# Fiber Orientation Estimation Guided by a Deep Network

## TL;DR

This paper introduces FORDN, a deep network-guided algorithm for estimating fiber orientations in diffusion MRI, improving accuracy in complex regions by combining coarse and dense dictionary-based reconstructions.

## Contribution

The paper presents a novel two-step FO estimation method that integrates deep learning with dictionary-based sparse reconstruction, reducing computational cost and enhancing accuracy.

## Key findings

- Outperforms state-of-the-art FO estimation algorithms.
- Effective in complex fiber configurations and noisy data.
- Reduces computational cost through a two-dictionary approach.

## Abstract

Diffusion magnetic resonance imaging (dMRI) is currently the only tool for noninvasively imaging the brain's white matter tracts. The fiber orientation (FO) is a key feature computed from dMRI for fiber tract reconstruction. Because the number of FOs in a voxel is usually small, dictionary-based sparse reconstruction has been used to estimate FOs with a relatively small number of diffusion gradients. However, accurate FO estimation in regions with complex FO configurations in the presence of noise can still be challenging. In this work we explore the use of a deep network for FO estimation in a dictionary-based framework and propose an algorithm named Fiber Orientation Reconstruction guided by a Deep Network (FORDN). FORDN consists of two steps. First, we use a smaller dictionary encoding coarse basis FOs to represent the diffusion signals. To estimate the mixture fractions of the dictionary atoms (and thus coarse FOs), a deep network is designed specifically for solving the sparse reconstruction problem. Here, the smaller dictionary is used to reduce the computational cost of training. Second, the coarse FOs inform the final FO estimation, where a larger dictionary encoding dense basis FOs is used and a weighted l1-norm regularized least squares problem is solved to encourage FOs that are consistent with the network output. FORDN was evaluated and compared with state-of-the-art algorithms that estimate FOs using sparse reconstruction on simulated and real dMRI data, and the results demonstrate the benefit of using a deep network for FO estimation.