Rank-2 model-order selection in diffusion tensor MRI: Infromation complexity based on the total Kullback-Leibler divergence
Diffusion-weighted MRI (DW-MRI) can assess the integrity of white matter (WM) structures in the human brain. Multi-compartment analysis of DW-MRI requires an estimate of the number of compartments to permit unbiased estimation of the diffusion shape in a single fibers as well as crossing fascicles. We propose a new, rotation-invariant measure to assess the suitability of a model by a measure for information complexity (ICOMP) based on the total Kullback-Leibler divergence (TKLD). ICOMP-TKLD is evaluated on simulated data and on data from the Human Connectome Project. Compared to the state-of-the-art, ICOMP-TKLD is the only method that yields reliable model-order selection in both homogeneous and heterogeneous WM regions. Therefore, ICOM-TKLD may open the way for structure-adaptive estimation of diffusion properties of the entire brain.
|DTI, model selection|
|12th IEEE International Symposium on Biomedical Imaging, ISBI 2015|
|Organisation||Department of Biomedical Engineering|
Yang, J, Poot, D.H.J, Caan, M.W.A, Vos, F, & van Vliet, L.J. (2015). Rank-2 model-order selection in diffusion tensor MRI: Infromation complexity based on the total Kullback-Leibler divergence. Presented at the 12th IEEE International Symposium on Biomedical Imaging, ISBI 2015. doi:10.1109/ISBI.2015.7164022