Ballesteros-Zebadúa P, García-Garduño O A, Galván de la Cruz O O, Arellano-Reynoso A, Lárraga-Gutiérrez J M, Celis M A
a Medical Physics Laboratory , National Institute of Neurology and Neurosurgery , Mexico.
b Radioneurosurgery Unit , National Institute of Neurology and Neurosurgery , Mexico.
Br J Neurosurg. 2016 Dec;30(6):606-610. doi: 10.3109/02688697.2016.1173188. Epub 2016 Apr 21.
To acknowledge the challenges and limitations of image-guided neurosurgery systems, we compared the application accuracy of two different image registration methods for one commercial system. (VectorVision, BrainLab, Germany).
We used an anthropomorphic head phantom for radiosurgery and a custom built add-on to simulate surgical targets inside the brain during an image-guided neurosurgery. We used two image registration methods, fiducial registration using attachable surface markers for computed tomography (CT) and surface registration using infrared laser face scanning. After simulation, we calculated the three-dimensional (3D) distance between the predicted position of a target, and its actual position using a registered pointer and an infrared camera. Deviations were measured for both superficial fiducial markers and internal surgical targets by five different users.
Deviations from the location of fiducial markers after each registration method were 2.15 ± 0.93 mm after CT surface marker registration and 1.25 ± 0.64 mm after infrared face scanner registration. The mean target registration errors were 2.95 ± 1.4 mm using fiducial registration and 2.90 ± 1.3 mm using surface registration. The largest deviations (6.2 mm) were found for the targets in the skull base and posterior cranial fossa. Fiducial deviations and target registration errors were statistically uncorrelated. The total application accuracy was 4.87 ± 0.97 mm after CT surface marker registration and 4.14 ± 0.64 mm after infrared face scanner registration.
Despite others have reported differences, we did not find significant variations between both registration methods for the target registration error, although application accuracy was slightly better after surface face registration. Superficial registration errors, but not the target registration error, can be routinely evaluated in the operating room. Since both errors were uncorrelated, surgeons may neglect the achievable accuracy of the procedure. The described method is recommended to assess application accuracy in the operating room.
为了解图像引导神经外科手术系统的挑战和局限性，我们比较了一款商业系统（德国BrainLab公司的VectorVision）两种不同图像配准方法的应用准确性。
我们使用了一个用于放射外科的仿真人头模型和一个定制的附加装置，以在图像引导神经外科手术期间模拟脑内的手术靶点。我们使用了两种图像配准方法，一种是使用用于计算机断层扫描（CT）的可附着表面标记物进行基准配准，另一种是使用红外激光面部扫描进行表面配准。模拟后，我们使用注册指针和红外摄像机计算靶点预测位置与其实际位置之间的三维（3D）距离。由五名不同的使用者测量浅表基准标记物和内部手术靶点的偏差。
CT表面标记物配准后，每种配准方法与基准标记物位置的偏差为2.15±0.93毫米，红外面部扫描仪配准后为1.25±0.64毫米。使用基准配准的平均靶点配准误差为2.95±1.4毫米，使用表面配准的为2.90±1.3毫米。在颅底和后颅窝的靶点中发现了最大偏差（6.2毫米）。基准偏差和靶点配准误差在统计学上不相关。CT表面标记物配准后的总应用准确性为4.87±0.97毫米，红外面部扫描仪配准后为4.14±0.64毫米。
尽管其他人报告了差异，但我们并未发现两种配准方法在靶点配准误差上有显著差异，尽管表面配准后的应用准确性略好。浅表配准误差而非靶点配准误差可在手术室中常规评估。由于两种误差不相关，外科医生可能会忽略该手术可达到的准确性。建议使用所述方法评估手术室中的应用准确性。
