Nithiananthan S, Mirota D, Uneri A, Schafer S, Otake Y, Stayman J W, Siewerdsen J H
Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205.
Department of Computer Science, Johns Hopkins University, Baltimore MD 21218.
Proc SPIE Int Soc Opt Eng. 2011 Feb;7964. doi: 10.1117/12.878258. Epub 2011 Mar 1.
The ability to perform fast, accurate, deformable registration with intraoperative images featuring surgical excisions was investigated for use in cone-beam CT (CBCT) guided head and neck surgery. Existing deformable registration methods generally fail to account for tissue excised between image acquisitions and typically simply "move" voxels within the images with no ability to account for tissue that is removed (or introduced) between scans. We have thus developed an approach in which an extra dimension is added during the registration process to act as a sink for voxels removed during the course of the procedure. A series of cadaveric images acquired using a prototype CBCT-capable C-arm were used to model tissue deformation and excision occurring during a surgical procedure, and the ability of deformable registration to correctly account for anatomical changes under these conditions was investigated. Using a previously developed version of the Demons deformable registration algorithm, we identify the difficulties that traditional registration algorithms encounter when faced with excised tissue and present a modified version of the algorithm better suited for use in intraoperative image-guided procedures. Studies were performed for different deformation and tissue excision tasks, and registration performance was quantified in terms of the ability to accurately account for tissue excision while avoiding spurious deformations arising around the excision.
研究了在锥束CT（CBCT）引导的头颈外科手术中，利用包含手术切除的术中图像进行快速、准确、可变形配准的能力。现有的可变形配准方法通常无法考虑图像采集之间切除的组织，并且通常只是简单地“移动”图像内的体素，而无法考虑扫描之间移除（或引入）的组织。因此，我们开发了一种方法，即在配准过程中增加一个额外的维度，作为手术过程中移除的体素的汇聚点。使用能够进行CBCT的原型C形臂获取的一系列尸体图像，对手术过程中发生的组织变形和切除进行建模，并研究了在这些条件下可变形配准正确考虑解剖学变化的能力。使用先前开发的Demons可变形配准算法版本，我们识别了传统配准算法在面对切除组织时遇到的困难，并提出了一个更适合术中图像引导手术的算法修改版本。针对不同的变形和组织切除任务进行了研究，并根据准确考虑组织切除同时避免切除周围出现虚假变形的能力对配准性能进行了量化。
