Estimating diffusion properties in complex fiber configurations based on structure-adaptive multi-valued tensor-field filtering
Conventionally, a single rank-2 tensor is used to assess the white matter integrity in diffusion imaging of the human brain. However, a single tensor fails to describe the diffusion in fiber crossings. Although a dual tensor model is able to do so, the low signal-to-noise ratio hampers reliable parameter estimation as the number of parameters is doubled. We present a framework for structure-adaptive tensor field filtering to enhance the statistical analysis in complex fiber structures. In our framework, a tensor model will be fitted based on an automated relevance determination method. Particularly, a single tensor model is applied to voxels in which the data seems to represent a single fiber and a dualtensor model to voxels appearing to contain crossing fibers. To improve the estimation of the model parameters we propose a structure-adaptive tensor filter that is applied to tensors belonging to the same fiber compartment only. It is demonstrated that the structure-adaptive tensor-field filter improves the continuity and regularity of the estimated tensor field. It outperforms an existing denoising approach called LMMSE, which is applied to the diffusion-weighted images. Track-based spatial statistics analysis of fiber-specific FA maps show that the method sustains the detection of more subtle changes in white matter tracts than the classical single-tensor-based analysis. Thus, the filter enhances the applicability of the dual-tensor model in diffusion imaging research. Specifically, the reliable estimation of two tensor diffusion properties facilitates fiber-specific extraction of diffusion features.
|, , ,|
|Medical Imaging 2015: Image Processing|
|Organisation||Biomedical Imaging Group Rotterdam|
Yang, J, Poot, D.H.J, Arkesteijn, G.A.M, Caan, M.W.A, van Vliet, L.J, & Vos, F. (2015). Estimating diffusion properties in complex fiber configurations based on structure-adaptive multi-valued tensor-field filtering. Presented at the Medical Imaging 2015: Image Processing. doi:10.1117/12.2080759