Driven by the observation that orbit optimization requires knowledge of the position, extent, and orientation of metallic objects in the volume of interest (VOI), we devise a shape model in the form of ellipsoids. Reconstructing an ellipsoid from two projection images is not unambiguously possible using analytic methods. By interpreting the problem as probability density estimation, a maximum likelihood fit can be recovered using a Gaussian Mixture Model (GMM). This parametric representation of metal objects is used to efficiently calculate metal pathlength maps for candidate projections on tilted circular trajectories through analytic forward projection. The scatter fraction behind the metal object is modelled as a function of metal pathlength to score views and choose artifact minimizing tilted trajectories.

Given two projection images of simulated ellipsoidal objects, the GMM accurately estimates the position and longest axis length within millimeter tolerance. Depending on the orientation relative to the two acquired scout views, an estimation error within the scout-view plane is observed. The generalization on measured data and the shape model hypothesis is verified in a phantom study showing a good correspondence of the modelled metric and observed reduction of artifacts in the tilted CBCT scans.

The severity of Metal Artifacts can be reduced by optimizing trajectories on low-fidelity shape models. These surrogate representations can be efficiently estimated from two views for most relative object orientations, but a well-defined ‘blind spot’ remains. The reconstruction error was found to have little effect on tilted orbit optimization if the tilt-axis is contained in the scout-view plan}, author = {Rohleder, Maximilian and Mekki, Lina and Uneri, Ali and Sisniega, Alex and Kunze, Holger and Kleinszig, Gerhard and Maier, Andreas and Kreher, Björn W. and Siewerdsen, Jeffrey H.}, date = {2023-04-07/2023-04-07}, doi = {10.1117/12.2654349}, faupublication = {yes}, peerreviewed = {unknown}, series = {Advanced Photonics Conference}, title = {{Cone}-beam {CT} trajectory optimization for metal artifact avoidance using ellipsoidal object parameterizations}, venue = {San Diego, CA}, year = {2023} }