弥散张量磁共振成像(DT-MRI)的脑白质纤维追踪成像利用脑白质水分子弥散构成的弥散张量信息追踪脑白质纤维束并无创重建其3维结构图像。针对现有追踪方法一般以局部体素的弥散张量为主要追踪依据,缺乏对纤维结构、弥散度等人体解剖结构...弥散张量磁共振成像(DT-MRI)的脑白质纤维追踪成像利用脑白质水分子弥散构成的弥散张量信息追踪脑白质纤维束并无创重建其3维结构图像。针对现有追踪方法一般以局部体素的弥散张量为主要追踪依据,缺乏对纤维结构、弥散度等人体解剖结构和生理机能的综合考量的缺陷,该文基于贝叶斯理论框架综合分析追踪路径与各体素弥散张量方向和纤维束几何结构相关性,并使用弥散度和追踪纤维角度对两者进行约束,获得各步追踪方向的概率密度分布,通过Markov Chain Monte Carlo采样确定其追踪方向进行追踪成像,通过多次追踪获得具有统计意义的3维结果。最后利用文中方法在合成弥散张量数据上进行了成像仿真,在真实脑部DT-MRI数据上进行了成像实验。仿真和实验结果表明,该方法能实现预期的脑白质纤维追踪成像,比现有追踪成像方法结果更可靠,可重复性更强。展开更多
Focused on the character of Statistical Application,The paper brings forward a framework of Statistical Application first,and then analyzes the current trend of Statistical study and Statistical Application based on t...Focused on the character of Statistical Application,The paper brings forward a framework of Statistical Application first,and then analyzes the current trend of Statistical study and Statistical Application based on this framework.Finally,The paper points out that the research of Statistical Methodology will become one of the important aspects of Statistical Study in the future.展开更多
A multivariate method for fault diagnosis and process monitoring is proposed. This technique is based on a statistical pattern(SP) framework integrated with a self-organizing map(SOM). An SP-based SOM is used as a cla...A multivariate method for fault diagnosis and process monitoring is proposed. This technique is based on a statistical pattern(SP) framework integrated with a self-organizing map(SOM). An SP-based SOM is used as a classifier to distinguish various states on the output map, which can visually monitor abnormal states. A case study of the Tennessee Eastman(TE) process is presented to demonstrate the fault diagnosis and process monitoring performance of the proposed method. Results show that the SP-based SOM method is a visual tool for real-time monitoring and fault diagnosis that can be used in complex chemical processes.Compared with other SOM-based methods, the proposed method can more efficiently monitor and diagnose faults.展开更多
文摘弥散张量磁共振成像(DT-MRI)的脑白质纤维追踪成像利用脑白质水分子弥散构成的弥散张量信息追踪脑白质纤维束并无创重建其3维结构图像。针对现有追踪方法一般以局部体素的弥散张量为主要追踪依据,缺乏对纤维结构、弥散度等人体解剖结构和生理机能的综合考量的缺陷,该文基于贝叶斯理论框架综合分析追踪路径与各体素弥散张量方向和纤维束几何结构相关性,并使用弥散度和追踪纤维角度对两者进行约束,获得各步追踪方向的概率密度分布,通过Markov Chain Monte Carlo采样确定其追踪方向进行追踪成像,通过多次追踪获得具有统计意义的3维结果。最后利用文中方法在合成弥散张量数据上进行了成像仿真,在真实脑部DT-MRI数据上进行了成像实验。仿真和实验结果表明,该方法能实现预期的脑白质纤维追踪成像,比现有追踪成像方法结果更可靠,可重复性更强。
文摘Focused on the character of Statistical Application,The paper brings forward a framework of Statistical Application first,and then analyzes the current trend of Statistical study and Statistical Application based on this framework.Finally,The paper points out that the research of Statistical Methodology will become one of the important aspects of Statistical Study in the future.
基金Project(2013CB733605)supported by the National Basic Research Program of ChinaProject(21176073)supported by the National Natural Science Foundation of ChinaProject supported by the Fundamental Research Funds for the Central Universities,China
文摘A multivariate method for fault diagnosis and process monitoring is proposed. This technique is based on a statistical pattern(SP) framework integrated with a self-organizing map(SOM). An SP-based SOM is used as a classifier to distinguish various states on the output map, which can visually monitor abnormal states. A case study of the Tennessee Eastman(TE) process is presented to demonstrate the fault diagnosis and process monitoring performance of the proposed method. Results show that the SP-based SOM method is a visual tool for real-time monitoring and fault diagnosis that can be used in complex chemical processes.Compared with other SOM-based methods, the proposed method can more efficiently monitor and diagnose faults.
