title: Improving Estimation of Fiber Orientations in Diffusion MRI Using Inter-Subject Information Sharing.
abstract: Diffusion magnetic resonance imaging is widely used to investigate diffusion patterns of water molecules in the human brain. It provides information that is useful for tracing axonal bundles and inferring brain connectivity. Diffusion axonal tracing, namely tractography, relies on local directional information provided by the orientation distribution functions (ODFs) estimated at each voxel. To accurately estimate ODFs, data of good signal-to-noise ratio and sufficient angular samples are desired. This is however not always available in practice. In this paper, we propose to improve ODF estimation by using inter-subject image correlation. Specifically, we demonstrate that diffusion-weighted images acquired from different subjects can be transformed to the space of a target subject to drastically increase the number of angular samples to improve ODF estimation. This is largely due to the incoherence of the angular samples generated when the diffusion signals are reoriented and warped to the target space. To reorient the diffusion signals, we propose a new spatial normalization method that directly acts on diffusion signals using local affine transforms. Experiments on both synthetic data and real data show that our method can reduce noise-induced artifacts, such as spurious ODF peaks, and yield more coherent orientations.
authors.affiliation: Data Processing Center, Northwestern Polytechnical University, Xi'an, 712000, China.|Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, NC, 27599, USA.
authors.forename: Geng
authors.initials: G
authors.lastname: Chen
authors.affiliation: Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, NC, 27599, USA.
authors.forename: Pei
authors.initials: P
authors.lastname: Zhang
authors.affiliation: Fundamental Science on Ergonomics and Environment Control Laboratory, Beihang University, Beijing, 100191, China.
authors.forename: Ke
authors.initials: K
authors.lastname: Li
authors.affiliation: Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, NC, 27599, USA.
authors.forename: Chong-Yaw
authors.initials: CY
authors.lastname: Wee
authors.affiliation: Data Processing Center, Northwestern Polytechnical University, Xi'an, 712000, China.
authors.forename: Yafeng
authors.initials: Y
authors.lastname: Wu
authors.affiliation: Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, NC, 27599, USA.|Department of Brain and Cognitive Engineering, Korea University, Seoul, 02841, Korea.
authors.forename: Dinggang
authors.initials: D
authors.lastname: Shen
authors.affiliation: Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, NC, 27599, USA.
authors.forename: Pew-Thian
authors.initials: PT
authors.lastname: Yap
mesh_terms: D001921:Brain / Q000000981:diagnostic imaging*; D016208:Databases, Factual; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D056324:Diffusion Tensor Imaging / Q000379:methods* / Q000592:standards; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D012015:Reference Standards; D059629:Signal-To-Noise Ratio