Mr. Jianfei Yang Profile

Jianfei Yang

at Technische Univ Delft

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Proceedings Article | 20 March 2015 Paper

Estimating diffusion properties in complex fiber configurations based on structure-adaptive multi-valued tensor-field filtering

Jianfei Yang, Dirk H. Poot, Georgius A. Arkesteijn, Matthan Caan, Lucas van Vliet, Frans Vos

Proceedings Volume 9413, 941320 (2015) https://doi.org/10.1117/12.2080759

KEYWORDS: Diffusion, Data modeling, Structured optical fibers, Optical filters, Statistical analysis, Autoregressive models, Denoising, Diffusion weighted imaging, Diffusion tensor imaging, Image filtering

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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.

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