Abstract
2D to 3D image registration techniques are useful in the treatment of neurological diseases such as stroke. Image registration can aid physicians and neurosurgeons in the visualization of the brain for treatment planning, provide 3D information during treatment, and enable serial comparisons. In the context of stroke, image registration is challenged by the occluded vessels and deformed anatomy due to the ischemic process. In this paper, we present an algorithm to register 2D digital subtraction angiography (DSA) with 3D magnetic resonance angiography (MRA) based upon local point cloud descriptors. The similarity between these local descriptors is learned using a machine learning algorithm, allowing flexibility in the matching process. In our experiments, the error rate of 2D/3D registration using our machine learning similarity metric (52.29) shows significant improvement when compared to a Euclidean metric (152.54). The proposed similarity metric is versatile and could be applied to a wide range of 2D/3D registration.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Holmes, D., Rettmann, M., Robb, R.: Visualization in image-guided interventions. In: Peters, T., Cleary, K. (eds.) Image-Guided Interventions: Technology and Applications, pp. 45–80. Springer, New York (2008)
Flood, P.D., Banks, S.A.: Automated registration of three-dimensional knee implant models to fluoroscopic images using lipschitzian optimization. IEEE Trans. Med. Imaging PP(99), 1 (2016). doi:10.1109/TMI.2016.2553111
Otake, Y., Wang, A.S., Stayman, J.W., Uneri, A., Kleinszig, G., Vogt, S., Khanna, A.J., Gokaslan, Z.L., Siewerdsen, J.H.: Robust 3D–2D image registration: application to spine interventions and vertebral labeling in the presence of anatomical deformation. Phys. Med. Biol. 58, 8535–8553 (2013)
Fu, D., Kuduvalli, G.: A fast, accurate, and automatic 2D–3D image registration for image-guided cranial radiosurgery. Med. Phys. 35(5), 2180–2194 (2008)
Markelj, P., Tomaevi, D., Likar, B., Pernu, F.: A review of 3D/2D registration methods for image-guided interventions. Med. Image Anal. 16, 642–661 (2012)
Alves, R.S., Tavares, J.M.R.S.: Computer image registration techniques applied to nuclear medicine images. In: Tavares, J.M.R.S., Natal Jorge, R.M. (eds.) Computational and Experimental Biomedical Sciences: Methods and Applications. LNCVB, vol. 21, pp. 173–191. Springer, Heidelberg (2015). doi:10.1007/978-3-319-15799-3_13
Tavares, J.M.R.S.: Analysis of biomedical images based on automated methods of image registration. In: Bebis, G., et al. (eds.) ISVC 2014. LNCS, vol. 8887, pp. 21–30. Springer, Heidelberg (2014). doi:10.1007/978-3-319-14249-4_3
Oliveira, F.P., Tavares, J.M.R.: Medical image registration: a review. Comput. Methods Biomech. Biomed. Engin. 17, 73–93 (2014)
Chen, X., Varley, M.R., Shark, L.K., Shentall, G.S., Kirby, M.C.: An extension of iterative closest point algorithm for 3D–2D registration for pre-treatment validation in radiotherapy. In: MedVis, pp. 3–8 (2006)
Birkfellner, W., Stock, M., Figl, M., Gendrin, C., Hummel, J., Dong, S., Kettenbach, J., Georg, D., Bergmann, H.: Stochastic rank correlation: a robust merit function for 2D/3D registration of image data obtained at different energies. Med. Phys. 36, 3420–3428 (2009)
Vermandel, M., Betrouni, N., Gauvrit, J.Y., Pasquier, D., Vasseur, C., Rousseau, J.: Intrinsic 2D/3D registration based on a hybrid approach: use in the radiosurgical imaging process. Cell. Mol. Biol. 52, 44–53 (2006)
Oliveira, F.P., Pataky, T.C., Tavares, J.M.R.: Registration of pedobarographic image data in the frequency domain. Comput. Methods Biomech. Biomed. Engin 13, 731–740 (2010)
Khandelwal, P., Yavagal, D.R., Sacco, R.L.: Acute ischemic stroke intervention. J. Am. Coll. Cardiol. 67, 2631–2644 (2016)
Frangi, A.F., Niessen, W.J., Vincken, K.L., Viergever, M.A.: Multiscale vessel enhancement filtering. In: Wells, W.M., Colchester, A., Delp, S. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 130–137. Springer, Heidelberg (1998). doi:10.1007/BFb0056195
Cai, D., He, X., Han, J.: Spectral regression for efficient regularized subspace learning. In: ICCV, pp. 1–8 (2007)
Lagarias, J.C., Reeds, J.A., Wright, M.H., Wright, P.E.: Convergence properties of the nelder-mead simplex method in low dimensions. SIAM J. Optim. 9, 112–147 (1998)
Scalzo, F., Liebeskind, D.S.: Perfusion angiography in acute ischemic stroke. Comput. Math. Methods Med. 2014, 1–14 (2016). doi:10.1155/2016/2478324. Article ID 2478324
Scalzo, F., Hao, Q., Walczak, A.M., Hu, X., Hoi, Y., Hoffmann, K.R., Liebeskind, D.S.: Computational hemodynamics in intracranial vessels reconstructed from biplane angiograms. In: Bebis, G., et al. (eds.) ISVC 2010. LNCS, vol. 6455, pp. 359–367. Springer, Heidelberg (2010). doi:10.1007/978-3-642-17277-9_37
Nam, H.S., Scalzo, F., Leng, X., Ip, H.L., Lee, H.S., Fan, F., Chen, X., Soo, Y., Miao, Z., Liu, L., Feldmann, E., Leung, T., Wong, K.S., Liebeskind, D.S.: Hemodynamic impact of systolic blood pressure and hematocrit calculated by computational fluid dynamics in patients with intracranial atherosclerosis. J. Neuroimaging 26, 331–338 (2016)
Leng, X., Scalzo, F., Fong, A.K., Johnson, M., Ip, H.L., Soo, Y., Leung, T., Liu, L., Feldmann, E., Wong, K.S., Liebeskind, D.S.: Computational fluid dynamics of computed tomography angiography to detect the hemodynamic impact of intracranial atherosclerotic stenosis. Neurovascular Imaging 1, 1 (2015)
Acknowledgments
Prof. Scalzo was partially supported by a AHA grant 16BGIA27760152, a Spitzer grant, and received hardware donations from Gigabyte, Nvidia, and Intel. Alice Tang was partially supported by a UC Leads Fellowship.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Tang, A., Scalzo, F. (2016). Similarity Metric Learning for 2D to 3D Registration of Brain Vasculature. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2016. Lecture Notes in Computer Science(), vol 10072. Springer, Cham. https://doi.org/10.1007/978-3-319-50835-1_1
Download citation
DOI: https://doi.org/10.1007/978-3-319-50835-1_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-50834-4
Online ISBN: 978-3-319-50835-1
eBook Packages: Computer ScienceComputer Science (R0)