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浙江大学学报(工学版)
计算机科学技术     
基于离散球面反卷积的白质纤维重构算法
李志娟, 冯远静, 牛延棚, 李蓉, 叶峰
浙江工业大学 信息工程学院,浙江 杭州 310023
Fiber reconstruction algorithm based on discrete spherical deconvolution
LI Zhi-juan, FENG Yuan-jing, NIU Yan-peng, LI Rong, YE Feng
College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
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摘要:

为了解决基于扩散张量成像(DTI)的跟踪过程难以刻画脑白质内复杂纤维结构的问题,提出一种基于离散球面反卷积的确定性跟踪算法.该算法采用离散纤维方向密度函数建立球面反卷积模型,解除对连续球面函数模型的依赖,获得高角度分辨率识别;借助球面高斯函数拟合以补偿离散误差,并在此基础上实现流线型白质纤维跟踪.合成数据、仿真数据和实际临床数据表明:该模型能显著提高体素内小角度交叉纤维的分辨率,并有效抑制噪声.相比于基于其他模型的重构算法,该算法能够更准确地反映活体脑神经组织的真实连接情况.

Abstract:

In order to resolve the problem that the tracking process based on diffision tensor imaging (DTI) has difficulty in describing the complex fiber structure of the brain white matter, a deterministic tracking algorithm based on discrete spherical deconvolution was proposed. The algorithm uses discrete fiber orientation density function to build the spherical deconvolution model, which aims at relieving the dependence on the continuous spherical function model and getting high angular resolution identification. A spherical Gaussian function was used to make up for the discretization error, then the streamline tracking algorithm was implemented on the basis of the aboving model. Experimental results concluded from the synthetic data, platform data and real clinical data demonstrate that the proposed model evidently improves the resolution of small angle crossing fibers within voxel, meanwhile the noise is effectively restrained. Compared with the reconstruction algorithms based on other models, the proposed algorithm can reflect the true connection of brain neural tissue in vivo more accurately.

出版日期: 2015-04-01
:  TP 391  
基金资助:

国家自然科学基金资助项目(61379020);钱江人才计划资助项目(2012R10051);浙江省自然科学基金资助项目(Y13F030056).

通讯作者: 冯远静,男,教授,硕导.     E-mail: fyjing@zjut.edu.cn
作者简介: 李志娟(1989- ),女,硕士生,从事医学图像处理的研究.E-mail: lizhijuantnt@126.com
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引用本文:

李志娟, 冯远静, 牛延棚, 李蓉, 叶峰. 基于离散球面反卷积的白质纤维重构算法[J]. 浙江大学学报(工学版), 10.3785/j.issn.1008-973X.2014.06.004.

LI Zhi-juan, FENG Yuan-jing, NIU Yan-peng, LI Rong, YE Feng. Fiber reconstruction algorithm based on discrete spherical deconvolution. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 10.3785/j.issn.1008-973X.2014.06.004.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2014.06.004        http://www.zjujournals.com/eng/CN/Y2014/V48/I6/987

[1] 米博会.基于DTI的脑白质神经纤维跟踪技术及其运用[D].天津: 天津大学, 2008.
MI Bo-hui. White matter fiber tracking and application in diffusion tensor imaging[D]. Tianjin: Tianjin University, 2008.
[2] EHRENS T E, JOHANSEN B H, JBABDI S. Probabilistic diffusion tractography with multiple fiber orientations: what can we gain?[J]. NeuroImage, 2007, 34(1): 144-155.
[3] DESCOTEAUX M, ANGELINO E, FITZGIBBINS S, et al. Regularized, fast, and robust analytical Q-ball imaging[J]. Magnetic Resonance in Medicine, 2007, 58(3): 497-510.
[4] CHENG Jian, GHOSH A, DERICHE R, et al. Model-free, regularized, fast, and robust analytical orientation distribution function estimation [C] ∥MICCAI 2010. Berlin: Springer-Verlag, 2010: 648-656.
[5] AMES G M, MARTHA E S, YOGESH R. Filtered multi-tensor tractography [J]. IEEE Transactions on Medical Imaging, 2010, 29(9): 1664-1675.
[6] 李蓉,冯远静,邵开来等.磁共振扩散高阶张量成像的脑白质纤维微结构模型及特征提取算法[J].中国生物医学工程学报,2012, 31(3): 51-59.
LI Rong, FENG Yuan-jing, SHAO kai-lai, et al. Any order tensor imaging model feature extraction algorithm based on iterative search [J]. Journal of Chinese Biomedical Engineering, 2012, 31(3): 51-59.
[7] TOUNNIER J D, CALAMANTE F, GADIAN D G, et al. Direct estimation of the fiber orientation density function from diffusion weighted MRI data using spherical deconvolution [J]. NeuroImage, 2004, 23(3): 1176-1185.
[8] ANDERSON A W. Measurement of fiber orientation distributions using high angular resolution diffusion imaging[J]. Magnetic Resonance in Medicine, 2005, 54: 11941206.
[9] TOUNNIER J D, CALAMANT F, CONNELY A, et al. Robust determination of the fibre orientation distribution indiffusion MRI: Non-negativity constrained super-resolved spherical deconvolution [J]. NeuroImage, 2007, 35(4): 1459-1472.
[10] PATEL V, SHI Y, THOMPSON P M, et al. Mesh-based spherical deconvolution: A flexible approach to reconstruction of non-negative fiber orientation distributions [J]. Neuroimage, 2010, 51(3): 1071-1081.
[11] SODEMAN O, JONSSO B. Restricted diffusion in cylindrical geometry [J]. Magnetic Resonance, Series A, 1995, 117(1): 94-97.
[12] AGANJ I, LENGLET C, JAHANSHAD N, et al. A hough transform global probabilistic approach to multiple subject diffusion MRI tractography[J]. Medical Image Analysis, 2011, 15(4): 414-425.

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