面向HARDI模型的脑纤维三维可视化系统
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摘要
脑纤维是大脑各区域间信息交互的重要通道,而高角度分辨率扩散成像是表征人脑复杂神经纤维通路的有效方法,能对多种神经性疾病的诊断提供参考.但脑纤维分布错综复杂,如何对局部区域进行直观展示是可视化领域的研究难点.通过设计脑纤维三维可视化系统,实现对HARDI脑纤维数据的追踪和处理,并将处理结果在三维空间中呈现出来,实现光照技术以增加脑纤维可视化的空间层次感.文中提出基于散布矩阵的纤维筛选方法以降低三维可视化效果的视觉混杂性,并在此基础上实现2种算法对纤维进行聚类,增强了局部区域的直观展示,为临床诊断提供辅助分析工具.
Brain fibers are important channels for information exchange among different brain regions. High angular resolution diffusion imaging(HARDI) is an effective method to explore complex nerve fiber pathways in human brain, which can be used for the diagnosis of diverse neurological diseases. However, the large number of brain fibers is intricately distributed, how to display their local area directly is a challenge issue in the visualization field. In this paper, we design a 3D visualization system for brain fibers, the system can achieve the tracking and processing for HARDI brain fiber data, and visualize brain fibers in 3D space. The system implements the illumination technology to enhance the visualization of spatial layers of brain fibers. We also propose the fiber selection method based on scatter matrix in order to reduce the visual clutter. Two clustering algorithms are exploited to show the local region of brain fibers, which provides an auxiliary analysis tool for clinical diagnosis.
Brain fibers are important channels for information exchange among different brain regions. High angular resolution diffusion imaging(HARDI) is an effective method to explore complex nerve fiber pathways in human brain, which can be used for the diagnosis of diverse neurological diseases. However, the large number of brain fibers is intricately distributed, how to display their local area directly is a challenge issue in the visualization field. In this paper, we design a 3D visualization system for brain fibers, the system can achieve the tracking and processing for HARDI brain fiber data, and visualize brain fibers in 3D space. The system implements the illumination technology to enhance the visualization of spatial layers of brain fibers. We also propose the fiber selection method based on scatter matrix in order to reduce the visual clutter. Two clustering algorithms are exploited to show the local region of brain fibers, which provides an auxiliary analysis tool for clinical diagnosis.
引文
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[19]Yang Pingxian,Chen Mingju.An adaptive nonlocal mean filter model based on scattering matrix[J].Journal of Chongqing University of Posts and Telecommunications:Natural Science Edition,2015,27(2):204-207(in Chinese)(杨平先,陈明举.一种基于散布矩阵的自适应非局部均值滤波模型[J].重庆邮电大学学报:自然科学版,2015,27(2):204-207)
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[22]Jin Y,Shi Y,Zhan L,et al.Automatic clustering of white matter fibers in brain diffusion MRI with an application to genetics[J].Neuroimage,2014,100:75-90
[23]Brun A,Park H J,Knutsson H,et al.Coloring of DT-MRI fiber traces using Laplacian eigenmaps[C]//Proceedings of International Conference on Computer Aided Systems Theory.Heidelberg:Springer,2003:518-529
[24]Xia Luning,Jing Jiwu.SA-DBSCAN:an adaptive clustering algorithm based on density[J].Journal of University of Chinese Academy of Sciences,2009,26(4):530-538(in Chinese)(夏鲁宁,荆继武.SA-DBSCAN:一种自适应基于密度聚类算法[J].中国科学院大学学报,2009,26(4):530-538)
[2]Li Fangye,Chen Xiaolei,Sai Xiaoyong,et al.Application of intraoperative magnetic resonance imaging and multimodal navigation in surgical resection of glioblastoma[J].Chinese Journal of Surgery,2013,51(6):542-546(in Chinese)(李昉晔,陈晓雷,赛晓勇,等.术中磁共振成像和多模态功能神经导航在胶质母细胞瘤手术中的应用[J].中华外科杂志,2013,51(6):542-546)
[3]Ren H,Chen X,Sun G,et al.Resection of subependymal giant cell astrocytoma guided by intraoperative magnetic resonance imaging and neuronavigation[J].Child’s Nervous System,29(7),2013:1113-1121
[4]Zhang Yan,Song Zhijian.Advances in tracking algorithm of white matter fiber tract[J].Fudan University Journal of Medical Sciences,2014,41(1):1-7(in Chinese)(张艳,宋志坚.脑白质纤维束跟踪算法的研究进展[J].复旦学报:医学版,2014,41(1):1-7)
[5]Liu Yipeng,Xu Chaoqing,Jiang Zhechen,et al.A survey on brain fiber visualization[J].Journal of Computer-Aided Design&Computer Graphics,2018,30(1):9-19(in Chinese)(刘义鹏,徐超清,蒋哲臣,等.脑纤维可视化综述[J].计算机辅助设计与图形学学报,2018,30(1):9-19)
[6]Ma Jun,Su Shaobo,Zhao Yan,et al.Three-dimensional reconstruction of cranial nerves and clinical exploration based on diffusion tensor tractography[J].Chinese Journal of Neurosurgery,2014,30(2):175-178(in Chinese)(马峻,苏少波,赵岩,等.弥散张量成像的脑神经示踪重建及临床应用研究[J].中华神经外科杂志,2014,30(2):175-178)
[7]Jiang Guiping,Liu Zhexing,Feng Qianjin,et al.A Study for Fiber Tracking and Visualization Based on DT-MRI[J].Chinese Journal of Medical Physics,2005,22(3):514-518(in Chinese)(江贵平,刘哲星,冯前进,等.基于DT-MRI的纤维跟踪及可视化研究[J].中国医学物理学杂志,2005,22(3):514-518)
[8]Fritzsche K H,Neher P H,Reicht I,et al.MITK Diffusion Imaging[J].Methods of Information in Medicine,2012,51(5):441-448.
[9]Peeters T,Prckovska V,Almsick V,et al.Fast and sleek glyph rendering for interactive HARDI data exploration[C]//Proceedings of the IEEE Pacific Visualization Symposium.Los Alamitos:IEEE Computer Society Press,2009:153-160
[10]Tuch D S,Reese T G,Wiegell M R,et al.High angular resolution diffusion imaging reveals intravoxel white matter fiber heterogeneity[J].Magnetic Resonance in Medicine,2002,48(4):577-582
[11]Chen W,Ding Z,Zhang S,et al.A novel interface for interactive exploration of DTI fibers[J].IEEE Transactions on Visualization and Computer Graphics,2009,15(6):1433-1440
[12]Liang R,Wang Z,Zhang S,et al.Visual exploration of HARDIfibers with probabilistic tracking[J].Information Sciences,2016,330:483-494
[13]Jianu R,Demiralp C,Laidlaw D.Exploring 3D DTI fiber tracts with linked 2D representations[J].IEEE Transactions on Visualization and Computer Graphics,2009,15(6):1449-1456
[14]Xie Juanying,Gao Hongchao,Xie Weixin.K nearest neighbor optimized density peak fast search clustering algorithm[J].Science China:Information Science,2016,46(2):258-280(in Chinese)(谢娟英,高红超,谢维信.K近邻优化的密度峰值快速搜索聚类算法[J].中国科学:信息科学,2016,46(2):258-280)
[15]Rodriguez A,Laio A.Clustering by fast search and find of density peaks[J].Science,2014,344(6191):1492-1496
[16]Ferstl F,Bürger K,Westermann R.Streamline variability plots for characterizing the uncertainty in vector field ensembles[J].IEEE Transactions on Visualization and Computer Graphics,2016,22(1):767-776
[17]Tournier J D,Fernando C,Alan C.Robust determination of the fibre orientation distribution in diffusion MRI:Non-negativity constrained super-resolved spherical deconvolution[J].Journal of Neuroimaging,2007,35(4):1459-1472
[18]Feng Yuan.Using OpenGL RGBA mode to achieve lighting[J].Fujian Computer,2004(4):23-24(in Chinese)(冯渊.利用OpenGL实现RGBA模式下的光照处理[J].福建电脑,2004(4):23-24)
[19]Yang Pingxian,Chen Mingju.An adaptive nonlocal mean filter model based on scattering matrix[J].Journal of Chongqing University of Posts and Telecommunications:Natural Science Edition,2015,27(2):204-207(in Chinese)(杨平先,陈明举.一种基于散布矩阵的自适应非局部均值滤波模型[J].重庆邮电大学学报:自然科学版,2015,27(2):204-207)
[20]Celebi M E,Kingravi H A,Vela P A.A comparative study of efficient initialization methods for the k-means clustering algorithm[J].Expert Systems with Applications,2013,40(1):200-210
[21]Zheng Y,Jeon B,Xu D,et al.Image segmentation by generalized hierarchical fuzzy c-means algorithm[J].Journal of Intelligent&Fuzzy Systems,2015,28(2):961-973
[22]Jin Y,Shi Y,Zhan L,et al.Automatic clustering of white matter fibers in brain diffusion MRI with an application to genetics[J].Neuroimage,2014,100:75-90
[23]Brun A,Park H J,Knutsson H,et al.Coloring of DT-MRI fiber traces using Laplacian eigenmaps[C]//Proceedings of International Conference on Computer Aided Systems Theory.Heidelberg:Springer,2003:518-529
[24]Xia Luning,Jing Jiwu.SA-DBSCAN:an adaptive clustering algorithm based on density[J].Journal of University of Chinese Academy of Sciences,2009,26(4):530-538(in Chinese)(夏鲁宁,荆继武.SA-DBSCAN:一种自适应基于密度聚类算法[J].中国科学院大学学报,2009,26(4):530-538)
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