Abstract
Cross-subject image registration is the building block for many cardiac studies. In the literature, it is often handled by voxel-wise registration methods. However, studies are lacking to show which methods are more accurate and stable in this context. Aiming at answering this question, this paper evaluates 12 popular registration methods and validates a recently developed method DRAMMS [16] in the context of cross-subject cardiac registration. Our dataset consists of short-axis end-diastole cardiac MR images from 24 subjects, in which non-cardiac structures are removed. Each registration method was applied to all 552 image pairs. Registration accuracy is approximated by Jaccard overlap between deformed expert annotation of source image and the corresponding expert annotation of target image. This accuracy surrogate is further correlated with deformation aggressiveness, which is reflected by minimum, maximum and range of Jacobian determinants. Our study shows that DRAMMS [16] scores high in accuracy and well balances accuracy and aggressiveness in this dataset, followed by ANTs [13], MI-FFD [14], Demons [15], and ART [12]. Our findings in cross-subject cardiac registrations echo those findings in brain image registrations [7].
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References
Chandrashekara, R., Rao, A., Sanchez-Ortiz, G.I., Mohiaddin, R.H., Rueckert, D.: Construction of a Statistical Model for Cardiac Motion Analysis Using Nonrigid Image Registration. In: Taylor, C.J., Noble, J.A. (eds.) IPMI 2003. LNCS, vol. 2732, pp. 599–610. Springer, Heidelberg (2003)
Isola, A., Grass, M., Niessen, W.J.: Fully automatic nonrigid registration-based local motion estimation for motion-corrected iterative cardiac CT reconstruction. Med. Phy., 1093–1109 (2010)
Perperidis, D., Mohiaddin, R., Rueckert, D.: Spatio-temporal free-form registration of cardiac MR image sequences. MedIA 9, 441–456 (2005)
Zhuang, X., Rhode, K.S., Razavi, R.S., Hawkes, D.J., Ourselin, S.: A Registration-Based Propagation Framework for Automatic Whole Heart Segmentation of Cardiac MRI. TMI, 1612–1625 (2010)
Ye, D.H., Litt, H., Davatzikos, C., Pohl, K.M.: Morphological Classification: Application to Cardiac MRI of Tetralogy of Fallot. In: Metaxas, D.N., Axel, L. (eds.) FIMH 2011. LNCS, vol. 6666, pp. 180–187. Springer, Heidelberg (2011)
Makela, T., Clarysse, P., Sipila, O., Pauna, N., Pham, Q., Katila, T., Magnin, I.E., Axis, L.L.: A review of cardiac image registration methods. TMI 21, 1011–1021 (2002)
Klein, A., et al.: Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. NeuroImage 46, 786–802 (2009)
Murphy, K., van Ginneken, B., Reinhardt, J.M., et al.: Evaluation of Registration Methods on Thoracic CT: The EMPIRE10 Challenge. TMI 30, 1901–1920 (2011)
Jenkinson, M., Smith, S.: A global optimisation method for robust affine registration of brain images. MedIA 5(2), 143–156 (2001)
Andersson, J., Smith, S., Jenkinson, M.: FNIRT–FMRIB’s non-linear image registration tool. Human Brain Mapping (2008)
Woods, R., Grafton, S., Holmes, C., Cherry, S., Mazziotta, J.: Automated image registration: I. general methods and intrasubject intramodality validation. JCAT, 139–152 (1998)
Ardekani, B., Guckemus, S., Bachman, A., Hoptman, M.J., Wojtaszek, M., Nierenberg, J.: Quantitative comparison of algorithms for inter-subject registration of 3D volumetric brain MRI scans. J. Neu. Methods. 142, 67–76 (2005)
Avants, B., Epstein, C.L., Grossman, M., Gee, J.C.: Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. MedIA 12, 26–41 (2008)
Rueckert, D., Sonoda, L.I., Hayes, C., Hill, D.L.G., Leach, M.O., Hawkes, D.J.: Nonrigid registration using free-form deformations: application to breast MR images. TMI 18, 712–721 (1999)
Vercauteren, T., Pennec, X., Perchant, A., Ayache, N.: Diffeomorphic demons: Efficient nonparametric image registration. NeuroImage 45(1), 61–72 (2009)
Ou, Y., Sotiras, A., Paragios, N., Davatzikos, C.: DRAMMS: Deformable Registration via Attribute Matching and Mutual-Saliency Weighting. MedIA, 622–639 (2011)
Glocker, B., Komodakis, N., Tziritas, G., Navab, N., Paragios, N.: Dense image registration through MRFs and efficient linear programming. MedIA, 731–741 (2008)
Heiberg, E., Sjogren, J., Ugander, M., Carlsson, M., Engblom, H., Arheden, H.: Design and Validation of Segment - a Freely Available Software for Cardiovascular Image Analysis. BMC Medical Imaging 10, 1 (2010)
Zhang, H., Wahle, A., Johnson, R., Scholz, T., Sonka, M.: 4D Cardiac MR Image Analysis: Left and Right Ventricular Morphology and Function. TMI, 350–364 (2010)
Sled, J.G., Zijdenbos, A.P., Evans, A.C.: A nonparametric method for automatic correction of intensity nonuniformity in MRI data. TMI 17(1), 87–97 (1998)
Christensen, G.E., Geng, X., Kuhl, J.G., Bruss, J., Grabowski, T.J., Pirwani, I.A., Vannier, M.W., Allen, J.S., Damasio, H.: Introduction to the Non-rigid Image Registration Evaluation Project (NIREP). In: Pluim, J.P.W., Likar, B., Gerritsen, F.A. (eds.) WBIR 2006. LNCS, vol. 4057, pp. 128–135. Springer, Heidelberg (2006)
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Ou, Y., Ye, D.H., Pohl, K.M., Davatzikos, C. (2012). Validation of DRAMMS among 12 Popular Methods in Cross-Subject Cardiac MRI Registration. In: Dawant, B.M., Christensen, G.E., Fitzpatrick, J.M., Rueckert, D. (eds) Biomedical Image Registration. WBIR 2012. Lecture Notes in Computer Science, vol 7359. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31340-0_22
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DOI: https://doi.org/10.1007/978-3-642-31340-0_22
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