Diffusion imaging is a noninvasive tool for probing the microstructure of fibrous nerve and muscle tissue. Higher-order tensors provide a powerful mathematical language to model and analyze the large and complex data that is generated by its modern variants such as High Angular Resolution Diffusion Imaging (HARDI) or Diffusional Kurtosis Imaging. This survey gives a careful introduction to the foundations of higher-order tensor algebra, and explains how some concepts from linear algebra generalize to the higher-order case. From the application side, it reviews a variety of distinct higher-order tensor models that arise in the context of diffusion imaging, such as higher-order diffusion tensors, q-ball or fiber Orientation Distribution Functions (ODFs), and fourth-order covariance and kurtosis tensors. By bridging the gap between mathematical foundations and application, it provides an introduction that is suitable for practitioners and applied mathematicians alike, and propels the field by stimulating further exchange between the two.
|Title of host publication||Visualization and Processing of Tensors and Higher Order Descriptors for Multi-Valued Data, Part III|
|Editors||C.-F. Westin, A. Vilanova, B. Burgeth|
|Publication status||Published - 2014|
|Name||Mathematics and Visualization|
Schultz, T., Fuster, A., Ghosh, A., Deriche, R., Florack, L. M. J., & Lim, L. H. (2014). Higher-order tensors in diffusion imaging. In C-F. Westin, A. Vilanova, & B. Burgeth (Eds.), Visualization and Processing of Tensors and Higher Order Descriptors for Multi-Valued Data, Part III (pp. 129-161). (Mathematics and Visualization). Springer. https://doi.org/10.1007/978-3-642-54301-2_6