Compensation of skull motion and breathing motion in CT using data-based and image-based metrics, respectively

H. Bruder; C. Rohkohl; K. Stierstorfer; T. Flohr

doi:10.1117/12.2217395

22 March 2016 Compensation of skull motion and breathing motion in CT using data-based and image-based metrics, respectively

H. Bruder, C. Rohkohl, K. Stierstorfer, T. Flohr

Author Affiliations +

Proceedings Volume 9783, Medical Imaging 2016: Physics of Medical Imaging; 97831E (2016) https://doi.org/10.1117/12.2217395
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

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 [1]. 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 [2] 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.

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H. Bruder, C. Rohkohl, K. Stierstorfer, and T. Flohr "Compensation of skull motion and breathing motion in CT using data-based and image-based metrics, respectively", Proc. SPIE 9783, Medical Imaging 2016: Physics of Medical Imaging, 97831E (22 March 2016); https://doi.org/10.1117/12.2217395

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