Estimation of Fiber Orientations Using Neighborhood Information

TL;DR

This paper introduces FORNI, a novel method for estimating fiber orientations in diffusion MRI that leverages neighborhood information to enhance accuracy and robustness against noise, outperforming existing techniques.

Contribution

The paper presents a new FO estimation method that incorporates spatial coherence via weighted l1 regularization and uses a fixed tensor basis for explicit directional modeling.

Findings

FORNI reduces noise effects in FO estimation.

Improves accuracy over state-of-the-art algorithms.

Effective on digital phantoms and real brain data.

Abstract

Data from diffusion magnetic resonance imaging (dMRI) can be used to reconstruct fiber tracts, for example, in muscle and white matter. Estimation of fiber orientations (FOs) is a crucial step in the reconstruction process and these estimates can be corrupted by noise. In this paper, a new method called Fiber Orientation Reconstruction using Neighborhood Information (FORNI) is described and shown to reduce the effects of noise and improve FO estimation performance by incorporating spatial consistency. FORNI uses a fixed tensor basis to model the diffusion weighted signals, which has the advantage of providing an explicit relationship between the basis vectors and the FOs. FO spatial coherence is encouraged using weighted l1-norm regularization terms, which contain the interaction of directional information between neighbor voxels. Data fidelity is encouraged using a squared error between the observed and reconstructed diffusion weighted signals. After appropriate weighting of these competing objectives, the resulting objective function is minimized using a block coordinate descent algorithm, and a straightforward parallelization strategy is used to speed up processing. Experiments were performed on a digital crossing phantom, ex vivo tongue dMRI data, and in vivo brain dMRI data for both qualitative and quantitative evaluation. The results demonstrate that FORNI improves the quality of FO estimation over other state of the art algorithms.