發(fā)布時(shí)間：2018-06-02 00:22

  本文選題：CWDS + MR-HOCM��； 參考：《西華大學(xué)》2017年碩士論文

【摘要】：1958年,Teare首次全面描述了肥厚型心肌病(Hypertrophic Cardiomyopathy,HCM),臨床中,根據(jù)靜息狀態(tài)下左室流出道壓力階差(Left Ventricle Outflow Tract Gradient,LVO TG)值將HCM分為肥厚梗阻型()和肥厚非梗阻型()。當(dāng)肥厚梗阻型心肌病(Hypertrophic Obstructive Cardiomyopathy,HOCM)患者混合其他病變?nèi)缍獍攴盗?Mitral Regurgitation,MR)時(shí),初級醫(yī)師易過高估計(jì)LVOT的最大瞬時(shí)峰值流速和LVOTG值,影響病情判斷和治療決策的最優(yōu)化。因此,在本研究室和第四軍醫(yī)大學(xué)第一附屬醫(yī)院西京醫(yī)院HCM研究小組前期研究工作成果的基礎(chǔ)上,本文致力于伴二尖瓣返流肥厚梗阻型心肌病(Hypertrophic Obstructive Cardiomyopathy with Mitral Regurgitation,MR-HOCM)患者LVOT連續(xù)多普勒頻譜圖(Continuous Wave Doppler Spectrum,CWDS-LVO T)的智能輔助測量,鑒別MR-HOCM患者的LVOT血流流速和LVO TG值時(shí)是否發(fā)生過高估計(jì)并對LVOTG值進(jìn)行矯正,同時(shí)自動(dòng)提取連續(xù)多普勒頻譜圖(Continuous Wave Doppler Spectrum,CWDS)的特征參數(shù),為LVOT血流動(dòng)力情況提供更加豐富的臨床信息。為了完成以上目標(biāo),本文主要從以下幾個(gè)方面展開深入的研究:(1)MR-HOCM患者CWDS-LVOT的采集和預(yù)處理深入研究MR-HOCM患者CWDS-LVOT的特點(diǎn)、采集方法及參數(shù)含義、LVOTG測量方法、準(zhǔn)確度和誤差原因;在第四軍醫(yī)大學(xué)第一附屬醫(yī)院西京醫(yī)院住院一部超聲科由剛?cè)肟撇怀^三年的初級超聲診斷醫(yī)師和具有超過二十年以上豐富經(jīng)驗(yàn)的專家超聲診斷醫(yī)師采集12名MR-HOCM患者CWDS-LVOT,并對LVOTG值的測量結(jié)果進(jìn)行確認(rèn)和記錄,并設(shè)計(jì)合理的降噪預(yù)處理方法,通過選取中值濾波,均值濾波和高斯曲率濾波三種圖像濾波方法進(jìn)行濾波效果的評估和對比,通過對比試驗(yàn),高斯曲率濾波(Gaussian Curvature Filter,GCF)在MR-HOCM患者CWDS-LVOT的降噪預(yù)處理上,效果明顯。(2)盲源分離(Blind Source Separation,BSS)算法及應(yīng)用深入研究BSS實(shí)現(xiàn)的基本模型、經(jīng)典算法FAST-ICA的數(shù)學(xué)理論和基本思想,研究算法的實(shí)現(xiàn)過程和相應(yīng)的實(shí)現(xiàn)步驟;探究BSS過程中出現(xiàn)的盲不確定性出現(xiàn)的原因;根據(jù)MR-HOCM患者CWDS-LVOT特點(diǎn),完成基于FAST-ICA的MR-HOCM患者CWDS-LVOT的信號分離算法和實(shí)驗(yàn)驗(yàn)證,同時(shí)使用占格率對估計(jì)源信號的之間的獨(dú)立性進(jìn)行了測度。將LVOTG估計(jì)值與專家診斷結(jié)果進(jìn)行對比分析,使用靈敏度和特異性指標(biāo),完成了算法準(zhǔn)確率的評估,并建立了基于Subspace discriminant集成分類器的算法評估模型。(3)MR-HOCM患者CWDS-LVOT特征參數(shù)的提取深入研究MR-HOCM患者CWDS-LVOT特征參數(shù)提取方法;利用單自由度模型提取MR-HOCM患者CWDS-LVOT的最大頻率曲線,基于提取得到的最大頻率曲線進(jìn)行8個(gè)特征參數(shù)的提取,提取的特征參數(shù)包括收縮期最大流速S、舒張末期最低流速D、收縮舒張流速比SD、LVOTG值、阻力指數(shù)RI、搏動(dòng)指數(shù)PI、收縮譜寬度W和收縮上升時(shí)間T,并利用得到的特征參數(shù)完成對血流狀況的評估。(4)系統(tǒng)界面設(shè)計(jì)采用MATLAB GUI設(shè)計(jì)一個(gè)MR-HOCM患者CWDS-LVOT的簡易分析鑒別系統(tǒng)。操作界面分LVOTG過高估計(jì)判定、特征參數(shù)提取和診斷分析報(bào)告三大部分。LVOTG過高估計(jì)判定部分,顯示原始MR-HOCM患者CWDS-LVOT、估計(jì)源信號圖、流速矯正后估計(jì)源信號和MR-HOCM患者CWDS-LVOT最大頻率曲線的對比圖以及矯正后流速和LVOTG值。特征參數(shù)提取和診斷分析報(bào)告部分,編寫相應(yīng)控件的回調(diào)函數(shù),結(jié)合相應(yīng)所需的臨床診斷參數(shù)和患者信息,將特征參數(shù)和分析結(jié)果圖生成WORD形式的分析報(bào)告。本文主要對MR-HOCM患者CWDS-LVOT進(jìn)行了深入的研究分析,建立了一個(gè)MR-HOCM患者CWDS-LVOT的流速簡易分析鑒別系統(tǒng),實(shí)現(xiàn)了對MR-HOCM患者CWDS-LVOT是否產(chǎn)生流速和LVOTG值過高估計(jì)的判定和矯正,同時(shí)還結(jié)合臨床所需,實(shí)現(xiàn)了MR-HOCM患者CWDS-LVOT部分特征參數(shù)的自動(dòng)提取,并通過實(shí)驗(yàn)進(jìn)一步驗(yàn)證了該系統(tǒng)的有效性和可行性。
[Abstract]:In 1958, Teare described the Hypertrophic Cardiomyopathy (HCM) for the first time. In clinical, the LVO pressure order (Left Ventricle Outflow Tract Gradient, LVO TG) is divided into hypertrophic obstructive () and hypertrophic non obstructive (Left). Cardiomyopathy, HOCM), when patients are mixed with other diseases such as Mitral Regurgitation (MR), the primary physician is prone to overestimate the maximum instantaneous peak velocity of LVOT and the LVOTG value, and affect the optimization of the condition judgment and treatment decision. Therefore, in this research room and the HCM research team of the Xijing Hospital of the First Affiliated Hospital of The Fourth Military Medical University On the basis of the results of the study, this paper aims to identify the blood flow of Hypertrophic Obstructive Cardiomyopathy with Mitral Regurgitation (MR-HOCM) patients with LVOT continuous Doppler spectrum (Continuous Wave Doppler Spectrum). Whether the flow rate and the LVO TG value are overestimated and correct the LVOTG value, and automatically extract the characteristic parameters of the continuous Doppler spectrum (Continuous Wave Doppler Spectrum, CWDS), provide more abundant clinical information for the LVOT blood flow power situation. Study: (1) the acquisition and preprocessing of CWDS-LVOT in MR-HOCM patients were deeply studied for the characteristics of CWDS-LVOT in MR-HOCM patients, the means of acquisition and parameters, the method of LVOTG measurement, the accuracy and the cause of error; a primary ultrasonic diagnostics in the Department of ultrasound of the First Affiliated Hospital of The Fourth Military Medical University, which was not more than three years in the Department of the Department of education. And expert ultrasonic diagnostics who have more than twenty years of experience to collect 12 MR-HOCM patients CWDS-LVOT, confirm and record the measurement results of LVOTG values, and design reasonable noise reduction preprocessing methods, and select the median filtering, mean filtering and Gauss curvature filtering for the filtering effect of three kinds of image filtering methods. Evaluation and comparison, the effect of Gaussian Curvature Filter (Filter, GCF) on CWDS-LVOT noise reduction preprocessing of MR-HOCM patients is obvious. (2) blind source separation (Blind Source Separation, BSS) algorithm and its application to the basic model of BSS implementation, the mathematical theory and basic idea of the classical algorithm FAST-ICA. The realization process of the algorithm and the corresponding implementation steps; explore the cause of the occurrence of the blind uncertainty in the BSS process. According to the CWDS-LVOT characteristics of the MR-HOCM patient, the signal separation algorithm and experimental verification of the CWDS-LVOT based on the MR-HOCM patient based on the FAST-ICA are completed, and the independence of the estimated source signal is measured using the duty rate. The LVOTG estimation is compared with the expert diagnosis results. Using sensitivity and specificity, the accuracy of the algorithm is evaluated, and an algorithm evaluation model based on the Subspace discriminant ensemble classifier is established. (3) the CWDS-LVOT feature parameters of the MR-HOCM patients are extracted and studied for the extraction of the CWDS-LVOT characteristic parameters of the MR-HOCM patients. Method: using the single degree of freedom model to extract the maximum frequency curve of CWDS-LVOT in MR-HOCM patients and extract the 8 characteristic parameters based on the maximum frequency curve extracted. The extracted feature parameters include the maximum systolic velocity S, the lowest end diastolic flow velocity D, the systolic and relaxation velocity ratio SD, the LVOTG value, the resistance index RI, the pulsation index PI, and the contraction. The spectrum width W and the systolic rise time T, and use the obtained characteristic parameters to evaluate the blood flow status. (4) the system interface design uses MATLAB GUI to design a simple analysis and identification system for MR-HOCM patients CWDS-LVOT. The operation interface is divided into LVOTG high estimation, feature extraction and diagnostic analysis report three most of the overestimation of.LVOTG In the decision section, the original MR-HOCM patient CWDS-LVOT was displayed, the source signal was estimated, the contrast map of the source signal and the CWDS-LVOT maximum frequency curve of the MR-HOCM patients after the correction of the flow rate and the corrected flow velocity and LVOTG value. The feature parameter extraction and the diagnosis analysis report part were used to compile the callback function of the corresponding control, and the corresponding clinical diagnosis was combined. The parameters and patient information are broken and the analysis report of the characteristic parameters and analysis results is generated in the form of WORD. In this paper, the MR-HOCM patient CWDS-LVOT is deeply studied and analyzed. A simple analysis and identification system for the flow velocity of CWDS-LVOT in the MR-HOCM patient is established to realize the flow velocity and the high LVOTG value for the CWDS-LVOT in the MR-HOCM patient. The estimation and correction of the MR-HOCM patients' CWDS-LVOT feature parameters are realized automatically, and the effectiveness and feasibility of the system are further verified by experiments.
【學(xué)位授予單位】：西華大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：R542.2;TP391.41

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 劉麗穩(wěn);杜占奎;王效增;;肥厚型心肌病的治療進(jìn)展[J];心血管康復(fù)醫(yī)學(xué)雜志;2015年03期

2 謝將劍;張軍國;董方舟;劉志明;王毅;;基于虛擬觀測的FastICA的墜砣位移的風(fēng)荷載分量去除[J];鐵道學(xué)報(bào);2014年07期

3 周愛琴;吳有秀;王小萍;葛貽瓏;李法權(quán);;肥厚型心肌病左室流出道壓差的超聲測量與有創(chuàng)測量的對比研究[J];贛南醫(yī)學(xué)院學(xué)報(bào);2014年03期

4 李慶;索曉華;呂京敏;鄭麗;;超聲評估左室流出道梗阻在肥厚型心肌病的臨床意義[J];北京醫(yī)學(xué);2014年04期

5 ;《超聲入門20元書系》系列書出版[J];醫(yī)學(xué)影像學(xué)雜志;2012年05期

6 李海燕;馬悅;張榆鋒;施心陵;;MP-PCNN超聲多普勒血流信號聲譜圖降噪去斑方法[J];生物醫(yī)學(xué)工程學(xué)雜志;2011年05期

7 趙高長;張磊;武風(fēng)波;;改進(jìn)的中值濾波算法在圖像去噪中的應(yīng)用[J];應(yīng)用光學(xué);2011年04期

8 王科俊;熊新炎;任楨;;高效均值濾波算法[J];計(jì)算機(jī)應(yīng)用研究;2010年02期

9 翁曉光;王惠南;;改進(jìn)的ICA算法及其在fMRI信號上的應(yīng)用[J];華南理工大學(xué)學(xué)報(bào)(自然科學(xué)版);2009年05期

10 王衛(wèi)華;黃鳳崗;王桐;;改進(jìn)的頻域盲分離排序不確定性消除算法[J];系統(tǒng)仿真學(xué)報(bào);2009年02期

相關(guān)博士學(xué)位論文 前1條

1 趙濤;磁共振成像在肥厚性心肌病中的臨床應(yīng)用研究[D];北京協(xié)和醫(yī)學(xué)院;2013年

相關(guān)碩士學(xué)位論文 前6條

1 石文斌;目標(biāo)信號分離技術(shù)研究[D];解放軍信息工程大學(xué);2012年

2 劉曉偉;車輛結(jié)構(gòu)振動(dòng)與噪聲源的盲分離技術(shù)研究[D];南京航空航天大學(xué);2012年

3 尹釗;超聲多普勒血流信號估計(jì)及處理方法研究[D];哈爾濱工業(yè)大學(xué);2011年

4 王兵;腦電信號中偽跡去除的綜合研究[D];浙江大學(xué);2010年

5 梁盛德;基于超聲多普勒效應(yīng)血流信號處理方法的研究[D];陜西師范大學(xué);2008年

6 畢楊;基于快速獨(dú)立分量分析的盲源分離算法研究及應(yīng)用[D];西安理工大學(xué);2007年

，

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