【摘要】：彌散張量成像(Diffusion Tensor Imaging,DTI)技術(shù)要求從多個(gè)方向連續(xù)施加彌散梯度編碼,既可以度量彌散的大小,又可以考察彌散的方向性,比單幅彌散加權(quán)圖像更全面地反映出成像體素內(nèi)彌散的變化情況,可以從微觀角度反映腦組織結(jié)構(gòu)的連通性和完整性。DTI技術(shù)已在多種中樞神經(jīng)系統(tǒng)疾病的病灶病理分析中表現(xiàn)出極大的研究?jī)r(jià)值,可為臨床治療及預(yù)后評(píng)估提供重要的參考依據(jù),因此越來(lái)越受到廣泛關(guān)注。然而,DTI技術(shù)目前尚主要停留在對(duì)腦神經(jīng)科學(xué)的研究階段,其臨床化進(jìn)程主要受到量化分析的瓶頸制約。已有的DTI量化分析方法存在各自的應(yīng)用局限性,為了建立較為準(zhǔn)確又合理的DTI量化分析模型,為疾病提供可靠的病理診斷依據(jù),開(kāi)展了本論文的研究工作,論文的研究?jī)?nèi)容主要如下:一方面,對(duì)當(dāng)前廣泛研究、普遍認(rèn)可的一種DTI量化分析方法,即基于體素的全腦分析方法(Voxel-based analysis,VBA),進(jìn)行算法分析及流程實(shí)現(xiàn),并針對(duì)其平滑步驟中的各向同性高斯核尺寸無(wú)統(tǒng)一標(biāo)準(zhǔn)而對(duì)分析結(jié)果產(chǎn)生很大影響的問(wèn)題,提出了一種適用于DTI圖像去噪,又有效保留纖維束特征的各向異性濾波算法,并將其與普通各向異性濾波方法處理的結(jié)果進(jìn)行比較,從主觀和客觀兩個(gè)角度,利用圖像間的均方根誤差值、峰值信噪比、結(jié)構(gòu)相似性指標(biāo)等多個(gè)圖像質(zhì)量評(píng)估參數(shù)驗(yàn)證了所提出的算法的優(yōu)越性。另一方面,對(duì)近年來(lái)逐漸流行的基于纖維束空間統(tǒng)計(jì)(Tract-based spatial statistics,TBSS)的DTI量化分析方法進(jìn)行了研究和實(shí)現(xiàn),并聯(lián)合基于所提出的各向異性濾波器的VBA方法,建立DTI量化分析融合模型,依據(jù)相同的DTI圖像預(yù)處理過(guò)程及相同的圖像配準(zhǔn)模板,綜合兩種方法分析結(jié)果各自的優(yōu)勢(shì),既可以較為準(zhǔn)確地定位腦白質(zhì)纖維束DTI參數(shù)值顯著變化的位置,又可以分析病變區(qū)域的相關(guān)解剖結(jié)構(gòu)變化�；谶@一融合量化分析模型,針對(duì)多發(fā)性硬化癥臨床DTI數(shù)據(jù)進(jìn)行了分析,取得了較為準(zhǔn)確合理的分析結(jié)果,并發(fā)現(xiàn)了視神經(jīng)纖維束出現(xiàn)的DTI參數(shù)顯著性變化,說(shuō)明視神經(jīng)束可能出現(xiàn)髓鞘微損傷或軸突損傷,符合病人普遍視力減弱的臨床癥狀表現(xiàn),而這樣的病灶在MRI影像上無(wú)法看到,這對(duì)多發(fā)性硬化的早期評(píng)估及臨床診斷具有十分重要的意義。
[Abstract]:Diffusion Zhang Liang imaging (Diffusion Tensor Imaging,DTI) technology requires continuous application of diffusion gradient coding from multiple directions, which can measure the size of dispersion and investigate its directionality. Compared with a single diffusion-weighted image, it reflects the dispersion in the voxel more comprehensively. It can reflect the connectivity and integrity of brain tissue structure from the microscopic angle. DTI technique has shown great research value in pathological analysis of various central nervous system diseases. It can provide important reference for clinical treatment and prognosis evaluation, so it has attracted more and more attention. However, DTI technology is still mainly in the research stage of brain neuroscience, and its clinical process is mainly restricted by the bottleneck of quantitative analysis. The existing quantitative analysis methods of DTI have their own application limitations. In order to establish a more accurate and reasonable quantitative analysis model of DTI and provide reliable pathological diagnosis basis for diseases, the research work of this paper has been carried out. The main contents of this paper are as follows: on the one hand, an DTI quantitative analysis method based on voxel (Voxel-based analysis,VBA), which is widely studied and widely accepted at present, is used to analyze the algorithm and implement the flow chart. Aiming at the problem that the isotropic Gao Si kernel size in the smoothing step has no uniform standard and has a great influence on the analysis results, an anisotropic filtering algorithm is proposed, which is suitable for denoising DTI images and effectively preserves the characteristics of fiber bundles. The results are compared with those obtained by the conventional anisotropic filtering method. From the subjective and objective aspects, the root mean square error (RMS) and peak signal-to-noise ratio (PSNR) between images are used. Several image quality evaluation parameters, such as structural similarity index, verify the superiority of the proposed algorithm. On the other hand, the DTI quantitative analysis method based on fiber bundle spatial statistics (Tract-based spatial statistics,TBSS) is studied and implemented in recent years, and the VBA method based on the proposed anisotropic filter is combined. The fusion model of DTI quantitative analysis is established. According to the same DTI image preprocessing process and the same image registration template, the advantages of the two methods are synthesised. Not only can the location of the DTI parameters of the white matter bundle change significantly, but also the related anatomical changes in the diseased areas can be analyzed. Based on this fusion quantitative analysis model, the clinical DTI data of multiple sclerosis were analyzed, the results were more accurate and reasonable, and the significant changes of DTI parameters of optic nerve fiber bundle were found. It is suggested that the optic nerve bundle may have myelin microinjury or axonal injury, which is consistent with the clinical symptoms of the general impaired visual acuity of the patient, but such lesions cannot be seen on MRI images. This is of great significance for the early evaluation and clinical diagnosis of multiple sclerosis.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：R445.2

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