An Expert-Supervised Registration Method for Multiparameter Description of the Knee Joint Using Serial Imaging

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.