A PSO-Powell Hybrid Method to Extract Fiber Orientations from ODF.

Zhanxiong Wu,Xiaohui Yu,Yang Liu,Ming Hong

doi:10.1155/2018/7680164

Abstract

High angular resolution diffusion imaging (HARDI) has opened up new perspectives for the delineation of crossing and branching fiber pathways by orientation distribution function (ODF). The fiber orientations contained in an imaging voxel are the key factor in tractography. To extract real fiber orientations from ODF, a hybrid method is proposed for computing the principal directions of ODF by combining the variation of Particle Swarm Optimization (PSO) algorithm with the modified Powell algorithm. This method is comprised of the global searching ability of PSO and the powerful local optimizing of Powell search. This combination can guarantee finding all the diffusion directions without applying sliding windows and improve the accuracy and efficiency. The proposed approach was evaluated on simulated crossing-fiber datasets, Tractometer, and in vivo datasets. The results show that this method could correctly identify fiber directions under a range of noise levels. This method was compared with the state-of-the-art methods, such as modified Powell, ball-stick model, and diffusion decomposition, showing that it outperformed them. As to the multimodal voxels where different fiber populations exist, the proposed approach allows us to improve the estimation accuracy of fiber orientations from ODF. It can play a significant role in the nerve fiber tracking.

Highlights

At present, tractography based on diffusion-weighted magnetic resonance imaging (DWI) is the only noninvasive tool to obtain information on the neural architecture of the human brain white matter (WM) in vivo
We utilized multitensor simulated datasets, Tractometer datasets, and in vivo datasets to evaluate the methods for extracting fiber orientations, including ball-stick, modified Powell, diffusion decomposition, and Particle Swarm Optimization (PSO)-Powell model
After the orientation distribution function (ODF) fields were constructed with Q-ball imaging (QBI), constant solid angle QBI (CSA-QBI), and diffusion orientation transform (DOT), we applied three algorithms including PSO-Powell, modified Powell with sliding windows, and diffusion decomposition to extract the fiber orientations

Summary

Introduction

Tractography based on diffusion-weighted magnetic resonance imaging (DWI) is the only noninvasive tool to obtain information on the neural architecture of the human brain white matter (WM) in vivo. There are three mathematical models applied to retrieve fiber orientations from DWI raw datasets: apparent diffusion coefficient (ADC), diffusion tensor (DT), and ODF. The local maxima of ADC profile do not necessarily coincide with the underlying fiber directions, making the extraction of orientation information difficult [5,6,7]. This is due to the nature of the ADC measurement which is the projection of spin displacements onto the diffusing gradient axis. ODF has its local maxima aligned with the underlying fiber directions at every voxel. ODF is most widely employed to determine the fiber orientations with high angular resolution

Methods

Results

Discussion

Conclusion