Robust Model-Based 3D/3D Fusion Using Sparse Matching for Minimally Invasive Surgery
Classical surgery is being disrupted by minimally invasive and transcatheter procedures. As there is no direct view or access to the affected anatomy, advanced imaging techniques such as 3D C-arm CT and C-arm fluoroscopy are routinely used for intra-operative guidance. However, intra-operative modalities have limited image quality of the soft tissue and a reliable assessment of the cardiac anatomy can only be made by injecting contrast agent, which is harmful to the patient and requires complex acquisition protocols. We propose a novel sparse matching approach for fusing high quality pre-operative CT and non-contrasted, non-gated intra-operative C-arm CT by utilizing robust machine learning and numerical optimization techniques. Thus, high-quality patient-specific models can be extracted from the pre-operative CT and mapped to the intra-operative imaging environment to guide minimally invasive procedures. Extensive quantitative experiments demonstrate that our model-based fusion approach has an average execution time of 2.9 s, while the accuracy lies within expert user confidence intervals.
Unable to display preview. Download preview PDF.
- 1.Leon, M.B., Smith, C.R., Mack, M., Miller, D.C., Moses, J.W., Svensson, L.G., Tuzcu, E.M., Webb, J.G., Fontana, G.P., Makkar, R.R., Brown, D.L., Block, P.C., Guyton, R.A., Pichard, A.D., Bavaria, J.E., Herrmann, H.C., Douglas, P.S., Petersen, J.L., Akin, J.J., Anderson, W.N., Wang, D., Pocock, S.: Transcatheter aortic-valve implantation for aortic stenosis in patients who cannot undergo surgery. N. Engl. J. Med. 363, 1597–1607 (2010)CrossRefGoogle Scholar
- 3.Maddux, J.T., Wink, O., Messenger, J.C., Groves, B.M., Liao, R., Strzelczyk, J., Chen, S.Y., Carroll, J.D.: Randomized study of the safety and clinical utility of rotational angiography versus standard angiography in the diagnosis of coronary artery disease. Catheter. Cardiovasc. Interv. 62(2), 167–174 (2004)CrossRefGoogle Scholar
- 4.Zheng, Y., John, M., Liao, R., Boese, J., Kirschstein, U., Georgescu, B., Zhou, S.K., Kempfert, J., Walther, T., Brockmann, G., Comaniciu, D.: Automatic aorta segmentation and valve landmark detection in C-arm CT: Application to aortic valve implantation. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010, Part I. LNCS, vol. 6361, pp. 476–483. Springer, Heidelberg (2010)CrossRefGoogle Scholar
- 8.Tu, Z.: Probabilistic boosting-tree: Learning discriminative models for classification, recognition, and clustering. In: Tenth IEEE Int’l Conf. on Comp. Vision, vol. 2, pp. 1589–1596. IEEE (2005)Google Scholar
- 9.Mitra, N.J., Gelfand, N., Pottmann, H., Guibas, L.: Registration of point cloud data from a geometric optimization perspective. In: Proc. of the 2004 Eurographics/ACM SIGGRAPH Symp. on Geom. Proc., pp. 22–31. ACM, New York (2004)Google Scholar