Babel Hugo, Omoumi Patrick, Cosendey Killian, Stanovici Julien, Cadas Hugues, Jolles Brigitte M, Favre Julien
Swiss BioMotion Lab, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), CH-1011 Lausanne, Switzerland.
Service of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), CH-1011 Lausanne, Switzerland.
J Clin Med. 2022 Jan 22;11(3):548. doi: 10.3390/jcm11030548.
As knee osteoarthritis is a disease of the entire joint, our pathophysiological understanding could be improved by the characterization of the relationships among the knee components. Diverse quantitative parameters can be characterized using magnetic resonance imaging (MRI) and computed tomography (CT). However, a lack of methods for the coordinated measurement of multiple parameters hinders global analyses. This study aimed to design an expert-supervised registration method to facilitate multiparameter description using complementary image sets obtained by serial imaging. The method is based on three-dimensional tissue models positioned in the image sets of interest using manually placed attraction points. Two datasets, with 10 knees CT-scanned twice and 10 knees imaged by CT and MRI were used to assess the method when registering the distal femur and proximal tibia. The median interoperator registration errors, quantified using the mean absolute distance and Dice index, were ≤0.45 mm and ≥0.96 unit, respectively. These values differed by less than 0.1 mm and 0.005 units compared to the errors obtained with gold standard methods. In conclusion, an expert-supervised registration method was introduced. Its capacity to register the distal femur and proximal tibia supports further developments for multiparameter description of healthy and osteoarthritic knee joints, among other applications.
由于膝关节骨关节炎是一种累及整个关节的疾病，通过描述膝关节各组成部分之间的关系，我们对其病理生理学的理解可能会得到改善。利用磁共振成像（MRI）和计算机断层扫描（CT）可以对多种定量参数进行表征。然而，缺乏对多个参数进行协同测量的方法阻碍了整体分析。本研究旨在设计一种专家监督的配准方法，以便利用通过序列成像获得的互补图像集进行多参数描述。该方法基于使用手动放置的吸引点定位在感兴趣图像集中的三维组织模型。使用两个数据集评估该方法在对股骨远端和胫骨近端进行配准时的性能，其中一个数据集包含10个膝关节的两次CT扫描，另一个数据集包含10个膝关节的CT和MRI成像。使用平均绝对距离和骰子系数量化的操作者间配准误差中位数分别≤0.45毫米和≥0.96单位。与使用金标准方法获得的误差相比，这些值的差异小于0.1毫米和0.005单位。总之，引入了一种专家监督的配准方法。其对股骨远端和胫骨近端进行配准的能力为健康和骨关节炎膝关节的多参数描述以及其他应用的进一步发展提供了支持。
