  Flat panel computed tomography is used intraoperatively to assess the result
of surgery. Due to workflow issues, the acquisition typically cannot be carried
out in such a way that the axis aligned multiplanar reconstructions (MPR) of
the volume match the anatomically aligned MPRs. This needs to be performed
manually, adding additional effort during viewing the datasets. A PoseNet
convolutional neural network (CNN) is trained such that parameters of
anatomically aligned MPR planes are regressed. Different mathematical
approaches to describe plane rotation are compared, as well as a cost function
is optimized to incorporate orientation constraints. The CNN is evaluated on
two anatomical regions. For one of these regions, one plane is not orthogonal
to the other two planes. The plane's normal can be estimated with a median
accuracy of 5{\deg}, the in-plane rotation with an accuracy of 6{\deg}, and the
position with an accuracy of 6 mm. Compared to state-of-the-art algorithms the
labeling effort for this method is much lower as no segmentation is required.
The computation time during inference is less than 0.05 s.
