前图像/计划数据并在肿瘤无法直接可视化的情况下指导靶向，开发了一种可变形配准方法，该方法可以在几何上解析肺部充气（即吸气或呼气）和放气状态的图像。这项新技术采用粗模型驱动方法，利用肺表面和支气管气道进行快速配准，然后使用 Demons 算法的变体进行图像驱动配准，将目标定位改进到 ∼1 mm 以内。提出并比较了两种模型驱动配准方法——第一种涉及放气和充气肺表面上的点对应，第二种是网格进化方法。使用先验肺密度修改来解释由于从肺部排出空气而导致的强度变化（即，放气的肺部中的图像强度较高），并证明了其对强度驱动恶魔算法性能的改进。模型驱动和强度驱动相结合的配准过程的初步结果表明，其准确性符合微创胸外科手术在目标定位和关键结构避免方面的要求。
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.