MR圖像腦白質(zhì)區(qū)域提取及纖維跟蹤研究.pdf

1、中國科學(xué)技術(shù)大學(xué)碩士學(xué)位論文MR圖像腦白質(zhì)區(qū)域提取及纖維跟蹤研究姓名：童同申請學(xué)位級別：碩士專業(yè)：生物醫(yī)學(xué)工程指導(dǎo)教師：馮煥清;李傳富2011-05-08Abstract III ABSTRACT Human brain is one of the most complicated systems in the world, and the exploration in the mechanism of human brain info

2、rmation processing is also one of the most chanllenging problems among science reserch. The development of modern imaging technique makes the research of human brain in a non-invasive way become available. From previous

3、research based on EEG, MEG and fMRI techniques, it indicates that human brain has different functions in differnt regions, which means “functional segregation“. However, even when the human brain implements an extremely

4、process, it always involves many different regions interacting and cooperating with each other, thereby constructing a network to complete the task, and this is called “functional integration“. As the human brain can be

5、considered as a very complicated network, it is necessary for us to investigate the function of human brain on the basis of network. There were numerous papers about the construction of brain network in recent years, whi

6、ch makes this topic become the most welcomed one among science research. The essential step in the process of constructing the brain network is tracing the white matter bundles, which is called tractography. Therefore, a

7、ccurate tractography is of critical significance for efficient brain network construcation. So far, there are numerous tractography techniques, and they can generally be classified into two catigories: deterministic trac

8、tography and probabilistic tractography. Traditional deterministic tractography is very fast but inaccurate in regions where fibers cross or twist within the voxel. Probabilistic tracking methods are accurate but a time-

9、consuming process and difficult to interpret, making the clinical use unavailable. Therefore, this thesis focused on investigating the latest techniques of tractography and exploring the application of tractography. The

10、major contributions in this thesis are the following points: 1. Considering both the accuracy and speed of the algorithm, in this thesis we proposed a combinatorial method based on a two-tensor model. As the two-tensor m

11、odel is able to address the fiber crossing problem, it will improve the accuracy of the algorithm. Also the deterministic method is very fast so it is possible to decrease the computational time. The proposed Combinatori