Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, 100084, China.
Department of Oral and Maxillofacial Surgery; National Engineering Laboratory for Digital and Material Technology of Stomatology; Beijing Key Laboratory of Digital Stomatology; National Clinical Research Center for Oral Diseases, Peking University School and Hospital of Stomatology, Beijing, 100081, China.
Int J Comput Assist Radiol Surg. 2022 Jun;17(6):1155-1165. doi: 10.1007/s11548-022-02626-y. Epub 2022 Apr 29.
In craniomaxillofacial (CMF) surgery planning, a preoperative reconstruction of the CMF reference model is crucial for surgical restoration, especially the reconstruction of bilateral defects. Current reconstruction algorithms mainly generate reference models from the image analysis aspect, however, clinical indicators of the CMF reference model mostly consider the distribution of anatomical landmarks. Generating a reference model with optimal clinical evaluation helps promote the feasibility of an algorithm.
We first build a dataset with 100 normal skull models and then calculate a statistical shape model (SSM) and the distribution of normal cephalometric values, which indicate the statistical features of a population. To further generate personalized reference models, we apply non-rigid registration to align the SSM with the defect skull model. An evaluation standard to select the optimal reference model considers both global performance and anatomical evaluation. Moreover, we develop a landmark detection network to improve the automatic level of the algorithm.
The proposed method performs better than methods including Iterative Closest Point and SSM. From a global evaluation aspect, the results show that the RMSE between the reference model and the ground truth is [Formula: see text] mm, the percentage of vertices with error below 2 mm is [Formula: see text]% and the average faces distance is [Formula: see text] mm (better than the state-of-the-art method). From the anatomical evaluation aspect, the target registration error between the landmark pairs is [Formula: see text] mm. In addition, the clinical application confirms that the reference model can meet clinical requirements.
To the best of our knowledge, we propose the first CMF reconstruction method considering the global performance of reconstruction and anatomically local evaluation from clinical experience. Simulated experiments and clinical cases prove the general applicability and strength of the method.
在颅面（CMF）手术规划中，术前重建 CMF 参考模型对于手术修复至关重要，特别是对于双侧缺损的重建。目前的重建算法主要从图像分析方面生成参考模型，然而，CMF 参考模型的临床指标主要考虑解剖标志的分布。生成具有最佳临床评估的参考模型有助于提高算法的可行性。
我们首先构建了一个包含 100 个正常颅骨模型的数据集，然后计算统计形状模型（SSM）和正常头影测量值的分布，这表示了人群的统计特征。为了进一步生成个性化的参考模型，我们应用非刚性配准将 SSM 与缺损颅骨模型对齐。选择最佳参考模型的评估标准同时考虑全局性能和解剖评估。此外，我们开发了一个地标检测网络来提高算法的自动化水平。
所提出的方法优于包括迭代最近点和 SSM 在内的方法。从全局评估方面来看，结果表明参考模型与真实值之间的均方根误差为[Formula: see text]mm，误差小于 2mm 的顶点百分比为[Formula: see text]%，平均面距离为[Formula: see text]mm（优于最先进的方法）。从解剖评估方面来看，地标对之间的目标配准误差为[Formula: see text]mm。此外，临床应用证实了参考模型可以满足临床需求。
据我们所知，我们提出了第一个从全局重建性能和临床经验的解剖局部评估角度考虑的 CMF 重建方法。模拟实验和临床案例证明了该方法的普遍适用性和强度。
