@inproceedings{punzet_gcc-based_2019,
	address = {Philadelphia, USA},
	title = {{GCC}-based extrapolation of truncated {CBCT} data with dimensionality-reduced extrapolation models},
	volume = {11072},
	url = {https://doi.org/10.1117/12.2534510},
	abstract = {A typical incomplete data problem arising in cone-beam computed tomography (CBCT) occurs when an object is either too large to be projected onto the detector or is deliberately only projected in parts. This problem is called truncation. Tomographic images reconstructed from truncated projection data can be severely impaired by image artifacts depending on the degree of truncation. A typical strategy to counter this is to extend the projection data by some smooth extrapolation. In order to accurately approximate the shape of the scanned object outside of the volume of interest (VOI), we previously presented a method which fits an extrapolation model to the truncated data by minimizing an error function based on the Grangeat consistency condition (GCC). In this work we propose a method of reducing the complexity of the extrapolation by making use of the 0th image moments of the truncated projection data.},
	booktitle = {Proceedings - 15th {International} {Meeting} on {Fully} {Three}-{Dimensional} {Image} {Reconstruction} in {Radiology} and {Nuclear} {Medicine} {Volume} 11072},
	author = {Punzet, Daniel and Frysch, Robert and Pfeiffer, Tim and Beuing, Oliver and Rose, Georg},
	month = may,
	year = {2019}
}