Volume Manipulations for Assisting Joint Surgery Diagnoses and Simulations

Volume visualization has widely used to assist medical diagnoses but still not to provide accurate joint pathology diagnoses, because CT or MR slices are usually not taken at the critical position inducing joint morphological pathology. This study proposes a volume manipulation method that segments and reconstructs anatomic structures of a joint and uploads their surface vertices to GPU, respectively. The vertices recorded in GPU are used to multiply with the same matrix for structure reposition, and different matrices for structure deformation manipulations. Real-time visual responses are achieved because time-consuming surface reconstruction and reloading the vertices to GPU are not required. Experimental results shows the surgeons can reposition related structures of a joint to their respective critical positions for accurate diagnoses about the joint morphology pathology, and simulate surgical procedures to confirm if the planned surgery can correct the pathology through the proposed system based on the developed method.