title: A Novel Richardson-Lucy Model with Dictionary Basis and Spatial Regularization for Isolating Isotropic Signals.
abstract: Diffusion-weighted magnetic resonance imaging is a non-invasive imaging method that has been increasingly used in neuroscience imaging over the last decade. Partial volume effects (PVEs) exist in sampling signal for many physical and actual reasons, which lead to inaccurate fiber imaging. We overcome the influence of PVEs by separating isotropic signal from diffusion-weighted signal, which can provide more accurate estimation of fiber orientations. In this work, we use a novel response function (RF) and the correspondent fiber orientation distribution function (fODF) to construct different signal models, in which case the fODF is represented using dictionary basis function. We then put forward a new index Piso, which is a part of fODF to quantify white and gray matter. The classic Richardson-Lucy (RL) model is usually used in the field of digital image processing to solve the problem of spherical deconvolution caused by highly ill-posed least-squares algorithm. In this case, we propose an innovative model integrating RL model with spatial regularization to settle the suggested double-models, which improve noise resistance and accuracy of imaging. Experimental results of simulated and real data show that the proposal method, which we call iRL, can robustly reconstruct a more accurate fODF and the quantitative index Piso performs better than fractional anisotropy and general fractional anisotropy.
authors.affiliation: Institute of Information Processing and Automation, Zhejiang University of Technology, Hangzhou, Zhejiang, China.
authors.forename: Tiantian
authors.identifier: http://orcid.org/0000-0002-0114-9064
authors.initials: T
authors.lastname: Xu
authors.affiliation: Institute of Information Processing and Automation, Zhejiang University of Technology, Hangzhou, Zhejiang, China.
authors.forename: Yuanjing
authors.initials: Y
authors.lastname: Feng
authors.affiliation: Institute of Information Processing and Automation, Zhejiang University of Technology, Hangzhou, Zhejiang, China.
authors.forename: Ye
authors.initials: Y
authors.lastname: Wu
authors.affiliation: Institute of Information Processing and Automation, Zhejiang University of Technology, Hangzhou, Zhejiang, China.
authors.forename: Qingrun
authors.initials: Q
authors.lastname: Zeng
authors.affiliation: Institute of Information Processing and Automation, Zhejiang University of Technology, Hangzhou, Zhejiang, China.
authors.forename: Jun
authors.initials: J
authors.lastname: Zhang
authors.affiliation: Institute of Information Processing and Automation, Zhejiang University of Technology, Hangzhou, Zhejiang, China.
authors.forename: Jianzhong
authors.initials: J
authors.lastname: He
authors.affiliation: Zhejiang Provincial Key Laboratory of Aging and Neurological Disorder Research, Wenzhou Medical University, Wenzhou, Zhejiang, China.
authors.forename: Qichuan
authors.initials: Q
authors.lastname: Zhuge
mesh_terms: D000818:Animals; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D006801:Humans; D008962:Models, Theoretical