Non-Negative Spherical Deconvolution (NNSD) for estimation of fiber Orientation Distribution Function in single-/multi-shell diffusion MRI
文献类型：期刊论文
| 作者 | Cheng, Jian1,2 ; Deriche, Rachid3; Jiang, Tianzi4; Shen, Dinggang1,2; Yap, Pew-Thian1,2
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| 刊名 | NEUROIMAGE
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| 出版日期 | 2014-11-01 |
| 卷号 | 101页码:750-764 |
| 关键词 | Spherical deconvolution Diffusion MRI Fiber Orientation Distribution Function Non-negativity constraint Spherical harmonics |
| 英文摘要 | 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. |
| WOS标题词 | Science & Technology ; Life Sciences & Biomedicine |
| 类目[WOS] | Neurosciences ; Neuroimaging ; Radiology, Nuclear Medicine & Medical Imaging |
| 研究领域[WOS] | Neurosciences & Neurology ; Radiology, Nuclear Medicine & Medical Imaging |
| 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记录号 | WOS:000344931800067 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/3155]  |
| 专题 | 自动化研究所_脑网络组研究中心 |
| 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. |
入库方式： OAI收割
来源：自动化研究所
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