本文選題：功能磁共振成像　切入點(diǎn)：圖像配準(zhǔn)　出處：《上海電力學(xué)院》2017年碩士論文

【摘要】：近年來(lái)隨著醫(yī)學(xué)影像技術(shù)的迅速發(fā)展,在磁共振成像技術(shù)基礎(chǔ)上發(fā)展起來(lái)的功能磁共振成像(functional Magnetic Resonance Imaging,fMRI)技術(shù),由于其能夠無(wú)創(chuàng)傷性地對(duì)腦功能進(jìn)行準(zhǔn)確的定位,并且具有較好的可重復(fù)性和可行性,因此得到廣泛的關(guān)注和研究。然而,在這一類研究中由于成像設(shè)備、個(gè)體差異及頭動(dòng)的影響,使得采集的fMRI圖像不能直接進(jìn)行處理分析,必須先進(jìn)行一系列預(yù)處理操作來(lái)消除這些因素的影響。其中,圖像配準(zhǔn)是較為關(guān)鍵的一步,其結(jié)果的好壞直接關(guān)系到最終腦功能定位的準(zhǔn)確性。此外,在對(duì)fMRI數(shù)據(jù)建模分析時(shí),模型的回歸量之間往往存在一定的共線性,導(dǎo)致得到不準(zhǔn)確甚至錯(cuò)誤的統(tǒng)計(jì)結(jié)果。因此,正確對(duì)fMRI數(shù)據(jù)進(jìn)行處理和分析是實(shí)現(xiàn)腦功能準(zhǔn)確定位的基礎(chǔ)和前提。本文以fMRI圖像為研究對(duì)象,對(duì)f MRI數(shù)據(jù)處理和分析過(guò)程中存在的問題進(jìn)行了深入的探討和研究。通過(guò)揭示腦認(rèn)知活動(dòng)的深層機(jī)制,對(duì)許多重大腦疾病的診斷、治療以及相關(guān)病理學(xué)、藥理學(xué)研究都具有重要意義。本文的主要研究工作如下:首先,介紹了圖像配準(zhǔn)算法的基本概念和框架,從幾何變換、特征空間、相似性測(cè)度以及搜索策略四個(gè)組成部分對(duì)配準(zhǔn)過(guò)程展開闡述,并以實(shí)際的fMRI數(shù)據(jù)為例,利用SPM軟件包對(duì)fMRI數(shù)據(jù)的一般處理過(guò)程和統(tǒng)計(jì)分析過(guò)程作了詳細(xì)的闡述;其次,考慮到fMRI數(shù)據(jù)采集過(guò)程中被試頭動(dòng)的影響,提出了一種基于互信息的功能磁共振圖像配準(zhǔn)方法,并應(yīng)用主軸法和多分辨率策略來(lái)提高配準(zhǔn)的速度和精度;最后,針對(duì)fMRI數(shù)據(jù)分析過(guò)程中一般線性模型回歸量之間存在的共線性問題,提出了一種正交化方法。通過(guò)將相關(guān)的兩個(gè)回歸量正交分解為兩個(gè)獨(dú)立的量,以此來(lái)消除其中一個(gè)回歸量對(duì)另一個(gè)回歸量在結(jié)果變量中的影響,從而得到更加準(zhǔn)確的結(jié)果。
[Abstract]:In recent years, with the rapid development of medical imaging technology, functional Magnetic Resonance imaging of MRI (functional magnetic resonance imaging) technology has been developed on the basis of magnetic resonance imaging technology.And has good repeatability and feasibility, so it has been widely concerned and studied.However, in this kind of research, because of the influence of imaging equipment, individual difference and head movement, the collected fMRI images can not be processed and analyzed directly, so a series of preprocessing operations must be carried out to eliminate the influence of these factors.Image registration is a key step, and the result is directly related to the accuracy of the final brain function location.In addition, when modeling and analyzing fMRI data, there is always a certain collinearity between the regression quantities of the model, which leads to inaccurate or even incorrect statistical results.Therefore, the correct processing and analysis of fMRI data is the basis and prerequisite for accurate localization of brain function.In this paper, fMRI image is taken as the research object, and the problems existing in the processing and analysis of f MRI data are deeply discussed and studied.By revealing the underlying mechanism of brain cognitive activity, it is of great significance for the diagnosis, treatment, related pathology and pharmacological research of many major brain diseases.The main work of this paper is as follows: firstly, the basic concept and framework of image registration algorithm are introduced. The registration process is described from four parts: geometric transformation, feature space, similarity measure and search strategy.Taking the actual fMRI data as an example, the general processing process and statistical analysis process of fMRI data are described in detail by using SPM software package. Secondly, considering the influence of the head movement in the process of fMRI data acquisition,In this paper, a mutual information based functional magnetic resonance image registration method is proposed, and the principal axis method and multi-resolution strategy are used to improve the speed and accuracy of registration.Aiming at the problem of collinearity between regression variables of general linear model in the process of fMRI data analysis, a orthogonalization method is proposed.By decomposing two correlated regression variables into two independent variables, the influence of one regression quantity on the other regression quantity in the result variable is eliminated, and more accurate results are obtained.
【學(xué)位授予單位】：上海電力學(xué)院
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：R445.2;TP391.41

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