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Issue No.11 - Nov. (2013 vol.35)
pp: 2638-2650
L. Cordero-Grande , Dept. of Teor. de la Senal y Comun. e Ing. Telematica, Univ. de Valladolid, Valladolid, Spain
S. Merino-Caviedes , Dept. of Teor. de la Senal y Comun. e Ing. Telematica, Univ. de Valladolid, Valladolid, Spain
S. Aja-Fernandez , Dept. of Teor. de la Senal y Comun. e Ing. Telematica, Univ. de Valladolid, Valladolid, Spain
C. Alberola-Lopez , Dept. of Teor. de la Senal y Comun. e Ing. Telematica, Univ. de Valladolid, Valladolid, Spain
This paper proposes a methodology for the joint alignment of a sequence of images based on a groupwise registration procedure by using a new family of metrics that exploit the expected sparseness of the temporal intensity curves corresponding to the aligned points. Therefore, this methodology is able to tackle the alignment of temporal sequences of images in which the represented phenomenon varies in time. Specifically, we have applied it to the correction of motion in contrast-enhanced first-pass perfusion cardiac magnetic resonance images. The time sequence is elastically registered as a whole by using the aforementioned family of multi-image metrics and jointly optimizing the parameters of the transformations involved. The proposed metrics are able to cope with dynamic changes in the intensity content of corresponding points in the sequence guided by the assumption that these changes allow for a sparse representation in a properly selected frame. Results have shown the statistically significant improvement in the performance of the proposed metric with respect to previous groupwise registration metrics for the problem at hand, which is especially relevant to correct for elastic deformations.
Measurement, Entropy, Myocardium, Image resolution, Vectors, Feature extraction, Magnetic resonance,myocardial perfusion, Groupwise elastic registration, registration metric, sparseness, cardiac magnetic resonance
L. Cordero-Grande, S. Merino-Caviedes, S. Aja-Fernandez, C. Alberola-Lopez, "Groupwise Elastic Registration by a New Sparsity-Promoting Metric: Application to the Alignment of Cardiac Magnetic Resonance Perfusion Images", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.35, no. 11, pp. 2638-2650, Nov. 2013, doi:10.1109/TPAMI.2013.74
[1] A. Sotiras and N. Paragios, "Deformable Image Registration: A Survey," Technical Report 7971, INRIA, , Mar. 2012.
[2] V. Gupta, H.A. Kirisli, E.A. Hendriks, R.J. van der Geest, M. van de Giessen, W. Niessen, J.H.C. Reiber, and B.P.F. Lelieveldt, "Cardiac MR Perfusion Image Processing Techniques: A Survey," Medical Image Analysis, vol. 16, no. 4, pp. 767-785, 2012.
[3] A.D. Scott, J. Keegan, and D.N. Firmin, "Motion in Cardiovascular MR Imaging," Radiology, vol. 250, no. 2, pp. 331-351, 2009.
[4] T. Shin, K.S. Nayak, J.M. Santos, D.G. Nishimura, B.S. Hu, and M.V. McConnell, "Three-Dimensional First-Pass Myocardial Perfusion MRI Using a Stack-of-Spirals Acquisition," Magnetic Resonance in Medicine, vol. 69, no. 3, pp. 839-844, 2013.
[5] J.P.W. Pluim, J.B.A. Maintz, and M.A. Viergever, "Mutual-Information-Based Registration of Medical Images: A Survey," IEEE Trans. Medical Imaging, vol. 22, no. 8, pp. 986-1004, Aug. 2003.
[6] A. Myronenko and X. Song, "Intensity-Based Image Registration by Minimizing Residual Complexity," IEEE Trans. Medical Imaging, vol. 29, no. 11, pp. 1882-1891, Nov. 2010.
[7] L. Zöllei, "A Unified Information Theoretic Framework for Pair- and Group-Wise Registration of Medical Images," PhD dissertation, Massachusetts Inst. of Technology, edu/lzollei/research/ thesislzollei-thesis06-al l-corr.pdf , 2006.
[8] C. Wachinger and N. Navab, "Simultaneous Registration of Multiple Images: Similarity Metrics and Efficient Optimization," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 35, no. 5, pp. 1221-1233, May 2013.
[9] L.M. Bidaut and J.-P. Vallée, "Automated Registration of Dynamic MR Images for the Quantification of Myocardial Perfusion," J. Magnetic Resonance Imaging, vol. 13, pp. 648-655, 2001.
[10] C. Dornier, M.K. Ivancevic, P. Thevenaz, and J.-P. Vallée, "Improvement in the Quantification of Myocardial Perfusion Using an Automatic Spline-Based Registration Algorithm," J. Magnetic Resonance Imaging, vol. 18, pp. 160-168, 2003.
[11] S.N. Gupta, M. Solaiyappan, G.M. Beache, A.E. Arai, and T.K.F. Foo, "Fast Method for Correcting Image Misregistration Due to Organ Motion in Time-Series MRI Data," Magnetic Resonance in Medicine, vol. 49, no. 3, pp. 506-514, 2003.
[12] K.K. Wong, E.S. Yang, E.X. Wu, H.-F. Tse, and S.T. Wong, "First-Pass Myocardial Perfusion Image Registration by Maximization of Normalized Mutual Information," J. Magnetic Resonance Imaging, vol. 27, pp. 529-537, 2008.
[13] G. Wollny, M. Ledesma-Carbayo, P. Kellman, and A. Santos, "Exploiting Quasiperiodicity in Motion Correction of Free-Breathing Myocardial Perfusion MRI," IEEE Trans. Medical Imaging, vol. 29, no. 8, pp. 1516-1527, Aug. 2010.
[14] A. Melbourne, D. Atkinson, M.J. White, D. Collins, M. Leach, and D. Hawkes, "Registration of Dynamic Contrast-Enhanced MRI Using a Progressive Principal Component Registration (PPCR)," Physics in Medicine and Biology, vol. 52, no. 5, pp. 5147-5156, 2007.
[15] J. Milles, R.J. van der Geest, M. Jerosch-Herold, J. Reiber, and B. Lelieveldt, "Fully Automated Motion Correction in First-Pass Myocardial Perfusion MR Image Sequences," IEEE Trans. Medical Imaging, vol. 27, no. 11, pp. 1611-1621, Nov. 2008.
[16] G. Wollny, P. Kellman, A. Santos, and M.J. Ledesma-Carbayo, "Automatic Motion Compensation of Free Breathing Acquired Myocardial Perfusion Data by Using Independent Component Analysis," Medical Image Analysis, vol. 16, no. 5, pp. 1015-1028, 2012.
[17] C. Li, Y. Sun, and P. Chai, "Pseudo Ground Truth Based Nonrigid Registration of Myocardial Perfusion MRI," Medical Image Analysis, vol. 15, no. 4, pp. 449-459, 2011.
[18] S. Hamrouni, N. Rougon, and F. Prêteux, "Multi-Feature Information-Theoretic Image Registration: Application to Groupwise Registration of Perfusion MRI Exams," Proc. IEEE Int'l Symp. Biomedical Imaging: Nano to Macro, pp. 574-577, 2011.
[19] K.K. Bhatia, J.V. Hajnal, B.K. Puri, A.D. Edwards, and D. Rueckert, "Consistent Groupwise Non-Rigid Registration for Atlas Construction," Proc. IEEE Int'l Symp. Biomedical Imaging: Nano to Macro, pp. 908-911, 2004.
[20] S. Joshi, B. Davis, M. Jomier, and G. Gerig, "Unbiased Diffeomorphic Atlas Construction for Computational Anatomy," NeuroImage, vol. 23, pp. 151-160, 2004.
[21] E.G. Learned-Miller, "Data Driven Image Models through Continuous Joint Alignment," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 28, no. 2, pp. 236-250, Feb. 2006.
[22] W.M. WellsIII, P. Viola, H. Atsumi, S. Nakajima, and R. Kikinis, "Multi-Modal Volume Registration by Maximization of Mutual Information," Medical Image Analysis, vol. 1, no. 1, pp. 35-51, 1996.
[23] C. Wachinger and N. Navab, "Entropy and Laplacian Images: Structural Representations for Multi-Modal Registration," Medical Image Analysis, vol. 16, no. 1, pp. 1-17, 2012.
[24] N. Hurley and S. Rickard, "Comparing Measures of Sparsity," IEEE Trans. Information Theory, vol. 55, no. 10, pp. 4723-4741, Oct. 2009.
[25] K. Kreutz-Delgado and B.D. Rao, "A General Approach to Sparse Basis Selection: Majorization, Concavity, and Affine Scaling," Technical Report UCSD-CIE-97-7-1, July 1997.
[26] S. Mallat, A Wavelet Tour of Signal Processing. Academic Press, 1999.
[27] L. Cordero-Grande, G. Vegas-Sánchez-Ferrero, P.C. de-la Higuera, J.A. San-Román-Calvar, A. Revilla-Orodea, M. Martín-Fernández, and C. Alberola-López, "Unsupervised 4D Myocardium Segmentation with a Markov Random Field Based Deformable Model," Medical Image Analysis, vol. 15, no. 3, pp. 283-301, 2011.
[28] D. Rueckert, L.I. Sonoda, C. Hayes, D.L.G. Hill, M.O. Leach, and D.J. Hawkes, "Nonrigid Registration Using Free-Form Deformations: Application to Breast MR Images," IEEE Trans. Medical Imaging, vol. 18, no. 8, pp. 712-721, Aug. 1999.
[29] S.K. Balci, P. Golland, and W.M. Wells, "Non-Rigid Groupwise Registration Using B-Spline Deformation Model," The Insight J., vol. 10, pp. 105-121,, 2007.
[30] R. Tamburo, "Entropy Image Filter," The VTK J., 2011.
[31] H. Wang and A.A. Amini, "Cardiac Motion and Deformation Recovery from MRI: A Review," IEEE Trans. Medical Imaging, vol. 31, no. 2, pp. 487-503, Feb. 2012.
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