Vascular tree reconstruction with discrete tomography: intensity based camera correction for 3D reconstruction
- 67 Downloads
This paper is concerned with the reconstruction of vascular trees from few projections using discrete tomography. However, its computational cost is high and it lacks robustness when the data are inconsistent. We improve robustness by incorporating an intensity-based camera-correction method. The proposed approach is also capable of handling small motion artifacts by modeling them as repositionings of a virtual X-ray camera. We also present a parallel implementation which substantially reduces reconstruction time.
We propose a data-driven reduction of positional inconsistencies by minimizing the reconstruction residual to increase the robustness. Inspired by motion compen-sation algorithms in SPECT imaging, we combine an intensity-based 2D/3D-registration method with itera-tive reconstruction methods. Our objective is the robust vascular-tree reconstruction from positionally inconsistent data. The speed of the reconstruction is substantially increased by a volume-splitting scheme that allows parallel processing.
Vascular trees in the liver can be accurately reconstructed from few positionally inconsistent projections using digitally reconstructed radiographs. We have tested the proposed method on synthetic projection data and on objects imaged with a new robotized C-arm. We measured a decrease in the average reconstruction residual of about 13% for real data compared to projection data without preprocessing. Over 4,600 reconstruction experiments were conducted to evaluate the speed-up obtained when employing the volume-splitting scheme. Reconstruction time decreased linearly with increased number of processor-cores, both for real and synthetic data.
The proposed method reduces inconsistencies caused by positioning errors and small motion artifacts. No prior segmentation or detection of correspondences between projections is necessary, because all algorithms are intensity-based. As a result, the proposed method allows for robust, high-quality reconstructions, while reducing radiation dose substantially.
Keywords3D Reconstruction Digital subtraction angiography Algebraic reconstruction 2D/3D-registration Camera calibration
Unable to display preview. Download preview PDF.
- 1.Binder N, Bodensteiner C, Matthaeus L et al (2006) Image guided positioning for an interactive c-arm fluoroscope. In: Computer Assisted Radiology and Surgery (CARS)Google Scholar
- 3.Censor Y, Matej S (1999) Binary steering of non-binary iterative algorithms. In: Discrete tomography: foundations, algorithms and applications. Birkhauser, Boston, pp 285–296Google Scholar
- 6.Condurache AP (2008) Cardiovascular biomedical image analysis. Ph.D. Thesis, University of LuebeckGoogle Scholar
- 7.Doetter M (2006) Flouroskopiebasierte navigation zur intraoperativen unterstuetzung orthopaedischer eingriffe. Ph.D. Thesis, Technische Universitaet MuenchenGoogle Scholar
- 11.Herman G, Kuba A (2003) Medical applications of discrete tomography. In: Proceedings of the IEEE, vol 91Google Scholar
- 14.Matthaeus L, Binder N, Bodensteiner C et al (2007) Closed form inverse kinematic solution for fluoroscopic c-arms. Advanced Robotics 21Google Scholar
- 17.Onnasch DGW, Prause GPM (1999) Heart chamber reconstruction from biplane angiography. In: Discrete tomography: foundations, algorithms and applications. Birkhauser, Boston, pp 385–403Google Scholar
- 18.Parker D (1999) An empirical investigation of the global behavior of several pattern search algorithms. In: Technical Report, Department of Computer Science—University of North CarolinaGoogle Scholar
- 19.Penney G, Weese J, Little JA et al (1998) A comparison of similarity measures for use in 2d-3d medical image registration. In: MICCAI, p 1153Google Scholar
- 21.Schumacher H, Fischer B (2007) A new approach for motion correction in spect imaging. Bildverarbeitung fuer die MedizinGoogle Scholar
- 22.Vaillant AR, Blondel C, Mal G, Ayache N (2004) Reconstruction of coronary arteries from one rotational X-ray. In: Projection Sequence, Research report, no 5214. INRIA (2004)Google Scholar
- 23.Weber S, Schnoerr C, Schuele T, Hornegger J (2006) Binary tomography by iterating linear programs. In: Geometric properties for incomplete data. Springer, Netherlands, pp 183–197Google Scholar
- 24.Weber S, Schuele T, Hornegger J et al (2004) Binary tomography by iterating linear programs from noisy projections. In: Proceedings of International Workshop on Combinatorial Image Analysis (IWCIA). Springer, AucklandGoogle Scholar
- 25.Weese J, Goecke R, Penney G, Desmedt P, Buzug T, Schumann H (1999) Fast voxel-based 2d/3d registration algorithm using a rendering method based on the shear-warp factorization. In: Proceedings of the SPIE, pp 802–810Google Scholar