Department of Oral and Maxillofacial Surgery, Albert-Ludwigs University Freiburg, Hugstetterstr. 55, 79106, Freiburg, Germany.
Department of Oral and Maxillofacial Surgery, Ludwig-Maximilians-University Munich, Lindwurmstr. 2a, 80137, Munich, Germany.
Int J Comput Assist Radiol Surg. 2018 Apr;13(4):519-529. doi: 10.1007/s11548-017-1674-6. Epub 2017 Oct 28.
Virtual reconstruction of large cranial defects is still a challenging task. The current reconstruction procedures depend on the surgeon's experience and skills in planning the reconstruction based on mirroring and manual adaptation. The aim of this study is to propose and evaluate a computer-based approach employing a statistical shape model (SSM) of the cranial vault.
An SSM was created based on 131 CT scans of pathologically unaffected adult crania. After segmentation, the resulting surface mesh of one patient was established as template and subsequently registered to the entire sample. Using the registered surface meshes, an SSM was generated capturing the shape variability of the cranial vault. The knowledge about this shape variation in healthy patients was used to estimate the missing parts. The accuracy of the reconstruction was evaluated by using 31 CT scans not included in the SSM. Both unilateral and bilateral bony defects were created on each skull. The reconstruction was performed using the current gold standard of mirroring the intact to the affected side, and the result was compared to the outcome of our proposed SSM-driven method. The accuracy of the reconstruction was determined by calculating the distances to the corresponding parts on the intact skull.
While unilateral defects could be reconstructed with both methods, the reconstruction of bilateral defects was, for obvious reasons, only possible employing the SSM-based method. Comparing all groups, the analysis shows a significantly higher precision of the SSM group, with a mean error of 0.47 mm compared to the mirroring group which exhibited a mean error of 1.13 mm. Reconstructions of bilateral defects yielded only slightly higher estimation errors than those of unilateral defects.
The presented computer-based approach using SSM is a precise and simple tool in the field of computer-assisted surgery. It helps to reconstruct large-size defects of the skull considering the natural asymmetry of the cranium and is not limited to unilateral defects.
大型颅缺损的虚拟重建仍然是一项具有挑战性的任务。目前的重建程序依赖于外科医生根据镜像和手动适配来规划重建的经验和技能。本研究旨在提出并评估一种基于颅骨统计形状模型（SSM）的计算机方法。
基于 131 例病理性未受影响的成人颅骨的 CT 扫描创建了 SSM。分割后，将一名患者的结果表面网格建立为模板，然后将其注册到整个样本中。使用注册的表面网格，生成了一个捕获颅骨穹窿形状可变性的 SSM。使用健康患者的这些形状变化知识来估计缺失的部分。通过使用未包含在 SSM 中的 31 个 CT 扫描来评估重建的准确性。在每个颅骨上创建单侧和双侧骨缺损。使用镜像完整颅骨到受影响侧的当前金标准进行重建，并将结果与我们提出的 SSM 驱动方法的结果进行比较。通过计算与完整颅骨上相应部位的距离来确定重建的准确性。
虽然两种方法都可以重建单侧缺损，但由于明显的原因，只有使用基于 SSM 的方法才能重建双侧缺损。对所有组进行比较，分析表明 SSM 组的精度明显更高，平均误差为 0.47 毫米，而镜像组的平均误差为 1.13 毫米。双侧缺损的重建仅比单侧缺损的重建产生略高的估计误差。
本文提出的基于 SSM 的计算机方法是计算机辅助手术领域中一种精确而简单的工具。它有助于在考虑颅骨自然不对称性的情况下重建大型颅骨缺损，并且不限于单侧缺损。
