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Intensity Based Methods for Brain MRI Longitudinal Registration. A Study on Multiple Sclerosis Patients


Registration is a key step in many automatic brain Magnetic Resonance Imaging (MRI) applications. In this work we focus on longitudinal registration of brain MRI for Multiple Sclerosis (MS) patients. First of all, we analyze the effect that MS lesions have on registration by synthetically eliminating some of the lesions. Our results show how a widely used method for longitudinal registration such as rigid registration is practically unconcerned by the presence of MS lesions while several non-rigid registration methods produce outputs that are significantly different. We then focus on assessing which is the best registration method for longitudinal MRI images of MS patients. In order to analyze the results obtained for all studied criteria, we use both descriptive statistics and statistical inference: one way ANOVA, pairwise t-tests and permutation tests.

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  1. Xinapse Systems, JIM software webpage,

  2. Insight Segmentation and Registration Toolkit webpage,

  3. Nifty Reg download at sourceforge,

  4. Image Registration Toolkit,

  5. DRAMMS can be downloaded at:

  6. NITRC Automatic Registration Toolbox webpage,

  7. Statistical Parameter Mapping webpage, For the computations related to this paper we used the SPM8 version.

  8. Advanced Normalization Tools webpage,

  9. We used ITK implementation for both Demons and Diffeomorphic Demons. Specifically, the Diffeomorphic demons implementation can be downloaded at See “the itk programming guide” for details on how to download the code for classical itk demons.


  • Ardekani, B.A., Guckemus, S., Bachman, A., Hoptman, M.J., Wojtaszek, M., Nierenberg, J. (2005). Quantitative comparison of algorithms for inter-subject registration of 3D volumetric brain MRI scans. NeuroImage, 142(1), 67–76.

    Google Scholar 

  • Ashburner, J. (2007). A fast diffeomorphic image registration algorithm. NeuroImage, 38(1), 95–113.

    PubMed  Article  Google Scholar 

  • Ashburner, J., & Friston, K.J. (2004). Human Brain Function, chap High-dimensional image warping, 2nd edn. (pp. 673–694). Academic Press.

  • Avants, B.B., Epstein, C.L., Grossman, M., Gee, J.C. (2008). Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Medical Image Analysis, 12(1), 26–41.

    CAS  PubMed Central  PubMed  Article  Google Scholar 

  • Cabezas, M., Oliver, A., Lladó, X., Freixenet, J., Bach Cuadra, M. (2011). A review of atlas-based segmentation for magnetic resonance brain images. Computer Methods and Programs in Biomedicine, 104(3), e158–e177.

    PubMed  Article  Google Scholar 

  • Chard, D.T., Jackson, J.S., Miller, D.H., Wheeler-Kingshott, C.A.M. (2010). Reducing the impact of white matter lesions on automated measures of brain gray and white matter volumes. Journal of Magnetic Resonance Imaging, 32(1), 223–228.

    PubMed  Article  Google Scholar 

  • Denton, E.R., Sonoda, L.I., Rueckert, D., Rankin, S.C., Hayes, C., Leach, M.O., Hill, D.L., Hawkes, D.J. (1999). Comparison and evaluation of rigid and non-rigid registration of breast MR images. Journal of Computer Assisted Tomography, 23(5), 800–805.

    CAS  PubMed  Article  Google Scholar 

  • Diez, Y., Oliver, A., Llad´o, X., Freixenet, J., Mart´ı, J., Vilanova, J.C., Martí, R. (2011). Revisiting intensity-based image registration appplied to mammography. IEEE Transactions on Information Technology in BioMedicine, 15(5), 716–725.

    PubMed  Article  Google Scholar 

  • Elliott, C., Arnold, D.L., Collins, D.L. (2013). Temporally consistent probabilistic detection of new multiple sclerosis lesions in brain mri. IEEE Transactions on Medical Imaging, 32(8), 1490–1502.

    PubMed  Article  Google Scholar 

  • García-Lorenzo, D., Francis, S., Narayanan, S., Arnold, D.L., Collins, D.L. (2013). Review of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging. Medical Image Analysis, 17(1), 1–18.

    PubMed  Article  Google Scholar 

  • Klein, A., Andersson, J., Ardekani, B.A., Ashburner, J., Avants, B.B., Chiang, M., Christensen, G.E., Collins, D.L., Gee, J., Hellier, P., Song Hyun, J., Jenkinson, M., Lepage, C., Rueckert, D., Thompson, P., Vercauteren, T., Woods, R.P., Mann, J.J., Parseya, R. (2009). Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. NeuroImage, 46(3), 786–802.

    PubMed Central  PubMed  Article  Google Scholar 

  • Liao, S., Wu, G., Shen, D. (2012). A statistical framework for inter-group image registration. Neuroinformatics, 10(4), 367–378.

    PubMed  Article  Google Scholar 

  • Liu, C., Iglesias, J.E., Tu, Z. (2013). Deformable templates guided discriminative models for robust 3d brain mri segmentation for the alzheimer’s disease neuroimaging initiative. Neuroinformatics, 11(4), 447–468.

    PubMed  Article  Google Scholar 

  • Lladó, X., Ganiler, O., Oliver, A., Martí, R., Freixenet, J., Valls, L., Rovira A (2012a). Automated detection of multiple sclerosis lesions in serial brain MRI. Neuroradiology, 54(8), 787–807.

    Article  Google Scholar 

  • Lladó, X., Oliver, A., Cabezas, M., Freixenet, J., Vilanova, J.C., Quiles, A., Valls, L., Ramió-Torrentà, L., Rovira, A. (2012b). Segmentation of multiple sclerosis lesions in brain MRI: a review of automated approaches. Information Sciences, 186(1), 164–185.

    Article  Google Scholar 

  • Menke, J., & Martinez, T. (2004). Using permutations instead of student’s t distribution for p-values in paired difference algorithm comparisons. In Proceedings IEEE international joint conference on neural networks (pp. 1331–1335).

  • Modat, M., Ridgway, G.R., Taylor, Z.A., Lehmann, M., Barnes, J., Hawkes, D.J., Fox, N.C., Ourselin, S. (2010). Fast free-form deformation using graphics processing units. Computer Methods and Programs in Biomedicine, 98(3), 278–284.

    PubMed  Article  Google Scholar 

  • Moraal, B., Meier, D.S., Poppe, P.A., Geurts, J.J., Vrenken, H., Jonker, W.M., Knol, D.L., van Schijndel, R.A., Pouwels, P.J., Pohl, C., Bauer, L., Sandbrink, R., Guttman, C.R., Barkhof, F. (2009). Subtraction mr images in a multiple sclerosis multicenter clinical trial setting. Radiology, 250(2), 506–514.

    PubMed Central  PubMed  Article  Google Scholar 

  • Moraal, B.,Wattjes, M.P., Geurts, J.J., Knol, D.L., van Schijndel, R.A., Pouwels, P.J., Vrenken, H., Barkhof, F. (2010). Improved detection of active multiple sclerosis lesions: 3d subtraction imaging. Radiology, 255(1), 154–163.

    PubMed  Article  Google Scholar 

  • Ou, Y., Sotiras, A., Paragios, N., Davatzikos, C. (2011). Dramms: deformable registration via attribute matching and mutual-saliency weighting. Medical Image Analysis, 15(4), 622–639.

    PubMed Central  PubMed  Article  Google Scholar 

  • Parisot, S., Duffau, H., Chemouny, S., Paragios, N. (2012). Joint tumor segmentation and dense deformable registration of brain MR images. In Proceedings medical image computing and computer assisted intervention (pp. 651–658).

  • Prados, F., Boada, I., Feixas, M., Prats-Galino, A., Blasco, G., Puig, J., Pedraza, S. (2012). Information-theoretic approach for automated white matter fiber tracts reconstruction. Neuroinformatics, 10(3), 305–318.

    PubMed  Article  Google Scholar 

  • Rohlfing, T. (2012). Image similarity and tissue overlaps as surrogates for image registration accuracy: widely used but unreliable. IEEE Transactions on Medical Imaging, 31(2), 153–163.

    PubMed Central  PubMed  Article  Google Scholar 

  • Rueckert, D., Sonoda, L.I., Hayes, C., Hill, D.L., Leach, M.O., Hawkes, D.J. (1999). Non-rigid registration using free-form deformations: application to breast MR images. IEEE Transactions on Medical Imaging, 18(8), 712–721.

    CAS  PubMed  Article  Google Scholar 

  • Rueckert, D., Aljabar, P., Heckemann, R.A., Hajnal, J.V., Hammers, A. (2006). Diffeomorphic registration using B-splines. In Proceedings medical image computing and computer assisted intervention (pp. 702–709).

  • Schnabel, J., Rueckert, D., Quist, M., Blackall, J., Castellano-Smith, A., Hartkens, T., Penney, G., Hall, W., Liu, H., Truwit, C., Gerritsen, F., Hill, D., Hawkes, D.J. (2001). A generic framework for non-rigid registration based on non-uniform multi-level free-form deformations. In Proceedings medical image computing and computer assisted intervention (pp. 573–581).

  • Sdika, M., & Pelletier, D. (2009). Nonrigid registration of multiple sclerosis brain images using lesion inpainting for morphometry or lesion mapping. Human Brain Mapping, 30(4), 1060–1067.

    PubMed  Article  Google Scholar 

  • Shah, M., Xiao, Y., Subbanna, N., Francis, S., Arnold, D.L., Arbel, T. (2011). Evaluating intensity normalization on MRIs of human brain with multiple sclerosis. Medical Image Analysis, 15(2), 267–282.

    PubMed  Article  Google Scholar 

  • Shi, W., Zhuang, X., Pizarro, L., Bai, W., Wang, H., Tung, K., Edwards, P., Rueckert, D. (2012). Registration using sparse free-form deformations. In Proceedings medical image computing and computer assisted intervention (pp. 659–666).

  • Thirion, J.P. (1996). Non-rigid matching using demons. In Proceedings IEEE conference on computer vision and pattern recognition (pp. 245–261).

  • Vercauteren, T., Pennec, X., Perchant, A., Ayache, N. (2009). Diffeomorphic demons: efficient non-parametric image registration. NeuroImage, 45(1 (S1)), S61–S72.

    PubMed  Article  Google Scholar 

  • Wang, S., Summers, R.M., (2012). Machine learning and radiology. Medical Image Analysis, 16(5), 933–951.

    CAS  PubMed Central  PubMed  Article  Google Scholar 

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We would like to thank to all the authors that have provided public registration algorithms. Moreover, we would like to specially thank the collaborators from University College London that provided us with the MS lesion filling software. This work has been supported by the Instituto de Salud Carlos III Grant PI09/91018, Grant VALTEC09-1-0025 from the Generalitat de Catalunya, and Grant CEM-Cat 2011 from the Fundació Esclerosi Múltiple. S. Valverde holds a FI-DGR2013 Grant from the Generalitat de Catalunya.

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Correspondence to Arnau Oliver.

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Diez, Y., Oliver, A., Cabezas, M. et al. Intensity Based Methods for Brain MRI Longitudinal Registration. A Study on Multiple Sclerosis Patients. Neuroinform 12, 365–379 (2014).

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  • Brain MRI
  • Longitudinal analysis
  • Multiple sclerosis
  • Registration