Abstract
The purpose of this chapter is to give an introduction to intensity-based deformable image registration and present a brief overview of the state-of-the-art. First, we lay out the basic principles of deformable registration. Next, the key components of the registration framework are discussed in detail and two popular algorithms for deformable registration are described as an example. We review past studies on respiratory motion estimation for radiotherapy. Finally, we briefly list useful open-source software packages and available image and validation data sets for deformable registration of the thorax.
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References
Boldea, V., Sarrut, D., Carrie, C.: Comparison of 3D dense deformable registration methods for breath-hold reproducibility study in radiotherapy. In: SPIE Medical Imaging: Visualization, Image-Guided Procedures, and Display, vol. 5747, pp. 222–230 (2005)
Boldea, V., Sarrut, D., Clippe, S.: Lung deformation estimation with non-rigid registration for radiotherapy treatment. In: International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), vol. 2878, pp. 770–777 (2003)
Boldea, V., Sharp, G.C., Jiang, S.B., Sarrut, D.: 4D-CT lung motion estimation with deformable registration: quantification of motion nonlinearity and hysteresis. Med. Phys. 35(3), 1008 (2008)
Bookstein, F.: Principal warps: thin-plate splines and the decomposition of deformations. IEEE Trans. Pattern Anal. Mach. Intell. 11(6), 567–585 (2002)
Bro-Nielsen, M., Gramkow, C.: Fast fluid registration of medical images. In: Visualization in Biomedical Computing, pp. 265–276. Springer, Berlin (1996)
Brock, K.K.: Results of a multi-institution deformable registration accuracy study (MIDRAS). Int. J. Radiat. Oncol. Biol. Phys. 76(2), 583–596 (2010)
Byrd, R., Lu, P., Nocedal, J., Zhu, C.: A limited memory algorithm for bound constrained optimization. SIAM J. Sci. Comput. 16(5), 1190–1208 (1995)
Cachier, P., Ayache, N.: Isotropic energies, filters and splines for vector field regularization. J. Math. Imaging Vis. 20, 251–265 (2004)
Capek, K.: Optimisation strategies applied to global similarity based image registration methods. In: International Conferences in Central Europe on Computer Graphics, Visualization and Computer Vision (WSCG), vol. 2, pp. 369–374, Plzen, Czech Republic (1999)
Castillo, E., Castillo, R., Martinez, J., Shenoy, M., Guerrero, T.: Four-dimensional deformable image registration using trajectory modeling. Phys. Med. Biol. 55(1), 305–327 (2010)
Castillo, E., Castillo, R., Zhang, Y., Guerrero, T.: Compressible image registration for thoracic computed tomography images. J. Med. Biol. Eng. 29(5) (2009)
Castillo, R., Castillo, E., Guerra, R., Johnson, V.E., McPhail, T., Garg, A.K., Guerrero, T.: A framework for evaluation of deformable image registration spatial accuracy using large landmark point sets. Phys. Med. Biol. 54(7), 1849–1870 (2009)
Choi, Y., Lee, S.: Local injectivity conditions of 2D and 3D uniform cubic B-spline functions. In: Proceedings of the Seventh Pacific Conference on Computer Graphics and Applications, 1999, vol. 62, pp. 302–311 (1999)
Christensen, G., Johnson, H.: Consistent image registration. IEEE Trans. Med. Imaging 20(7), 568–582 (2001)
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. Med. Phys. 31(11), 2942 (2004)
Delmon, V., Rit, S., Pinho, R., Sarrut, D.: Direction dependent B-splines decomposition for the registration of sliding objects. In: MICCAI (Medical Image Computing and Computer-Assisted Intervention); Four International Workshop on Pulmonary Image, Analysis, pp. 45–55 (2011)
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. Med. Phys. 37(3), 1261 (2010)
Ding, K., Yin, Y., Cao, K., Christensen, G., Lin, C., Hoffman, E., Reinhardt, J.: Evaluation of lobar biomechanics during respiration using image registration. In: Medical Image Computing and Computer-Assisted Intervention—MICCAI, 2009, pp. 739–746 (2009)
Ehrhardt, J., Werner, R., SchmidtRichberg, A., Handels, H., SchmidtRichberg, A.: Prediction of respiratory motion using a statistical 4D mean motion model. In: Second International Worskshop on Pulmonary Image Processing, pp. 3–14 (2009)
Fan, L.: Evaluation and application of 3D lung warping and registration model using HRCT images. In: Proceedings of SPIE, pp. 234–243 (2001)
Glocker, B., Komodakis, N., Tziritas, G., Navab, N., Paragios, N.: Dense image registration through MRFs and efficient linear programming. Med. Image Anal. 12(6), 731–741 (2008)
Gu, X., Pan, H., Liang, Y., Castillo, R., Yang, D., Choi, D., Castillo, E., Majumdar, A., Guerrero, T., Jiang, S.B.: Implementation and evaluation of various demons deformable image registration algorithms on a GPU. Phys. Med. Biol. 55(1), 207–219 (2010)
Guerrero, T., Zhang, G., Huang, T.C., Lin, K.P.: Intrathoracic tumour motion estimation from CT imaging using the 3D optical flow method. Phys. Med. Biol. 49(17), 4147–4161 (2004)
Holden, M.: A review of geometric transformations for nonrigid body registration. IEEE Trans. Med. Imaging 27(1), 111–128 (2008)
Ibanez, L., Schroeder, W., Ng, L., Cates, J., Insight consortium: the ITK software guide. Technical report, pp. 1–3 (2005)
Johnson, H., Christensen, G.: Consistent landmark and intensity-based image registration. IEEE Trans. Med. Imaging 21(5), 450–461 (2002)
Kabus, S., Klinder, T., Murphy, K., Van Ginneken, B., Van Lorenz, C., Pluim, J.P.W., Lorenz, C.: Evaluation of 4D-CT lung registration. In: Medical Image Computing and Computer-Assisted Intervention, vol. 12(Pt 1), pp. 747–754 (2009)
Kaus, M., Netsch, T., Kabus, S., Pekar, V., McNutt, T., Fischer, B.: Estimation of organ motion from 4D CT for 4D radiation therapy planning of lung cancer. In: Medical Image Computing and Computer-Assisted Intervention, MICCAI 2004, vol. 3217, pp. 1017–1024. Springer, Berlin (2004)
Keall, P.: 4-dimensional computed tomography imaging and treatment planning. Semin. Radiat. Oncol. 14(1), 81–90 (2004)
Klein, S., Staring, M., Murphy, K., Viergever, M.A., Pluim, J.P.W.: Elastix: a toolbox for intensity-based medical image registration. IEEE Trans. Med. Imaging 29(1), 196–205 (2010)
Klein, S., Staring, M., Pluim, J.P.W.: Evaluation of optimization methods for nonrigid medical image registration using mutual information and B-splines 16, 2879–2890 (2007)
Kybic, J., Unser, M.: Multidimensional elastic registration of images using splines. In: Proceedings of International Conference on Image Processing, 2000, vol. 2, pp. 455–458 (2000)
Lehmann, T.M., Gönner, C., Spitzer, K.: Survey: interpolation methods in medical image processing. IEEE Trans. Med. Imaging 18(11), 1049–1075 (1999)
Li, B., Christensen, G.G.E.G., Homan, E., McLennan, G., Reinhardt, J.M.J., Hoffman, E.A.: Establishing a normative atlas of the human lung: intersubject warping and registration of volumetric CT images. Acad. Radiol. 10(3), 255–265 (2003)
Li, T., Koong, A., Xing, L.: Enhanced 4D cone-beam CT with inter-phase motion model. Med. Phys. 34(9), 3688 (2007)
Lu, W., Chen, M., Olivera, G., Ruchala, K.J., Mackie, T.: Fast free-form deformable registration via calculus of variations. Phys. Med. Biol. 49(14), 3067 (2004)
Maes, F., Collignon, A., Vandermeulen, D., Marchal, G., Suetens, P.: Multimodality image registration by maximization of mutual information. IEEE Trans. Med. Imaging 16(2), 187–198 (1997)
Markelj, P., Tomazevic, D., Likar, B., Pernus, F.: A review of 3D/2D registration methods for image-guided interventions. Med. Image Anal. 16(3), 642–661 (2010)
Mcclelland, J.R., Blackall, J., Tarte, S., Chandler, A., S: A continuous 4D motion model from multiple respiratory cycles for use in lung radiotherapy. Med. Phys. 333348 (2006)
Modat, M., Ridgway, G.R., Taylor, Z.A., Lehmann, M., Barnes, J., Hawkes, D.J., Fox, N.C., Ourselin, S.: Fast free-form deformation using graphics processing units. Comput. Methods Programs Biomed. 98(3), 278–284 (2010)
Murphy, K., van Ginneken, B., Reinhardt, J.M., et al.: Evaluation of registration methods on thoracic CT: the EMPIRE10 challenge. IEEE Trans. Med. Imaging 30(11), 1901–1920 (2011)
Nagel, H., Enkelmann, W.: An investigation of smoothness constraints for the estimation of displacement vector fields from image sequences. IEEE Trans. Pattern Anal. Mach. Intell. 8(5), 565–593 (1986)
Nocedal, J.: Updating quasi-Newton matrices with limited storage. Math. comput. 35(151), 773–782 (1980)
Noe, K.O., Tanderup, K., Lindegaard, J.C., Grau, C., Sørensen, T.S.: GPU accelerated viscous-fluid deformable registration for radiotherapy. Stud. Health Technol. Inform. 132, 327–332 (2008)
Pennec, X., Cachier, P.: Understanding the demon’s algorithm: 3D non-rigid registration by gradient descent. In: MICCAI (Medical Image Computing and Computer-Assisted Intervention), pp. 597–606 (1999)
Pluim, J.P.W., Maintz, J., Viergever, M.A.: Mutual-information-based registration of medical images: a survey. IEEE Trans. Med. Imaging 22(8), 986–1004 (2003)
Press, W.H., Flannery, B.P., Teukolsky, S.A., Vetterling, W.T.: Numerical recipes in C: the art of scientific computing. Comput. Math. Appl. 19(7), 100 (1990)
Rietzel, E., Chen, G.T.Y.: Deformable registration of 4D computed tomography data. Med. Phys. 33(11), 4423 (2006)
Rit, S., Pinho, R., Delmon, V., Pech, M., Bouilhol, G., Schaerer, J., Navalpakkam, B., Vandemeulebroucke, J., Seroul, P., Sarrut, D.: VV, a 4D slicer. In: MICCAI Conference International Workshop on Pulmonary Image Analysis (2011)
Roche, A., Malandain, G., Ayache, N., N: Unifying maximum likelihood approaches in medical image registration. Int. J. Imaging Syst. Technol. 11(1), 71–80 (2000)
Rohlfing Jr, T., C.M., Bluemke, D., Jacobs, M.: Volume-preserving nonrigid registration of MR breast images using free-form deformation with an incompressibility constraint. IEEE Trans. Med. Imaging 22(6), 730–741 (2003)
Ruan, D., Esedoglu, S., Fessler, J.: Discriminative sliding preserving regularization in medical image registration. In: Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro, pp. 430–433. IEEE Press, Piscataway (2009)
Rueckert, D., Aljabar, P., Heckemann, R.A., Hajnal, J.V., Hammers, A.: Diffeomorphic registration using B-splines. In: International Conference on Medical Image Computing and Computer-Assisted Intervention—LNCS 4191, vol. 9(Pt 2), pp. 702–709 (2006)
Rueckert, D., Sonoda, L., Hayes, C., Hill, D., Leach, M.: Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans. Med. Imaging 18(8), 712–721 (1999)
Ruppertshofen, H., Kabus, S., Fischer, B.: Image Registration using Tensor Grids for Lung Ventilation Studies: Bildverarbeitung für die Medizin, vol. 6, pp. 117–121 (2009)
Sarrut, D., Boldea, V., Ayadi, M., Badel, J., Ginestet, C., Clippe, S.: Nonrigid registration method to assess reproducibility of breath-holding with ABC in lung cancer. Int. J. Radiat. Oncol. Biol. Phys. 61(2), 594–607 (2005)
Sarrut, D., Boldea, V., Miguet, S., Ginestet, C.: Simulation of four-dimensional CT images from deformable registration between inhale and exhale breath-hold CT scans. Med. Phys. 33(3), 605 (2006)
Sarrut, D., Vandemeulebroucke, J.: B-LUT: fast and low memory B-spline image interpolation. Comput. Methods Programs Biomed. 99, 172–178 (2010)
Schmidt-Richberg, A., Ehrhardt, J., Werner, R., Handels, H., Schmidt-Richberg, A.: Slipping objects in image registration: improved motion field estimation with direction-dependent regularization. In: International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), vol. 12(Pt 1), pp. 755–62 (2009)
Seroul, P., Sarrut, D.: VV: a viewer for the evaluation of 4D image registration. MIDAS J., 1–8 (2008). http://hdl.handle.net/10380/1458
Shackleford, J.a., Kandasamy, N., Sharp, G.C.: On developing B-spline registration algorithms for multi-core processors. Phys. Med. Biol. 55(21), 6329–6351 (2010)
Shackleford, J.A., Kandasamy, N., Sharp, G.C.: On developing B-spline registration algorithms for multi-core processors. Phys. Med. Biol. 55(21), 6329–6351 (2010)
Sharp, G.C., Kandasamy, N., Singh, H., Folkert, M.: GPU-based streaming architectures for fast cone-beam CT image reconstruction and demons deformable registration. Phys. Med. Biol. 52(19), 5771 (2007)
Staring, M., Klein, S., Pluim, J.P.W.: A rigidity penalty term for nonrigid registration. Med. Phys. 34(11), 4098 (2007)
Studholme, C., Hill, D., Hawkes, D.: Others: an overlap invariant entropy measure of 3D medical image alignment. Pattern Recogn. 32(1), 71–86 (1999)
Sundaram, T., Gee, J.: Towards a model of lung biomechanics: pulmonary kinematics via registration of serial lung images. Med. Image Anal. 9(6), 524–537 (2005)
Thirion, J.: Image matching as a diffusion process: an analogy with Maxwell’s demons. Med. Image Anal. 2(3), 243–260 (1998)
Unser, M., Aldroubi, A., Eden, M.: B-spline signal processing: Part I theory. IEEE Trans. Signal Process. 41(2), 821–833 (1993)
Vandemeulebroucke, J., Bernard, O., Rit, S., Kybic, J., Clarysse, P., Sarrut, D.: Automated segmentation of a motion mask to preserve sliding motion in deformable registration of thoracic CT. Med. Phys. 39(2), 1006 (2012)
Vandemeulebroucke, J., Rit, S., Kybic, J., Clarysse, P., Sarrut, D.: Spatiotemporal motion estimation for respiratory-correlated imaging of the lungs. Med. Phys. 38(1), 166 (2011)
Vandemeulebroucke, J., Sarrut, D., Clarysse, P.: The POPI-model, a point-validated pixel-based breathing thorax model. In: XVth International Conference on the use of Computers in Radiation Therapy (ICCR), pp. 1–8. Toronto, Canada (2007)
Vercauteren, T., Pennec, X., Perchant, A., Ayache, N.: Diffeomorphic demons: efficient non-parametric image registration. Neuroimage 45(1), S61–S72 (2009)
von Siebenthal, M., Székely, G., Gamper, U., Boesiger, P., Lomax, A., Cattin, P.: 4D MR imaging of respiratory organ motion and its variability. Phys. Med. Biol. 52(6), 1547–1564 (2007)
Wells 3rd, W., Viola, P., Atsumi, H., Nakajima, S., Kikinis, R.: Multi-modal volume registration by maximization of mutual information. Med. Image Anal. 1(1), 35–51 (1996)
Weruaga, L., Morales, J., Nunez, L., Verdu, R.: Estimating volumetric motion in human thorax with parametric matching constraints. IEEE Trans. Med. Imaging 22(6), 766–772 (2003)
Wolthaus, J.W.H., Sonke, J.J., van Herk, M., Belderbos, J.S.A., Rossi, M.M.G., Lebesque, J.V., Damen, E.M.F.: Comparison of different strategies to use four-dimensional computed tomography in treatment planning for lung cancer patients. Int. J. Radiat. Oncol. Biol. Phys. 70(4), 1229–1238 (2008)
Wu, Z., Rietzel, E., Boldea, V., Sarrut, D., Sharp, G.C.: Evaluation of deformable registration of patient lung 4DCT with subanatomical region segmentations. Med. Phys. 35, 775 (2008)
Yang, D., Li, H., Low, D., Deasy, J.O., El Naqa, I.M.: A fast inverse consistent deformable image registration method based on symmetric optical flow computation. Phys. Med. Biol. 53(21), 6143–6165 (2008)
Yang, D., Lu, W., Low, D., Deasy, J.O., Hope, A.J., El Naqa, I.M.: 4D-CT motion estimation using deformable image registration and 5D respiratory motion modeling. Med. Phys. 35(10), 4577 (2008)
Yin, Y., Hoffman, E., Lin, C.: Mass preserving nonrigid registration of CT lung images using cubic B-spline. Med. Phys. 36(9), 4213–4222 (2009)
Zeng, R., Fessler, J.A., Balter, J.M.: Respiratory motion estimation from slowly rotating x-ray projections: theory and simulation. Med. Phys. 32(4), 984 (2005)
Zeng, R., Fessler, J.J.A., Balter, J.M.: Estimating 3-D respiratory motion from orbiting views by tomographic image registration. IEEE Trans. Med. Imaging 26(2), 153–163 (2007)
Zhang, Q., Pevsner, A., Hertanto, A., Hu, Y.C., Rosenzweig, K.E., Ling, C.C., Mageras, G.S.: A patient-specific respiratory model of anatomical motion for radiation treatment planning. Med. Phys. 34(12), 4772 (2007)
Zhao, T., Lu, W., Yang, D., Mutic, S., Noel, C.E., Parikh, P.J., Bradley, J.D., Low, D.A.: Characterization of free breathing patterns with 5D lung motion model. Med. Phys. 36(11), 5183 (2009)
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Sarrut, D., Vandemeulebroucke, J., Rit, S. (2013). Intensity-Based Deformable Registration: Introduction and Overview. In: Ehrhardt, J., Lorenz, C. (eds) 4D Modeling and Estimation of Respiratory Motion for Radiation Therapy. Biological and Medical Physics, Biomedical Engineering. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36441-9_6
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