Abstract
The consideration of the individual knee ligament attachments is crucial for the application of patient specific musculoskeletal models in the clinical routine, e.g. in knee arthroplasty. Commonly, the pre-operative planning is based on CT images, where no soft tissue information is available. The goal of this study was to evaluate the accuracy of a full automatic and robust mesh morphing method that estimates locations of cruciate ligament attachments on the basis of training data.
The cruciate ligament attachments from 6 (n=6) different healthy male subjects (BH 184±6cm, BW 90±10kg) were identified in MRI-datasets by a clinical expert. The insertion areas were exported as point clouds and the centres of gravitation served as approximations of the attachments. These insertion points were used to annotate mean shapes of femur and tibia.
The mean shapes were built up from 332 training data sets each. The surface data were obtained from CT scans by performing an automatic segmentation followed by manual cleaning steps. The mean shapes were computed by selecting a data set randomly and aligning this reference rigidly to each of the remaining data sets. The data were fitted using the non-rigid ICP variant (N-ICP-A). Due to this morphing step, point correspondences were established.
By morphing a mean shape to the target geometries, including the cruciate ligament attachments, the distribution of the insertions on the original mean shape was obtained. Subsequently, a statistical mean was computed (annotated mean). The annotated mean shape was again morphed to the target data sets and the deviations of the respective predicted insertion points from the measured insertion points were computed.
The training data was successfully morphed to all 6 subjects in an automatic manner with virtually no distance error (10-5 mm). The mean distance between the measured and morphed ligament attachments was highest for the ACL in the femur (4.26±1.48 mm) and lowest for PCL in the tibia (1.63±0.36 mm). The highest deviation was observed for femoral ACL (6.93 mm).
In this study, a morphing based approach was presented to predict origins and insertions of the knee ligaments on the basis of CT-data, exemplarily shown for the cruciate ligaments. It has been demonstrated, that the N-ICP-A is applicable to predict the attachments automatic and robust with a high accuracy. This might help to improve patient-specific biomechanical models and their integration in the clinical routine.
