3D reconstruction of joints from partial data using multi-object-based model: Towards a patient-specific knee implant design

Abstract

In clinical routine, the capture of three-dimensional (3D) bone geometry is crucial for surgical planning, implant placement and postoperative evaluation. Nevertheless, accurate 3D reconstruction of the knee joint for the estimation of patient-specific features remains a challenge, although it has been widely studied. In this context, statistical shape models (SSM) have been used to reconstruct a global shape from partial observations, based on their ability to capture the anatomical variation from different patients. However, these studies incorporate single object SSMs which limit their application for analyzing local bone morphology and thus they lack the capacity to analyze the human anatomy at the joint level. In this paper, we present a multi-object based framework for the 3D reconstruction of the knee joint using a dynamic multi-object Gaussian process model (DMO-GPM) and an adapted Markov Chain Monte Carlo (MCMC) based model fitting algorithm.The knees were reconstructed with an average mean square error of 1.81±0.37 mm and maximum error of 3.31 mm corresponding to the surface-to-surface distance between the predicted and original knees. The results show that the knee is accurately reconstructed, especially around the joint contact surfaces. This is crucial because most of the patient- specific features required for the implant design, use landmarks in this area. The results suggest that the approach is robust and accurate to design personalized knee implants.