發(fā)布時(shí)間：2018-04-29 08:24

  本文選題：復(fù)數(shù)fMRI數(shù)據(jù) + ICA��； 參考：《大連理工大學(xué)》2014年碩士論文

【摘要】：功能磁共振成像(function Magnetic Resonance Imaging, fMRI)是一種重要的腦功能成像技術(shù)。通過對采集到的fMRI數(shù)據(jù)進(jìn)行獨(dú)立分量分析(independent component analysis,ICA),可以獲取腦認(rèn)知所需的空間成分及其時(shí)間成分。 完整的fMRI數(shù)據(jù)是復(fù)數(shù),但由于fMRI相位數(shù)據(jù)噪聲嚴(yán)重且特性未知,人們通常只分析其幅值數(shù)據(jù)(即實(shí)數(shù)fMRI)。然而,越來越多的證據(jù)表明,相位數(shù)據(jù)含有獨(dú)特的腦功能信息,對其進(jìn)行有效的利用,有助于揭示更為完整的腦功能信息。目前,復(fù)數(shù)fMRI數(shù)據(jù)的ICA分析采用了預(yù)處理消噪法,但存在信息損失問題；ICA后處理方法也不夠完善。為此,本文提出一種能夠充分利用ICA估計(jì)成分相位信息的后處理分析框架,具體內(nèi)容如下： (1)針對復(fù)數(shù)ICA固有的相位模糊問題,提出了基于時(shí)間成分的相位模糊矯正方法。通過時(shí)間成分和空間成分的非環(huán)形度對比,以及對激活區(qū)體素相位原理和fMRI時(shí)空關(guān)系的分析,提出了時(shí)間成分實(shí)部能量最大化的相位模糊矯正準(zhǔn)則,并利用標(biāo)準(zhǔn)時(shí)間序列或空間參考信息去除了矯正過程中的符號錯(cuò)誤,成功解決了相位模糊問題。實(shí)驗(yàn)結(jié)果表明,本文基于時(shí)間成分的相位模糊矯正方案具有魯棒性好、準(zhǔn)確率高的優(yōu)點(diǎn)； (2)針對相位噪聲對ICA空間成分激活區(qū)的嚴(yán)重干擾問題,提出了相位定位思想和相位掩蔽方法,以及將相位范圍和幅值強(qiáng)度相結(jié)合的空間可視化方案。相位定位利用ICA空間成分的相位信息區(qū)分有用體素和干擾體素,相位掩蔽法利用相位定位mask去除ICA空間成分中的干擾體素,提取激活區(qū)域。最后,結(jié)合體素的幅值強(qiáng)度信息,精確提取激活區(qū)域。實(shí)驗(yàn)結(jié)果表明,這些方法具有去噪性能好、激活區(qū)提取準(zhǔn)確度高等優(yōu)點(diǎn)； (3)利用本文提出的復(fù)數(shù)fMRI數(shù)據(jù)分析框架,對運(yùn)動刺激下的主要腦功能網(wǎng)絡(luò)進(jìn)行了提取以及交互作用分析,驗(yàn)證了本文框架的有效性和普適性,并為復(fù)數(shù)fMRI數(shù)據(jù)的功能連接工作提供了有效的支持。
[Abstract]:Functional magnetic resonance imaging (function Magnetic Resonance Imaging (fMRI)) is an important brain functional imaging technique. By means of independent component analysis (independent component analysis, ICA) of the collected fMRI data, the spatial components and time components needed for brain cognition can be obtained.
The complete fMRI data is plural, but because of the serious noise and unknown characteristics of the fMRI phase data, people usually only analyze its amplitude data (i.e. real fMRI). However, more and more evidence shows that the phase data contains unique brain function information, and the effective use of it can help to reveal more complete brain function information. The ICA analysis of fMRI data adopts the preprocessing denoising method, but there is a problem of information loss, and the post-processing method of ICA is not perfect. Therefore, this paper proposes a post processing analysis framework which can make full use of ICA to estimate the phase information of the component. The specific contents are as follows:
(1) in view of the inherent phase ambiguity of complex ICA, a method of phase fuzzy correction based on time component is proposed. By comparing the non ring degree of the time component and the space component, and the analysis of the phase principle of the voxel in the active region and the analysis of the time and space relation of the fMRI, the phase fuzzy correction criterion of the time component real energy maximization is put forward, and the benefit of the phase fuzzy correction is put forward. The standard time sequence or spatial reference information is used to remove the symbol errors in the correction process, and the phase ambiguity problem is successfully solved. The experimental results show that the phase fuzzy correction scheme based on the time component has the advantages of good robustness and high accuracy.
(2) in view of the serious interference of phase noise to the active area of ICA space components, the phase location idea and phase masking method are proposed, and the space visualization scheme combining phase range and amplitude intensity is proposed. Phase location uses phase information of ICA space component to distinguish between voxel and interferin, phase masking method uses phase Mask removes the disturbing voxel in the ICA space components and extracts the active region. Finally, the activation region is extracted accurately with the amplitude intensity information of the voxel. The experimental results show that these methods have the advantages of good denoising performance and high accuracy in the activation area.
(3) using the complex fMRI data analysis framework proposed in this paper, the main brain function network under the motion stimulus is extracted and the interaction analysis is used to verify the validity and universality of the framework, and provide an effective support for the functional connection of the complex fMRI data.

【學(xué)位授予單位】：大連理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：R445.2;TN911.7

【參考文獻(xiàn)】

相關(guān)期刊論文 前1條

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本文編號：1819152