An anisotropy preserving metric for DTI processing
This work addresses a domain-specific problem in medical imaging for researchers and practitioners, offering an incremental improvement over existing Riemannian methods by focusing on anisotropy preservation.
The paper tackles the challenge of statistical analysis in Diffusion Tensor Imaging (DTI) by proposing a novel metric and computational framework that preserves anisotropy during interpolation, a key feature of diffusion tensor data, while maintaining geometry and computational tractability.
Statistical analysis of Diffusion Tensor Imaging (DTI) data requires a computational framework that is both numerically tractable (to account for the high dimensional nature of the data) and geometric (to account for the nonlinear nature of diffusion tensors). Building upon earlier studies that have shown that a Riemannian framework is appropriate to address these challenges, the present paper proposes a novel metric and an accompanying computational framework for DTI data processing. The proposed metric retains the geometry and the computational tractability of earlier methods grounded in the affine invariant metric. In addition, and in contrast to earlier methods, it provides an interpolation method which preserves anisotropy, a central information carried by diffusion tensor data.