Non-Negative Spherical Deconvolution (NNSD) for estimation of fiber Orientation Distribution Function in single-/multi-shell diffusion MRI

	Non-Negative Spherical Deconvolution (NNSD) for estimation of fiber Orientation Distribution Function in single-/multi-shell diffusion MRI
	Cheng, Jian 1,2; Deriche, Rachid 3; Jiang, Tianzi4 ; Shen, Dinggang 1,2; Yap, Pew-Thian 1,2
发表期刊	NEUROIMAGE
	2014-11-01
卷号	101 页码:750-764
文章类型	Article
摘要	Spherical Deconvolution (SD) is commonly used for estimating fiber Orientation Distribution Functions (fODFs) from diffusion-weighted signals. Existing SD methods can be classified into two categories: 1) Continuous Representation based SD (CR-SD), where typically Spherical Harmonic (SH) representation is used for convenient analytical solutions, and 2) Discrete Representation based SD (DR-SD), where the signal profile is represented by a discrete set of basis functions uniformly oriented on the unit sphere. A feasible fODF should be non-negative and should integrate to unity throughout the unit sphere S-2. However, to our knowledge, most existing SH-based SD methods enforce non-negativity only on discretized points and not the whole continuum of S-2. Maximum Entropy SD(MESD) and Cartesian Tensor Fiber Orientation Distributions (CT-FOD) are the only SD methods that ensure non-negativity throughout the unit sphere. They are however computational intensive and are susceptible to errors caused by numerical spherical integration. Existing SD methods are also known to overestimate the number of fiber directions, especially in regions with low anisotropy. DR-SD introduces additional error in peak detection owing to the angular discretization of the unit sphere. This paper proposes a SD framework, called Non-Negative SD (NNSD), to overcome all the limitations above. NNSD is significantly less susceptible to the false-positive peaks, uses SH representation for efficient analytical spherical deconvolution, and allows accurate peak detection throughout the whole unit sphere. We further show that NNSD and most existing SD methods can be extended to work on multi-shell data by introducing a three-dimensional fiber response function. We evaluated NNSD in comparison with Constrained SD (CSD), a quadratic programming variant of CSD, MESD, and an L1-norm regularized non-negative least-squares DR-SD. Experiments on synthetic and real single-/multi-shell data indicate that NNSD improves estimation performance in terms of mean difference of angles, peak detection consistency, and anisotropy contrast between isotropic and anisotropic regions. (C) 2014 Elsevier Inc. All rights reserved.
关键词	Spherical Deconvolution Diffusion Mri Fiber Orientation Distribution Function Non-negativity Constraint Spherical Harmonics
WOS标题词	Science & Technology ; Life Sciences & Biomedicine
关键词[WOS]	RIEMANNIAN FRAMEWORK ; WEIGHTED MRI ; MODEL-FREE ; RECONSTRUCTION ; TRACTOGRAPHY ; RESOLUTION ; NETWORKS ; CONNECTIVITY
收录类别	SCI
语种	英语
WOS研究方向	Neurosciences & Neurology ; Radiology, Nuclear Medicine & Medical Imaging
WOS类目	Neurosciences ; Neuroimaging ; Radiology, Nuclear Medicine & Medical Imaging
WOS记录号	WOS:000344931800067
引用统计	被引频次：29[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/3155
专题	脑图谱与类脑智能实验室_脑网络组研究
作者单位	1.Univ N Carolina, Dept Radiol, Chapel Hill, NC USA 2.Univ N Carolina, BRIC, Chapel Hill, NC USA 3.INRIA Sophia Antipolis Mediterranee, Athena Project Team, Valbonne, France 4.Chinese Acad Sci, Ctr Computat Med, Inst Automat, LIAMA, Beijing 100864, Peoples R China
推荐引用方式 GB/T 7714	Cheng, Jian,Deriche, Rachid,Jiang, Tianzi,et al. Non-Negative Spherical Deconvolution (NNSD) for estimation of fiber Orientation Distribution Function in single-/multi-shell diffusion MRI[J]. NEUROIMAGE,2014,101:750-764.
APA	Cheng, Jian,Deriche, Rachid,Jiang, Tianzi,Shen, Dinggang,&Yap, Pew-Thian.(2014).Non-Negative Spherical Deconvolution (NNSD) for estimation of fiber Orientation Distribution Function in single-/multi-shell diffusion MRI.NEUROIMAGE,101,750-764.
MLA	Cheng, Jian,et al."Non-Negative Spherical Deconvolution (NNSD) for estimation of fiber Orientation Distribution Function in single-/multi-shell diffusion MRI".NEUROIMAGE 101(2014):750-764.