Keywords:
Dynamic cone-beam computed tomography (CBCT) imaging of the thorax, i. e. time-resolved reconstruction w. r. t. cardiac or respiratory motion, requires sophisticated algorithms, many of which are iterative and computationally expensive in terms of both runtime and memory. For the latter, hardware constraints pose a considerable challenge insofar as the volume grid cannot be chosen arbitrarily large. On the other hand, choosing a small grid may lead to severe artifacts if the object exceeds the size of the reconstruction domain. Additionally, lateral truncation of the projection data is commonly encountered as, e. g., flat panel detectors employed in interventional C-arm devices are not large enough to simultaneously image the entire width of the thorax in most patients. In iterative reconstruction, mild data truncation artifacts can also be alleviated by reconstructing on a sufficiently large grid. We present a simple model to incorporate information from outside the target grid in dynamic reconstruction. Its main component is the reconstruction of a static background image used to precompute an additive data correction term, which can be used in combination with any dynamic iterative reconstruction method. The effectiveness of our approach is demonstrated in a numerical phantom and clinical patient data.
- Oliver Taubmann
- Pattern Recognition Lab, Friedrich-Alexander-University Erlangen-Nuremberg
- Gunter Lauritsch
- Siemens Healthcare GmbH
- Gregor Krings
- University Medical Center Utrecht
- Andreas Maier
- Pattern Recognition Lab, Friedrich-Alexander-University Erlangen-Nuremberg
- C. Mory, V. Auvray, B. Zhang, M. Grass, D. Schafer, S. Chen, J. Carroll, ¨ S. Rit, F. Peyrin, P. Douek, and L. Boussel, “Cardiac C-arm computed tomography using a 3D + time ROI reconstruction method with spatial and temporal regularization,” Med Phys, vol. 41, p. 021903, 2014.
- S. Rit, M. van Herk, and J.-J. Sonke, “Fast distance-driven projection and truncation management for iterative cone-beam CT reconstruction,” in Fully3D, Beijing, China, 2009, p. 49–52.
- Y. Xia, H. Hofmann, F. Dennerlein, K. Muller, C. Schwemmer, S. Bauer, ¨ G. Chintalapani, P. Chinnadurai, J. Hornegger, and A. Maier, “Towards Clinical Application of a Laplace Operator-based Region of Interest Reconstruction Algorithm in C-arm CT,” IEEE Trans Med Imaging, vol. 33, no. 3, pp. 593–606, 2014.
- R. Chityala, K. R. Hoffmann, S. Rudin, and D. R. Bednarek, “Artifact reduction in truncated CT using sinogram completion,” in SPIE Med Imag, 2005, pp. 2110–2117.
- F. Dennerlein and A. Maier, “Region-of-interest reconstruction on medical C-arms with the ATRACT algorithm,” in SPIE Med Imag, 2012, p. 83131B.
- B. Zhang and G. L. Zeng, “Two-dimensional iterative region-of-interest (ROI) reconstruction from truncated projection data,” Med Phys, vol. 34, no. 3, pp. 935–944, 2007.
- P. T. Lauzier, J. Tang, and G.-H. Chen, “Time-resolved cardiac interventional cone-beam CT reconstruction from fully truncated projections using the prior image constrained compressed sensing (PICCS) algorithm,” Phys Med Biol, vol. 57, no. 9, p. 2461, 2012.
- D. Kolditz, M. Meyer, Y. Kyriakou, and W. A. Kalender, “Comparison ofextended field-of-view reconstructions in C-arm flat-detector CT using patient size, shape or attenuation information,” Phys Med Biol, vol. 56, no. 1, p. 39, 2011.
- Y. Xia, S. Bauer, A. Maier, M. Berger, and J. Hornegger, “Patientbounded extrapolation using low-dose priors for volume-of-interest imaging in C-arm CT,” Med Phys, vol. 42, no. 4, pp. 1787–1796, 2015.
- W. P. Segars, G. Sturgeon, S. Mendonca, J. Grimes, and B. M. W. Tsui, “4D XCAT phantom for multimodality imaging research,” Med Phys, vol. 37, pp. 4902–4915, 2010.
- A. Maier, H. Hofmann, C. Schwemmer, J. Hornegger, A. Keil, and R. Fahrig, “Fast Simulation of X-ray Projections of Spline-based Surfaces using an Append Buffer,” Phys Med Biol, vol. 57, no. 19, pp. 6193–6210, 2012.
- O. Taubmann, V. Haase, G. Lauritsch, Y. Zheng, G. Krings, J. Hornegger, and A. Maier, “Assessing cardiac function from total-variationregularized 4-D C-arm CT in the presence of angular undersampling,”Phys Med Biol, vol. 62, no. 7, p. 2762, 2017.