Improved execution efficiency of model-based roentgen stereophotogrammetric analysis: simplification and segmentation of model meshes

Abstract

Recently, the model-based roentgen stereophotogrammetric analysis (RSA) method has been developed as an in vivo tool to estimate static pose and dynamic motion of the instrumented prostheses. The two essential inputs for the RSA method are prosthetic models and roentgen images. During RSA calculation, the implants are often reversely scanned and input in the form of meshes to estimate the outline error between prosthetic projection and roentgen images. However, the execution efficiency of the RSA iterative calculation may limit its clinical practicability, and one reason for inefficiency may be very large number of meshes in the model. This study uses two methods of mesh manipulation to improve the execution efficiency of RSA calculation. The first is to simplify the model meshes and the other is to segment and delete the meshes of insignificant regions. An index (i.e. critical percentage) of an optimal element number is defined as the trade-off between execution efficiency and result accuracy. The predicted results are numerically validated by total knee prosthetic system. The outcome shows that the optimal strategy of the mesh manipulation is simplification and followed by segmentation. On average, the element number can even be reduced to 1% of the original models. After the mesh manipulation, the execution efficiency can be increased about 75% without compromising the accuracy of the predicted RSA results (the increment of rotation and translation error: 0.06° and 0.02 mm). In conclusion, prosthetic models should be manipulated by simplification and segmentation methods prior to the RSA calculation to increase the execution efficiency and then to improve clinical applicability of the RSA method.