A Novel Approach to Anatomical Structure Morphing for Intraoperative Visualization

In computer assisted surgery 3D models are now routinely used to plan and navigate a surgery. These models enhance the surgeon’s capability to decrease the invasiveness of surgical procedures and increase their accuracy and safety. Models obtained from specifically acquired CT scans have the disadvantage that they induce high radiation dose to the patient. In this paper we propose a novel method to construct a patient-specific model that provides an appropriate intra-operative 3D visualization without the need for a pre or intra-operative imaging. The 3D model is reconstructed by fitting a statistical deformable model to minimal sparse 3D data consisting of digitized landmarks and surface points that are obtained intra-operatively. The statistical model is constructed using Principal Component Analysis from training objects. Our morphing method then computes a Mahalanobis distance weighted least square fit of the model by solving a linear equation system. The refined morphing scheme has better convergence behaviour because of the additional parameter that relaxes the Mahalanobis distance term as additional points are incorporated. We present leave-one-out experiments with model generated from proximal femors and hippocampi.