Abstract
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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Notes
Xinapse Systems, JIM software webpage, http://www.xinapse.com/home.php.
Insight Segmentation and Registration Toolkit webpage, http://www.itk.org/.
Nifty Reg download at sourceforge, http://sourceforge.net/projects/niftyreg/.
Image Registration Toolkit, http://www.doc.ic.ac.uk/%7Edr/software/index.html.
DRAMMS can be downloaded at: http://www.rad.upenn.edu/sbia/software/dramms/download.html
NITRC Automatic Registration Toolbox webpage, http://www.nitrc.org/projects/art/.
Statistical Parameter Mapping webpage, http://www.fil.ion.ucl.ac.uk/spm/ For the computations related to this paper we used the SPM8 version.
Advanced Normalization Tools webpage, http://www.picsl.upenn.edu/ANTS/download.php.
We used ITK implementation for both Demons and Diffeomorphic Demons. Specifically, the Diffeomorphic demons implementation can be downloaded at http://www.insight-journal.org/browse/publication/154/. See “the itk programming guide” for details on how to download the code for classical itk demons.
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Acknowledgments
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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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). https://doi.org/10.1007/s12021-013-9216-z
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DOI: https://doi.org/10.1007/s12021-013-9216-z