基于重建髁突和颅底的 TMJ 的三维 CBCT 图像配准方法。
Three-dimensional CBCT images registration method for TMJ based on reconstructed condyle and skull base.
机构信息
1 Signal and image processing laboratory, School of Electronic Information Engineering, Beijing Jiao tong University , Beijing , China.
2 Department of Oral and Maxillofacial Radiology, Peking University, School and Hospital of Stomatology , Beijing , China.
出版信息
Dentomaxillofac Radiol. 2018 Jul;47(5):20170421. doi: 10.1259/dmfr.20170421. Epub 2018 May 2.
OBJECTIVES
A method was introduced for three-dimensional (3D) cone-beamCT (CBCT) images registration of temporomandibular joint (TMJ). This study aimed to provide quantitative and qualitative analysis of TMJ bone changes in two-dimensional (2D) and 3D and to provide the technique for computer-aided diagnosis of temporomandibular joint disorders in the future.
METHODS
10 TMJ samples of six patients were obtained from Peking University Hospital of Stomatology. Four of the six patients imaged bilateral TMJs and the other two patients only imaged unilateral TMJ. Each sample consisted of two images from the same TMJ taken at different times. First, condyle and skull base were segmented semi-automatically for 3D model reconstruction. Then the segmented condyle and skull base were registered separately. Registration process can be divided into two processes of rough registration and fine registration. Rough registration step was achieved by selecting corresponding points manually and initialized fine registration. Condyle and skull base were fine registered by minimizing mean square error of condyle (MSE) and skull base (MSE) respectively. Qualitative assessment of osseous component changes utilized 2D color-fused model and 3D surface-fused model and quantitative analyses the convergence of this method used the mean square error of the model (MSE). Independent repeated experiments were carried out to test the stability of our 3D registration method.
RESULTS
Sufficiently alignment was achieved. Osseous abnormality and morphology changes were displayed using fusion model. MSE of condylar registration and skull base registration declined 51.80% and 64.58% compared with that before registration. Quantitative analysis verified the stability of the method.
CONCLUSIONS
The proposed method completed 3D TMJ registration for different physiological structure. The result of this method was accurate, reproducible and not relied on the experience of operators.
目的
介绍一种用于颞下颌关节（TMJ）的三维（3D）锥形束 CT（CBCT）图像配准的方法。本研究旨在提供二维（2D）和 3D 中 TMJ 骨变化的定量和定性分析，并为未来的颞下颌关节疾病的计算机辅助诊断提供技术。
方法
从北京大学口腔医院获得了 6 名患者的 10 个 TMJ 样本。其中 4 名患者对双侧 TMJ 进行成像，另外 2 名患者仅对单侧 TMJ 进行成像。每个样本由来自同一 TMJ 的两次不同时间拍摄的两幅图像组成。首先，对髁突和颅底进行半自动分割以进行 3D 模型重建。然后分别对分割的髁突和颅底进行配准。配准过程可以分为粗配准和精配准两个过程。粗配准步骤通过手动选择对应点并初始化精细配准来实现。通过最小化髁突（MSE）和颅底（MSE）的均方误差来分别精细注册髁突和颅底。利用 2D 彩色融合模型和 3D 表面融合模型对骨性成分变化进行定性评估，并利用模型的均方误差（MSE）对该方法的收敛性进行定量分析。进行了独立的重复实验以测试我们的 3D 配准方法的稳定性。
结果
实现了充分的对齐。融合模型显示了骨性异常和形态变化。与配准前相比，髁突配准和颅底配准的均方误差分别下降了 51.80%和 64.58%。定量分析验证了该方法的稳定性。
结论
所提出的方法完成了不同生理结构的 3D TMJ 配准。该方法的结果准确、可重复，并且不依赖于操作人员的经验。
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