We present a novel reconstruction for motion correction of non-cardiac organs. With non-cooperative patients or in
emergency case, breathing motion or motion of the skull may compromise image quality.
Our algorithm is based on the optimization of either motion artefact metrics or data-driven metrics. This approach was
successfully applied in cardiac CTA . While motion correction of the coronary vessels requires a local motion model,
global motion models are sufficient for organs like the lung or the skull. The parameter vector for the global affine
motion is estimated iteratively, using the open source optimization library NLOPT. The image is updated using motion
compensated reconstruction in each of the iterations.
Evaluation of the metric value, e.g. the image entropy, provides information for the next iteration loop. After reaching
the fixed point of the iteration, the final motion parameters are used for a motion-compensated full quality
reconstruction. In head imaging the motion model is based on translation and rotation, in thoracic imaging the rotation is
replaced by non-isotropic scaling in all three dimensions.
We demonstrate the efficiency of the method in thoracic imaging by evaluating PET-CT data from free-breathing
patients. In neuro imaging, data from stroke patients showing skull tremor were analyzed. It was shown that motion
artefacts can be largely reduced and spatial resolution was restored. In head imaging, similar results can be obtained
using motion artefact metrics or data-driven metrics. In case of image-based metrics, the entropy of the image proved to
be superior. Breathing motion could also be significantly reduced using entropy metric. However, in this case data driven
metrics cannot be applied because the line integrals associated to the ROI of the lung have to be computed using the
local ROI mechanism  It was shown that the lung signal is corrupted by signals originating from the complement of
the lung. Thus a meaningful optimization of a data-driven cost function is not possible.