[1]刘薇,周振宇,刘小征,等.一种基于层选择的弥散张量优化算法[J].东南大学学报(自然科学版),2013,43(1):30-34.[doi:10.3969/j.issn.1001-0505.2013.01.006]
 Liu Wei,Zhou Zhenyu,Liu Xiaozheng,et al.Slice-wise optimization algorithm for diffusion tensor estimation[J].Journal of Southeast University (Natural Science Edition),2013,43(1):30-34.[doi:10.3969/j.issn.1001-0505.2013.01.006]
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一种基于层选择的弥散张量优化算法()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
43
期数:
2013年第1期
页码:
30-34
栏目:
计算机科学与工程
出版日期:
2013-01-20

文章信息/Info

Title:
Slice-wise optimization algorithm for diffusion tensor estimation
作者:
刘薇12周振宇34刘小征12严序12杨光2王遵亮5周永迪1Peterson Bradley S34徐冬溶1234
1华东师范大学脑功能基因组学教育部重点实验室, 上海200062; 2华东师范大学上海市磁共振重点实验室, 上海200062; 3哥伦比亚大学精神病学系, 纽约10032; 4纽约州立精神疾病研究所, 纽约10032; 5东南大学生物科学与医学工程学院, 南京210096
Author(s):
Liu Wei12 Zhou Zhenyu34 Liu Xiaozheng12 Yan Xu12 Yang Guang2 Wang Zunliang5 Zhou Yongdi1 Peterson Bradley S34 Xu Dongrong1234
1 Key Laboratory of Brain Functional Genomics of Ministry of Education, East China Normal University, Shanghai 200062, China
2 Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai 200062, China
3 Department of Psychiatry, Columbia University, New York 10032, USA
4 New York State Psychiatric Institute, New York 10032, USA
5 School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China
关键词:
弥散加权成像 弥散张量成像 张量优化 小波标记 互信息 灰度共生矩阵
Keywords:
diffusion weighted imaging diffusion tensor imaging tensor optimization wavelet signature mutual information gray-level co-occurrence matrix
分类号:
TP391.41
DOI:
10.3969/j.issn.1001-0505.2013.01.006
摘要:
为了从带伪影的弥散加权数据中重建更准确的张量,提出了一种基于层选择的张量优化算法.首先对弥散加权图像中常见的3种伪影(波状、层间运动和对比度伪影)进行定性分析,分别提取3种对应的特征(小波标记、相似度和相关性)来识别这些伪影,从而区分出正常图层和伪影图层.然后,利用正常图层数据进行张量重建.模拟实验结果验证了这3种特征对弥散加权图像中相关伪影判断的有效性,且对波状伪影和层间运动伪影的判断率均大于90%.真实数据实验结果表明, 与类似算法相比,所提算法可以更好地改善部分各向异性伪彩图中的偏色现象,在白质结构分析中提供了更准确的方向信息.
Abstract:
A slice-wise optimization algorithm for diffusion tensor estimation is proposed to improve the accuracy of estimating tensors by using diffusion weighted imaging(DWI)data which contain artifacts. Firstly, three types of common artifacts(wavelike, motion-between-slice and contrast artifacts)are qualitatively analyzed in DWI data. Three types of features(wavelet signature, similarity and correlation)are extracted to identify these three artifacts, respectively. Thus, a slice with or without artifacts can be distinguished. Then, tensors can be reconstructed by using the slices tagged without any artifacts. The simulation results show that the three features are effective to identify related artifacts in DWI data. A high discrimination capability(>90%)can be achieved for identifying wavelike and motion-between-slice artifacts. The experimental results using real datasets demonstrate that, compared with other similar algorithms, the proposed algorithm can improve the bias found in color-encoded fractional anisotropy map more effectively, and can provide more accurate directionality information to analyze white matter structure.

参考文献/References:

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备注/Memo

备注/Memo:
作者简介: 刘薇(1983—),女,博士生;徐冬溶(联系人),男,博士,教授,博士生导师,dx2103@columbia.edu.
基金项目: 上海市科学技术委员会资助项目(10440710200).
引文格式: 刘薇,周振宇,刘小征,等.一种基于层选择的弥散张量优化算法[J].东南大学学报:自然科学版,2013,43(1):30-34. [doi:10.3969/j.issn.1001-0505.2013.01.006]
更新日期/Last Update: 2013-01-20