Abstract
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.
Highlights
Knee osteoarthritis is a painful, incapacitating joint disease affecting the quality of life of hundreds of millions of people worldwide [1,2,3]
This study aimed to develop an expert-supervised registration method for serial imaging based on three-dimensional tissue models and attraction points
The method proposed in this study requires at least one set of images for the segmentation of the tissue of interest and the creation of its three-dimensional mesh model, and one or more other sets of images where the three-dimensional model is imported based on attraction points placed manually
Summary
Knee osteoarthritis is a painful, incapacitating joint disease affecting the quality of life of hundreds of millions of people worldwide [1,2,3]. Consistency among the regions of interest is not guaranteed, the benefit of having complementary acquisition protocols allowing the identification of different tissues is underused, and processing time might be squandered by the replication of some operations with each image set [14,15,16,17,18] This methodology works for analyses based on large regions of interest [11,19,20,21], it prevents applications requiring higher resolution, for example, those analyzing the spatial variation of the parameters [10,22,23,24]. Relationship analyses would benefit from a registration among image sets
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