A new iterative algorithm for generating gradient directions to detect white matter fibers in brain from MRI data

TL;DR

This paper introduces an iterative algorithm that adaptively refines gradient directions to improve the accuracy of white matter fiber detection in brain MRI data, reducing angular error in fiber orientation estimation.

Contribution

The paper presents a novel iterative method for selecting gradient directions that enhances fiber orientation accuracy in MRI data, outperforming previous approaches.

Findings

Significantly reduces angular error in fiber detection.

Effective with synthetic and real MRI data, including fibers outside the XY-plane.

Performs well under various noise conditions.

Abstract

This paper proposes an iterative algorithm for choosing gradient directions use to reconstruct white matter fibers in the brain. The present study is not focusing on data acquisition where scanning is performed. The Adaptive Gradient Directions (AGD) approach is extended to refine the position and area of the grid, resulting in an admissible reduction in angular error. We begin with the gradient directions distributed uniformly inside a grid of bigger size and with larger spacing between the points. Both (size of the grid and spacing between the points) reduce iteratively. The proposed algorithm ensures that the actual position of fiber comes inside the grid at each iteration, unlike as in the AGD approach. As a result, the solution tends to actual orientation in each iteration followed by better estimation of fibers. The proposed algorithm is validated by associating it with mixture of Gaussian diffusion and mixture of non-central Wishart distribution models. The proposed approach significantly reduce the angular error for multiple computer-generated experiments on synthetic simulations and real data. Moreover, we have also performed simulations with fibers not residing in the XY-plane. For this set-up also, the proposed work outperforms, giving lesser angular error with both the models. Synthetic simulations have been performed with Rician distributed (R-D) noise of standard deviation ranging from 0.02-0.1. This work helps in better understanding of the anatomy of the brain using the MRI signal data.