A New Approach in MRI Signal Processing for Detecting the Axonal Organization of the Brain

TL;DR

This paper presents a novel MRI signal processing method that improves brain white matter fiber pathway reconstruction by selecting diffusion directions based on signal diminishment, reducing redundancy and computational constraints of existing schemes.

Contribution

The proposed approach introduces a new way to select gradient directions in diffusion MRI, overcoming limitations of redundancy and computational cost in current methods.

Findings

Effective in reducing gradient direction redundancy

Decreases computational cost compared to existing methods

Successfully validated on simulation and real data

Abstract

This article introduces a new methodology for reconstructing the white matter fiber pathways of brain in diffusion MRI. Usually, the signal intensity values will be lesser in the direction of higher diffusivity. The proposed approach picks the diffusion sensitivity gradient directions (dSGD), where the signal intensities are diminutive. Considering these as the directions of maximum diffusivity, we generate directions uniformly distributed around the picked dSGD. These newly computed uniformly spaced directions are considered gradient directions used in the reconstruction process. The state-of-art schemes like uniform gradient direction (UGD) have redundancy in the gradient direction, and adaptive gradient direction (AGD) has a constraint of solving linear system twice per voxel. These two limitations are turned down in this study simultaneously. Estimating gradient directions with the proposed scheme is employed in the multi-compartmental mixture models for calculating the fiber orientations. Simulation and experiments on the real data evaluate the feasibility of the proposed method.