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

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