A research programme that aims to improve the state of the art in shoulder joint replacement surgery has been initiated at the Delft University of Technology. Development of improved endoprostheses for the upper extremities (DIPEX), as this effort is called, is a clinically driven multidisciplinary programme consisting of many contributory aspects. A part of this research programme focuses on the pre-operative planning and per-operative guidance issues. The ultimate goal of this part of the DIPEX project is to create a surgical support infrastructure that can be used to predict the optimal surgical protocol and can assist with the selection of the most suitable endoprosthesis for a particular patient. In the pre-operative planning phase, advanced biomechanical models of the endoprosthesis fixation and the musculo-skeletal system of the shoulder will be incorporated, which are adjusted to the individual's morphology. Subsequently, the support infrastructure must assist the surgeon during the operation in executing his surgical plan. In the per-operative phase, the chosen optimal position of the endoprosthesis can be realised using camera-assisted tools or mechanical guidance tools. In this article, the pathway towards the desired surgical support infrastructure is described. Furthermore, we discuss the pre-operative planning phase and the per-operative guidance phase, the initial work performed, and finally, possible approaches for improving prosthesis placement. Author Keywords: pre-operative planning; per-operative guidance; navigation; computer-assisted orthopaedic surgery; shoulder arthroplasty; medical applications; data registration; tracking; visualisation; MRI; CT; X-ray@en

For successful ball-joint replacement surgery, it is important to maintain the joint's geometric center. Pre-operative detection of this center is achieved by detecting the sphere that fits onto the articular surfaces in CT or MRI images. We have developed a novel technique to automatically determine the sub-voxel position and size of a sphere in unsegmented 3D images. The method is invariant to size and robust to noise. It only needs one fourth of a sphere to detect the center. Isotropically as well as anisotropically sampled images can be used. As no segmentation is required, it can be applied directly to clinical images.@en