Abstract
Purpose
A pulmonary respiration model for deformable registration of lung CT for the surgery path planning and surgical navigation is an important, difficult, and time-consuming task. This paper presents a new fast deformable registration method for 4D lung CT in a hybrid framework incorporating point set registration with mutual information registration.
Method
The point sets of the lung surface and vessels are automatically extracted. Their displacement vectors are obtained by point set registration. The sum of squared Euclidean distance between the displacement vectors of these point sets and the displacement vectors based on the B-spline transformation model is minimized as a novel similarity measure to derive the rough transformation function. Finally, the rough transformation function is refined by using the mutual information-based registration method. To evaluate the effectiveness of the proposed method, the authors performed registrations on 20 4D lung volume cases from two different CT scanners. The proposed method was compared with the point set-based method, the mutual information-based method, and the ANTS method, which is a state-of-the-art deformable registration technique.
Results
The results show that the landmark distance errors and computation time of the proposed method decreased an average of 5 and 70 %, respectively, when compared to the mutual information-alone-based method. The proposed method results in an average of 28 % lower landmark distance error than registration method based on point sets in spite of increase in computation time. Moreover, compared with ANTS, the computation time of the proposed method is reduced by an average of 93 % in the case of comparable landmark distance errors.
Conclusion
The accuracy and speed of the proposed deformable registration method indicate that the method is suitable for use in a clinical image-guided intervention system.





Similar content being viewed by others
References
Keall P (2004) 4-dimensional computed tomography imaging and treatment planning. Semin Radiat Oncol 14:81–90
Werner R, Ehrhardt J, Schmidt-Richberg A, Heiss A, Handels H (2010) Estimation of motion fields by non-linear registration for local lung motion analysis in 4D CT image data. Int J Comput Assist Radiol Surg 5:595–605
Boldea V, Sharp GC, Jiang SB, Sarrut D (2008) 4D-CT lung motion estimation with deformable registration: quantification of motion nonlinearity and hysteresis. Med Phys 35:1008–1018
Rueckert D, Aljabar P (2010) Nonrigid registration of medical images: theory, methods, and applications. IEEE Signal Process Mag 27:113–119
Kabus S, Klinder T, Murphy K, van Ginneken B, Lorenz C, Pluim JPW (2009) Evaluation of 4D-CT lung registration. Med Image Comput Comput-Assist Interv Miccai 2009 Pt I, Proc 5761:747–754
Mattes D, Haynor DR, Vesselle H, Lewellen TK, Eubank W (2003) PET-CT image registration in the chest using free-form deformations. IEEE Trans Med Imaging 22:120–128
Rueckert D, Sonoda LI, Hayes C, Hill DLG, Leach MO, Hawkes DJ (1999) Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans Med Imaging 18:712–721
Thevenaz P, Unser M (2000) Optimization of mutual information for multiresolution image registration. IEEE Trans Image Process 9:2083–99
Paquin D, Levy D, Xing L (2007) Hybrid multiscale landmark and deformable image registration. Math Biosci Eng 4:711–737
Gorbunova V, Durrleman S, Lo PC, Pennec X, de Bruijne M (2010) Lung Ct registration combining intensity, curves and surfaces. In: 2010 7th IEEE international symposium on biomedical imaging: from nano to macro, pp 340–343
Yin YB, Hoffman EA, Ding K, Reinhardt JM, Lin CL (2011) A cubic B-spline-based hybrid registration of lung CT images for a dynamic airway geometric model with large deformation. Phys Med Biol 56:203–218
Negahdar M, Zacarias A, Milam RA, Dunlap N, Woo SY, Amini AA (2012) An automated landmark-based elastic registration technique for large deformation recovery from 4-D CT lung images. Med Imaging 2012: Biomed Appl Mol Struct Funct Imaging, vol. 8317
Rueckert D, Aljabar P, Heckemann RA, Hajnal JV, Hammers A (2006) Diffeomorphic registration using B-splines. Med Image Comput Comput Assist Interv Miccai 2006, Pt 2 4191:702–709
Murphy K, van Ginneken B, Reinhardt JM, Kabus S, Ding K, Deng X et al (2011) Evaluation of registration methods on thoracic CT: the EMPIRE10 challenge. IEEE Trans Med Imaging 30:1901–1920
Yin Y, Hoffman EA, Lin CL (2009) Mass preserving nonrigid registration of CT lung images using cubic B-spline. Med Phys 36:4213–4222
Choi Y, Lee S (2000) Injectivity conditions of 2D and 3D uniform cubic B-spline functions. Graph Model 62:411–427
Myronenko A, Song XB (2010) Point set registration: coherent point drift. IEEE Trans Pattern Anal Mach Intell 32:2262–2275
Byrd RH, Lu PH, Nocedal J, Zhu CY (1995) A limited memory algorithm for bound constrained optimization. SIAM J Sci Comput 16:1190–1208
Available: http://www.dir-lab.com/
Castillo R, Castillo E, Guerra R, Johnson VE, McPhail T, Garg AK et al (2009) A framework for evaluation of deformable image registration spatial accuracy using large landmark point sets. Phys Med Biol 54:1849–70
Castillo E, Castillo R, Martinez J, Shenoy M, Guerrero T (2010) Four-dimensional deformable image registration using trajectory modeling. Phys Med Biol 55:305–327
Hu SY, Hoffman EA, Reinhardt JM (2001) Automatic lung segmentation for accurate quantitation of volumetric X-ray CT images. IEEE Trans Med Imaging 20:490–498
Frangi AF, Niessen WJ, Vincken KL, Viergever MA (1998) Multiscale vessel enhancement filtering. Med Image Comput Comput-Assist Interv Miccai’98, 1496:130–137
Murphy K, van Ginneken B, Klein S, Staring M, de Hoop BJ, Viergever MA et al (2011) Semi-automatic construction of reference standards for evaluation of image registration. Med Image Anal 15:71–84
Available: http://stnava.github.io/ANTs/
Avants BB, Epstein CL, Grossman M, Gee JC (2008) Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med Image Anal 12:26–41
Available: https://sourceforge.net/p/advants/discussion/search/?q=btahir
Acknowledgments
This work was supported by National Natural Science Foundation of China (81000651, 81371640), Project in Technology Program in Suzhou (SH201210), and Special Project in Clinical Medicine of Jiangsu Province, China (BL2012049).
Conflict of interest
None.
Author information
Authors and Affiliations
Corresponding author
Appendix
Appendix
We provide here the calculation the gradient \(\nabla C_{\textit{SSVED}}\) as Eq. (11):
For a given control node displacement vector \(d\), if the number of control nodes is \(m,\,d=(d_1, d_2, \ldots , d_i, \ldots , d_m )\), where \(d_i =(d_{ix}, d_{iy}, d_{iz} )\) is the control node displacement component along direction \(x, y\), and \(z\). The gradient of cost function \(C_{SSVED} (U_F, U_T ;d)\) respect to \(d\) is written as:
Besides, we adopted a simple calculation method for reducing the computing time of optimization. If the direction of the components in \(T_{P}\) is inconsistent with the direction of the components in \(d_{i}\), the corresponding derivative should be zero:
Likewise, the derivative of \(T_P\) respect to displacement components along y and z direction \(\frac{\partial C_{SSVED} (U_F, U_T ;d)}{\partial d_{iy}}\) and \(\frac{\partial C_{SSVED} (U_F, U_T ;d)}{\partial d_{iz}}\) is also calculated in the same way.
Rights and permissions
About this article
Cite this article
Xia, W., Gao, X. A fast deformable registration method for 4D lung CT in hybrid framework. Int J CARS 9, 523–533 (2014). https://doi.org/10.1007/s11548-013-0960-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11548-013-0960-1