@misc{haselji_time_separation_2022, 
author={Haselji , H.,Kulvait, V.,Frysch, R.,Saad, F.,Hensen, B.,Wacker, F.,Brüsch, I.,Werncke, T.,Rose, G.},
title={Time separation technique using prior knowledge for dynamic liver perfusion imaging},
year={2022},
howpublished = {conference paper: null; null},
doi = {https://doi.org/10.1117/12.2646449},
abstract = {The perfusion imaging using C-arm CT could be used intraoperatively for liver cancer treatment planning and evaluation. To deal with undersampled data due to slow C-arm CT rotation and pause between the rotations, we applied model-based reconstruction methods. Recent works using the time separation technique with an analytical basis function set have led to a significant improvement in the quality of C-arm CT perfusion maps. In this work we apply the time separation technique with a prior knowledge basis function set extracted using singular value decomposition from CT perfusion reconstructions. On C-arm CT liver perfusion scan simulated based on the real CT liver perfusion scan we show that the bases extracted from only two CT perfusion scans are capable of modeling the C-arm CT data correctly.},
note = {Online available at: \url{https://doi.org/10.1117/12.2646449} (DOI). Haselji, H.; Kulvait, V.; Frysch, R.; Saad, F.; Hensen, B.; Wacker, F.; Brüsch, I.; Werncke, T.; Rose, G.: Time separation technique using prior knowledge for dynamic liver perfusion imaging. In: SPIE Digital Library (Ed.): Proceedings of SPIE: 7th International Conference on Image Formation in X-Ray Computed Tomography. 2022. 1230420. DOI: 10.1117/12.2646449}}