Abstract
Image registration plays an important role within pulmonary image analysis. Accurate registration is critical to post-analysis of lung mechanical properties. To improve registration accuracy, we utilize the rich information of vessel locations and shapes, and introduce a new similarity criterion, sum of squared vesselness measure difference (SSVMD). This metric is added to three existing intensity-based similarity criteria for nonrigid lung CT image registration to show its ability in improving matching accuracy. The registration accuracy is assessed by landmark error calculation and distance map visualization on vascular tree. The average landmark errors are reduced by over 20% and are within 0.7 mm after adding SSVMD constraint to three existing intensity-based similarity metrics. Visual inspection shows matching accuracy improvements in the lung regions near the thoracic cage and near the diaphragm. Experiments also show this vesselness constraint makes the Jacobian map of transformations physiologically more plausible and reliable.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Guerrero, T., Sanders, K., Castillo, E., Zhang, Y., Bidaut, L., Pan, T., Komaki, R.: Dynamic ventilation imaging from four-dimensional computed tomography. Phys. Med. Biol. 51(4), 777–791 (2006)
Reinhardt, J.M., Ding, K., Cao, K., Christensen, G.E., Hoffman, E.A., Bodas, S.V.: Registration-based estimates of local lung tissue expansion compared to xenon-CT measures of specific ventilation. Medical Image Analysis 12(6), 752–763 (2008)
Ding, K., Bayouth, J.E., Buatti, J.M., Christensen, G.E., Reinhardt, J.M.: 4DCT-based measurement of changes in pulmonary function following a course of radiation therapy. Medical Physics 37(3), 1261–1272 (2010)
Coselmon, M.M., Balter, J.M., McShan, D.L., Kessler, M.L.: Mutual information based CT registration of the lung at exhale and inhale breathing states using thin-plate splines. Medical Physics 31(11), 2942–2948 (2004)
Christensen, G.E., Song, J.H., Lu, W., Naqa, I.E., Low, D.A.: Tracking lung tissue motion and expansion/compression with inverse consistent image registration and spirometry. Medical Physics 34(6), 2155–2165 (2007)
Gorbunova, V., Lo, P., Ashraf, H., Dirksen, A., Nielsen, M., de Bruijne, M.: Weight preserving image registration for monitoring disease progression in lung CT. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part II. LNCS, vol. 5242, pp. 863–870. Springer, Heidelberg (2008)
Yin, Y., Hoffman, E.A., Lin, C.L.: Mass preserving non-rigid registration of CT lung images using cubic B-spline. Medical Physics 36(9), 4213–4222 (2009)
Hu, S., Hoffman, E.A., Reinhardt, J.M.: Automatic lung segmentation for accurate quantitation of volumetric X-ray CT images. IEEE Trans. on Medical Imaging 20, 490–498 (2001)
Murphy, K., van Ginneken, B., Pluim, J., Klein, S., Staring, M.: Semi-automatic reference standard construction for quantitative evaluation of lung CT registration. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part II. LNCS, vol. 5242, pp. 1006–1013. Springer, Heidelberg (2008)
Thevenaz, P., Unser, M.: Spline pyramids for inter-modal image registration using mutual information. In: Proc. SPIE, vol. 3169, pp. 236–247 (1997)
Mattes, D., Haynor, D., Vesselle, H., Lewellen, T., Eubank, W.: PET-CT image registration in the chest using free-form deformations. IEEE Transactions on Medical Imaging 22(1), 120–128 (2003)
Frangi, A.F., Niessen, W.J., Vincken, K.L., Viergever, M.A.: Multiscale vessel enhancement filtering. In: Wells, W.M., Colchester, A.C.F., Delp, S.L. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 130–137. Springer, Heidelberg (1998)
Byrd, R.H., Lu, P., Nocedal, J., Zhu, C.: A limited memory algorithm for bound constrained optimization. SIAM J. Sci. Comput. 16(5), 1190–1208 (1995)
Choi, Y., Lee, S.: Injectivity conditions of 2d and 3d uniform cubic b-spline functions. Graphical Models 62(6), 411–427 (2000)
Guo, J., Fuld, M.K., Alford, S.K., Reinhardt, J.M., Hoffman, E.A.: Pulmonary analysis software suite 9.0: Integrating quantitative measures of function with structural analyses. In: First International Workshop on Pulmonary Image Analysis, New York, pp. 283–292 (2008)
Kabus, S., Klinder, T., Murphy, K., van Ginneken, B., Lorenz, C., Pluim, J.P.W.: Evaluation of 4D-CT lung registration. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009. LNCS, vol. 5761, pp. 747–754. Springer, Heidelberg (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cao, K., Ding, K., Christensen, G.E., Raghavan, M.L., Amelon, R.E., Reinhardt, J.M. (2010). Unifying Vascular Information in Intensity-Based Nonrigid Lung CT Registration. In: Fischer, B., Dawant, B.M., Lorenz, C. (eds) Biomedical Image Registration. WBIR 2010. Lecture Notes in Computer Science, vol 6204. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14366-3_1
Download citation
DOI: https://doi.org/10.1007/978-3-642-14366-3_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14365-6
Online ISBN: 978-3-642-14366-3
eBook Packages: Computer ScienceComputer Science (R0)