  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.
