Construction of An Unbiased Spatio-Temporal Atlas of the Tongue During Speech
Quantitative characterization and comparison of tongue motion during speech and swallowing present fundamental challenges because of striking variations in tongue structure and motion across subjects. A reliable and objective description of the dynamics tongue motion requires the consistent integration of inter-subject variability to detect the subtle changes in populations. To this end, in this work, we present an approach to constructing an unbiased spatio-temporal atlas of the tongue during speech for the first time, based on cine-MRI from twenty two normal subjects. First, we create a common spatial space using images from the reference time frame, a neutral position, in which the unbiased spatio-temporal atlas can be created. Second, we transport images from all time frames of all subjects into this common space via the single transformation. Third, we construct atlases for each time frame via groupwise diffeomorphic registration, which serves as the initial spatio-temporal atlas. Fourth, we update the spatio-temporal atlas by realigning each time sequence based on the Lipschitz norm on diffeomorphisms between each subject and the initial atlas. We evaluate and compare different configurations such as similarity measures to build the atlas. Our proposed method permits to accurately and objectively explain the main pattern of tongue surface motion.
KeywordsSpatio-temporal atlas MRI Speech Motion
We thank reviewers for their comments. This work is supported by NIH/NIDCD R00DC012575.
- 1.Harandia, N.M., Abugharbieh, R., Fels, S.: 3D segmentation of the tongue in MRI: a minimally interactive model-based approach. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, pp. 1–11 (2014)Google Scholar
- 4.Woo, J., Xing, F., Lee, J., Stone, M., Prince, J.: Determining functional units of tongue motion via graph-regularized sparse non-negative matrix factorization. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 146–153, Boston, MA (2014)Google Scholar
- 9.De Craene, M., Piella, G., Camara, O., Duchateau, N., Silva, E., Doltra, A., D’hooge, J., Brugada, J., Sitges, M., Frangi, A.: Temporal diffeomorphic free-form deformation: application to motion and strain estimation from 3D echocardiography. Med. Image Anal 16(2), 427–450 (2011)CrossRefGoogle Scholar
- 10.Woo, J., Lee, J., Murano, E., Xing, F., Meena, A., Stone, M., Prince, J.: A high-resolution atlas and statistical model of the vocal tract from structural MRI. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, pp. 1–14 (2014)Google Scholar
- 11.Gholipour, A., Limperopoulos, C., Clancy, S., Clouchoux, C., Akhondi-Asl, A., Estroff, J.A., Warfield, S.K.: Construction of a deformable spatiotemporal MRI atlas of the fetal brain: evaluation of similarity metrics and deformation models. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 292–299, Boston, MA (2014)Google Scholar
- 13.Durrleman, S., Pennec, X., Gerig, G., Trouve, A., Ayache, N.: Spatiotemporal atlas estimation for developmental delay detection in longitudinal datasets. In: International Conference on Medical Image Computing and Computer-Assisted Intervention 59(2), pp. 297–304 (2009)Google Scholar
- 14.Lorenzi, M., Ayache, N., Pennec, X.: Schild’s ladder for the parallel transport of deformations in time series of images. Inf Process Med Imaging 22, 463–474 (2011)Google Scholar
- 18.Tustison, N., Avants, B.B.: Explicit B-spline regularization in diffeomorphic image registration. Frontiers in Neuroinformatics 7(39), 1–13 (2013)Google Scholar