title: Brain white matter fiber estimation and tractography using Q-ball imaging and Bayesian MODEL.
abstract: Diffusion tensor imaging allows for the non-invasive in vivo mapping of the brain tractography. However, fiber bundles have complex structures such as fiber crossings, fiber branchings and fibers with large curvatures that tensor imaging (DTI) cannot accurately handle. This study presents a novel brain white matter tractography method using Q-ball imaging as the data source instead of DTI, because QBI can provide accurate information about multiple fiber crossings and branchings in a single voxel using an orientation distribution function (ODF). The presented method also uses graph theory to construct the Bayesian model-based graph, so that the fiber tracking between two voxels can be represented as the shortest path in a graph. Our experiment showed that our new method can accurately handle brain white matter fiber crossings and branchings, and reconstruct brain tractograhpy both in phantom data and real brain data.
authors.affiliation: College of Information Science and Engineering, Northeastern University, China.
authors.forename: Meng
authors.initials: M
authors.lastname: Lu
mesh_terms: D000328:Adult; D001499:Bayes Theorem; D001931:Brain Mapping / Q000295:instrumentation / Q000379:methods*; D003198:Computer Simulation; D056324:Diffusion Tensor Imaging / Q000295:instrumentation / Q000379:methods; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000295:instrumentation / Q000379:methods; D008297:Male; D008953:Models, Anatomic; D009412:Nerve Fibers / Q000648:ultrastructure; D019047:Phantoms, Imaging; D066127:White Matter / Q000033:anatomy & histology* / Q000648:ultrastructure
keywords: Bayesian model; Brain white matter; QBI; graph theory; tractography