Abstract
Spatial normalization is one of the most important steps in population based statistical analysis of brain images. This involves normalizing all the brain images to a pre-defined template or a population specific template. With multiple emerging imaging modalities, it is quintessential to develop a method for building a joint template that is a statistical representation of the given population across different modalities. It is possible to create different population specific templates in different modalities using existing methods. However, they do not give an opportunity for voxelwise comparison of different modalities. A multimodal brain template with probabilistic region of interest (ROI) definitions will give opportunity for multivariate statistical frameworks for better understanding of brain diseases. In this paper, we propose a methodology for developing such a multimodal brain atlas using the anatomical T1 images and the diffusion tensor images (DTI), along with an automated workflow to probabilistically define the different white matter regions on the population specific multimodal template. The method will be useful to carry out ROI based statistics across different modalities even in the absence of expert segmentation. We show the effectiveness of such a template using voxelwise multivariate statistical analysis on population based group studies on HIV/AIDS patients.
Vikash Gupta is presently at Imaging Genetics Center, University of Southern California, Los Angeles, CA, USA
Grégoire Malandain is presently at MORPHENE team, INRIA Sophia-Antipolis, Sophia Antipolis, France
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- 1.
The ICBM family of templates are available for download at http://www.loni.usc.edu/atlases/Atlas_Detail.php?atlas_id=5.
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Acknowledgements
We would like to acknowledge Nice University Hospital (CHU) and the NEURADAPT study for collecting the data. The work was partly supported by the European Research Council through the ERC Advanced Grant MedYMA 2011-291080.
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Gupta, V., Malandain, G., Ayache, N., Pennec, X. (2016). A Framework for Creating Population Specific Multimodal Brain Atlas Using Clinical T1 and Diffusion Tensor Images. In: Fuster, A., Ghosh, A., Kaden, E., Rathi, Y., Reisert, M. (eds) Computational Diffusion MRI. Mathematics and Visualization. Springer, Cham. https://doi.org/10.1007/978-3-319-28588-7_9
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DOI: https://doi.org/10.1007/978-3-319-28588-7_9
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