title: Adaptively constrained convex optimization for accurate fiber orientation estimation with high order spherical harmonics.
abstract: Diffusion imaging data from the Human Connectome Project (HCP) provides a great opportunity to map the whole brain white matter connectivity to unprecedented resolution in vivo. In this paper we develop a novel method for accurately reconstruct fiber orientation distribution from cutting-edge diffusion data by solving the spherical deconvolution problem as a constrained convex optimization problem. With a set of adaptively selected constraints, our method allows the use of high order spherical harmonics to reliably resolve crossing fibers with small separation angles. In our experiments, we demonstrate on simulated data that our algorithm outperforms a popular spherical deconvolution method in resolving fiber crossings. We also successfully applied our method to the multi-shell and diffusion spectrum imaging (DSI) data from HCP to demonstrate its ability in using state-of-the-art diffusion data to study complicated fiber structures.
authors: Dept. of Mathematics, UCLA, Los Angeles, CA, USA.
authors: Giang
authors: G
authors: Tran
authors: Lab of Neuro Imaging (LONI), UCLA School of Medicine, Los Angeles, CA, USA.
authors: Yonggang
authors: Y
authors: Shi
mesh_terms: D000465:Algorithms; D001921:Brain / Q000166:cytology*; D063132:Connectome / Q000379:methods*; D056324:Diffusion Tensor Imaging / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D021621:Imaging, Three-Dimensional / Q000379:methods*; D009413:Nerve Fibers, Myelinated / Q000648:ultrastructure*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity