高分辨率CT定性、定量技術評估慢性阻塞性肺疾病的臨床應用價值
發(fā)布時間：2018-07-25 19:08
【摘要】：第一部分高分辨率CT定量技術評估慢性阻塞性肺疾病的臨床應用價值目的:利用高分辨率 CT(High-resolution computed tomography,HRCT)容積定量技術自動測量每個肺葉的低密度區(qū)容積百分比(low attenuation areas volume percentage,LA A%),所得定量參數(shù)與肺功能(pulmonary function test,PFT)各項參數(shù)進行相關性研究,從而綜合評估慢性阻塞性肺疾病(Chronic obstructive pulmonary disease,COPD)患者肺功能損傷的嚴重程度,對臨床制定診療方案提供有價值的影像支持。材料和方法:收集2015年12月至2016年12月期間經(jīng)臨床及肺功能檢查確診的COPD患者83例(所有患者均于7天內(nèi)完成HRCT檢查及肺功能檢查)。所有病例都采用GE 64排Lightspeed VCT于深吸氣末進行掃描,把HRCT原始數(shù)據(jù)傳到后處理工作站GEadw4.6,系統(tǒng)設定吸氣末CT閾值-950HU為肺氣腫區(qū),運用CT后處理Parenchyma analysis軟件自動得出以下參數(shù):包括左肺下葉LAA%、左肺上葉LAA%、右肺上葉LAA%、右肺下葉LAA%、右肺中葉LAA%、雙肺LAA%、右肺 LAA%、左肺 LAA%,總肺氣腫體積(the total emphysema volume,TEV),總肺體積(Total Lung Volume,TLV)。本文主要采用的肺功能參數(shù)如下:第一秒用力呼氣容積(forced expiratory volume in one second,FEV1)、第一秒用力呼氣容積實測值和預計值的比值(FEV1%預測值)、用力肺活量(forced vital capacity,FVC)、FEV1與FVC的比值(FEV1/FVC)、呼氣流量峰值(peak expiratory flow,PEF)、用力呼出 25%肺活量時呼氣流量(forced expiratory flow at 25%of FVC exhaled,FEF25)、用力呼出 50%肺活量時呼氣流量(forced expiratory flow at 50%of FVC exhaled,FEF50)、用力呼出 75%肺活量時呼氣流量(forced expiratory flow at 75%of FVC exhaled,FEF75)、肺一氧化碳彌散量(diffusion capacity of carbon monoxide in the lung,DLCO)實測值占預計值百分比(DLCO%)、殘總比即殘氣容積與肺總量的比值(RV/TLC)。本研究把肺功能參數(shù)和之前提到的CT定量化參數(shù)進行Spearman相關性研究。COPD不同分級間CT定量化參數(shù)的差異性分析應用單因素方差分析法(One-Way ANOVA),組間比較采用LSD檢驗。采用Kruskal-Wallis檢驗對COPD不同分級間FEV1/FVC、FEV1%預測值、BMI等進行比較分析。本研究統(tǒng)計學用SPSS20.0軟件分析,p0.05認為差異有統(tǒng)計學意義,p0.01認為差異有顯著統(tǒng)計學意義。結果:83例患者,年齡47～85歲,平均年齡66歲,性別均為男性,BMI指數(shù)13.3～28.1,吸煙者71例,不吸煙者12例,根據(jù)2017年COPD分級標準,該組病例中:GOLD1級8 例;GOLD2 級 33 例;GOLD3 級 27 例;GOLD 4 級 15 例。左肺下葉LAA%、左肺LAA%、右肺下葉LAA%、右肺LAA%、總肺LAA%與FEV1/FVC、FEV1、FEV1%預計值、FEF25、FEF50均有相關性,兩肺下葉與PEF、FEF75也有相關性,兩肺下葉LAA%與肺功能氣流受限參數(shù)(FEV1、FEV1%預測值、FEV1/FVC、PEF、FEF50)呈顯著相關。FEV1/FVC與TEV顯著相關(r=0.759**,P0.001)。TLV 與 TLC 顯著相關(r=-0.355,**P=0.001)。DLCO%與兩肺上葉LAA%有相關性(r=-0.473,P=0.026)。兩肺下葉LAA%在GOLD1級與GOLD3級間差異有統(tǒng)計學意義,左肺下葉LAA%在GOLD 1和GOLD4級之間差異有統(tǒng)計學意義。除了右肺中葉LAA%外,其他各肺葉LAA%在GOLD2級與GOLD3級間差異均有統(tǒng)計學意義�？偡蜭AA%、左肺下葉LAA%、右肺上LAA%在GOLD2級與GOLD4級間差異有統(tǒng)計學意義。FEV1/FVC、FEV1%預測值在GOLD各組間差異有統(tǒng)計學意義。結論:兩肺下葉LAA%與肺功能氣流受限參數(shù)(FEV1、FEV1%預測值、FEV1/FVC、FEF50、PEF)均具有顯著相關性,與RV/TLC、DLCO%無明顯相關性。兩肺上葉與DLCO%有相關性。因此提示不同肺葉LAA%可評估COPD病人肺功能的損傷部位和損傷嚴重情況,從而為臨床制定不同治療方案提供相關依據(jù)。第二部分高分辨率CT圖像利用計算機后處理技術定性肺氣腫亞型目的:通過計算機后處理技術為臨床提供一種可重復、無偏倚、更精準的肺氣腫亞型的自動識別方法,以深入認識肺氣腫不同形態(tài)及不同病變程度,從而為臨床醫(yī)生在制定COPD患者的個性化治療方案上提供了一種新的思路。材料和方法:采用GE64排LightspeedVCT于深吸氣末進行全肺掃描,采集病例組(肺氣腫典型病例)6例(每種亞型為主型各2例)及正常組6例,并將重建后數(shù)據(jù)導入ITK-SNAP軟件,然后利用不同的顏色代表不同的肺氣腫亞型及正常肺組織進而進行圖像標注:正常的肺組織(Normaltissue,NT)使用紅色標注,小葉中心型肺氣腫(Centrilobular Emphysema,CLE)使用綠色標注,全小葉型肺氣腫(Panlobular Emphysema,PLE)利用藍色標注,間隔旁型肺氣腫(Paraseptal Emphysema,PSE)利用黃色進行標注。在全部標注結束的病例組中每個病例隨機取1000個不重疊的異常ROI(Region of interest),每個異常ROI都有一個對應的標簽(label):包括CLE、PLE、PSE,采集總共6000個異常ROI。病例組中另外再取1000個不重疊的正常肺組織(Normaltissue,NT)ROI。正常組中隨機取1000個不重疊的正常組織(NT)的ROI。病例組中從6000個異常ROI中每類肺氣腫亞型(CLE,PSE,PLE)隨機選1000個ROI,共3000個,加上正常組的1000個ROI作為訓練樣本(training samples),從而訓練出可以自動識別肺氣腫亞型的分類器。然后在病例組中剩下的3000個異常ROI及1000個正常組織ROI中,每次每類肺氣腫亞型(CLE,PSE,PLE)及正常肺組織(NT)隨機選200個ROI作為測試樣本(test samples)。利用計算機后處理 Intensity(INT)、Rotation invariant Local Binary Patterns(RILBPs)、INT+RILBPs三種方法分別自動識別各種肺氣腫亞型,測試結果與人工標注結果進行對比并計算分類精度,以上測試實驗重復5次,以5次分類精度的平均值作為最終分類精度。結果:利用計算機后處理INT、RILBPs、INT+RILBPs三種方法分別自動識別肺氣腫亞型的分類精度如下:只提取CLE、PLE、PSE三種肺氣腫亞型進行測試,測得精度 INT 法為 88.28%,RILBPs 法為 86.46%,INT+RILBPs 法 94.62%;三種方法中均以小葉中心型肺氣腫(CLE)的分類精度最高。加入正常組織(NT)后分類精度下降,INT 法為 72.09%,RILBPs 法為 67.34%,INT+RILBPs 法 84.29%。該組中除了 INT法中以全小葉型肺氣腫(PLE)分類精度最高外,其他兩種方法中均為CLE分類精度最高。由此可見INT+RILBPs這一方法分類精度最高,在肺氣腫亞型中以CLE的分類精度較高。結論:利用計算機后處理INT、RILBPs、INT+RILBPs三種方法對比分析計算機自動識別肺氣腫亞型分類精度,INT+RILBPs法分類精度明顯高于其他兩種方法;INT+RILBPs法僅提取CLE、PLE、PSE三種肺氣腫亞型測試結果明顯高于加入正常肺組織后的分類精度。INT+RILBPs這一方法在自動識別肺氣腫亞型上可以為臨床提供診斷依據(jù)。
[Abstract]:The first part of the high resolution CT quantitative technique to evaluate the clinical application of chronic obstructive pulmonary disease: using the high resolution CT (High-resolution computed tomography, HRCT) volume quantitative technique to automatically measure the volume percentage of the low density region of each lobe (low attenuation areas volume percentage, LA), and the quantitative parameters Correlation study with pulmonary function test (PFT) parameters to assess the severity of pulmonary function damage in patients with chronic obstructive pulmonary disease (Chronic, obstructive pulmonary disease, COPD), and provide valuable imaging support for clinical diagnosis and treatment programs. Materials and methods: from December 2015 to 2016 In December, 83 patients with COPD were diagnosed with clinical and pulmonary function tests (all patients completed HRCT examination and pulmonary function examination within 7 days). All cases were scanned by GE 64 row Lightspeed VCT at the deep inhalation terminal. HRCT original data were transmitted to the post processing workstation GEadw4.6, and the system set the terminal CT threshold -950HU to emphysema. The following parameters were obtained by using CT after processing Parenchyma analysis software, including left lower lobe LAA%, left upper lobe LAA%, right upper lobe LAA%, right lower lobe LAA%, right pulmonary middle lobe LAA%, double lung LAA%, right lung LAA%, left lung LAA%, total emphysema volume, total lung volume. The parameters of the lung function are as follows: the first second forced expiratory volume (forced expiratory volume in one second, FEV1), the ratio of the measured value of the forced expiratory volume to the predicted value (FEV1% prediction), the forced vital capacity (forced vital capacity, FVC), the ratio of the expiratory volume, the peak expiratory flow, and the force of the expiratory flow, and the peak expiratory flow peak. Expiratory flow (forced expiratory flow at 25%of FVC exhaled, FEF25), expiratory expiratory flow of 50% vital capacity (forced expiratory flow at), expiratory exhalation of 75% vital capacity, lung carbon monoxide dispersion Usion capacity of carbon monoxide in the lung, DLCO) measured value as a percentage of expected value (DLCO%), the ratio of residual volume to total volume of lung (RV/TLC). Factor variance analysis (One-Way ANOVA), LSD test was used among groups. Kruskal-Wallis test was used to compare FEV1/FVC, FEV1% prediction value, BMI and so on. This study was analyzed by SPSS20.0 software, P0.05 thought the difference was statistically significant, P0.01 thought the difference was significant statistically. Results: 83 cases People aged 47~85, age 47~85, average age 66 years, sex are male, BMI index 13.3 ~ 28.1, smokers 71 cases, non smokers 12 cases, according to the 2017 COPD grading standard, the group cases: GOLD1 class 8 cases, GOLD2 class 33 cases, GOLD3 class 27 cases, GOLD 4 level 15 cases, left lung LAA%, right lower lobe LAA%, right lung LAA%, LAA% lung and FEV1/F. VC, FEV1, FEV1% predicted values, FEF25 and FEF50 were correlated. Two the lower lobe of the lung was also associated with PEF and FEF75. Two lower lobe LAA% and lung function limited parameters (FEV1, FEV1% predictive value, FEV1/FVC, PEF) were significantly correlated with the two lung The leaf LAA% was correlated (r=-0.473, P=0.026). Two the difference between LAA% in the lower lobe of the lung in GOLD1 and GOLD3 was statistically significant. The difference between the LAA% in the lower lobe of the left lung was statistically significant between GOLD 1 and GOLD4. Except for the middle lobe LAA% of the right lung, the difference between the other lung lobes was statistically significant between the GOLD2 level and the grade. There was a significant difference between the GOLD2 and GOLD4 levels on the right lung LAA%, and the difference between the FEV1% prediction values in the GOLD group was statistically significant. Conclusion: two the LAA% in the lower lobe of the lung has a significant correlation with the limited parameters of the pulmonary function (FEV1, FEV1% prediction, FEV1/FVC, FEF50, PEF), and there is no significant correlation with the lung function. Two the upper lobe of the lung is not significantly correlated. It is pertinence. Therefore, it is suggested that different pulmonary lobectomy LAA% can evaluate the damage location and serious damage of lung function in COPD patients, and provide the relevant basis for the clinical formulation of different treatment schemes. The second part of high resolution CT image is used by computer post-processing technique to determine the subtype of emphysema. An automatic identification of reduplicated, unbiased, and more accurate emphysema subtypes to deeply understand the different morphologic and pathological changes of emphysema, thus providing a new idea for clinicians to develop individualized treatment schemes for COPD patients. Materials and methods: GE64 row LightspeedVCT at the end of deep inhalation for the whole lung 6 cases (typical cases of emphysema) (2 cases of emphysema) and 6 cases of normal group were collected, and the reconstructed data were introduced into ITK-SNAP software, and then different colors representing different emphysema subtypes and normal lung tissues were used for image tagging: normal lung tissue (Normaltissue, NT) used red label, lobule. Centrilobular Emphysema (CLE) used green tagging, full lobular emphysema (Panlobular Emphysema, PLE) using blue tagging, paranatal emphysema (Paraseptal Emphysema, PSE) using yellow for tagging. 1000 unoverlapped abnormal ROI (Region of) at the end of all the cases were randomly selected. Interest), each abnormal ROI has a corresponding label (label): including CLE, PLE, PSE, and collect 1000 unoverlapped normal lung tissues (Normaltissue, NT) ROI. in a total of 6000 non overlapping normal groups (Normaltissue, NT) ROI., and take 1000 non overlapping normal tissues (NT) in the ROI. case group from 6000 abnormal lungs in each type of emphysema. The subtype (CLE, PSE, PLE) randomly selected 1000 ROI, a total of 3000, and 1000 ROI of the normal group as a training sample (training samples), and then trained the classifier that could automatically identify the emphysema subtype. Then the 3000 remaining ROI and 1000 normal tissue ROI were left in the case group, and each type of emphysema subtype (CLE, PSE, PLE) and positive was in each class. NT (NT) was randomly selected as a test sample (test samples). Using a computer to process Intensity (INT), Rotation invariant Local Binary Patterns (RILBPs), three methods were used to automatically identify the various emphysema subtypes. The test results were compared with the artificial annotation results and the classification accuracy was calculated. The above tests were tested. The experiment was repeated 5 times and the average value of 5 classification accuracy was used as the final classification accuracy. Results: the classification accuracy of the subtype of emphysema was automatically identified by three methods of computer post-processing INT, RILBPs and INT+RILBPs, respectively: only CLE, PLE, PSE, three emphysema subtypes were tested, the accuracy INT method was 88.28%, RILBPs method was 86.46%, I. NT+RILBPs method 94.62%; among the three methods, the classification accuracy of the lobular central emphysema (CLE) was the highest. The classification accuracy decreased after adding the normal tissue (NT), the INT method was 72.09%, the RILBPs method was 67.34%, and the INT+RILBPs method 84.29%. had the highest classification accuracy except for the total lobular emphysema (PLE) in the INT method, and the other two methods were all. The classification precision of CLE is the highest. This shows that INT+RILBPs has the highest classification precision, and the classification precision of CLE is higher in emphysema subtype. Conclusion: computer after processing INT, RILBPs, INT+RILBPs three methods to compare and analyze the classification accuracy of emphysema subtype by computer, the precision of INT+RILBPs method is obviously higher than that of other two. The INT+RILBPs method only extracts CLE, PLE, and PSE three emphysema subtypes, which is significantly higher than the classification precision.INT+RILBPs after adding normal lung tissue. This method can provide diagnostic basis for the automatic identification of emphysema subtype.
【學位授予單位】：浙江大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：R563.9;R816.41
本文編號：2144793
[Abstract]:The first part of the high resolution CT quantitative technique to evaluate the clinical application of chronic obstructive pulmonary disease: using the high resolution CT (High-resolution computed tomography, HRCT) volume quantitative technique to automatically measure the volume percentage of the low density region of each lobe (low attenuation areas volume percentage, LA), and the quantitative parameters Correlation study with pulmonary function test (PFT) parameters to assess the severity of pulmonary function damage in patients with chronic obstructive pulmonary disease (Chronic, obstructive pulmonary disease, COPD), and provide valuable imaging support for clinical diagnosis and treatment programs. Materials and methods: from December 2015 to 2016 In December, 83 patients with COPD were diagnosed with clinical and pulmonary function tests (all patients completed HRCT examination and pulmonary function examination within 7 days). All cases were scanned by GE 64 row Lightspeed VCT at the deep inhalation terminal. HRCT original data were transmitted to the post processing workstation GEadw4.6, and the system set the terminal CT threshold -950HU to emphysema. The following parameters were obtained by using CT after processing Parenchyma analysis software, including left lower lobe LAA%, left upper lobe LAA%, right upper lobe LAA%, right lower lobe LAA%, right pulmonary middle lobe LAA%, double lung LAA%, right lung LAA%, left lung LAA%, total emphysema volume, total lung volume. The parameters of the lung function are as follows: the first second forced expiratory volume (forced expiratory volume in one second, FEV1), the ratio of the measured value of the forced expiratory volume to the predicted value (FEV1% prediction), the forced vital capacity (forced vital capacity, FVC), the ratio of the expiratory volume, the peak expiratory flow, and the force of the expiratory flow, and the peak expiratory flow peak. Expiratory flow (forced expiratory flow at 25%of FVC exhaled, FEF25), expiratory expiratory flow of 50% vital capacity (forced expiratory flow at), expiratory exhalation of 75% vital capacity, lung carbon monoxide dispersion Usion capacity of carbon monoxide in the lung, DLCO) measured value as a percentage of expected value (DLCO%), the ratio of residual volume to total volume of lung (RV/TLC). Factor variance analysis (One-Way ANOVA), LSD test was used among groups. Kruskal-Wallis test was used to compare FEV1/FVC, FEV1% prediction value, BMI and so on. This study was analyzed by SPSS20.0 software, P0.05 thought the difference was statistically significant, P0.01 thought the difference was significant statistically. Results: 83 cases People aged 47~85, age 47~85, average age 66 years, sex are male, BMI index 13.3 ~ 28.1, smokers 71 cases, non smokers 12 cases, according to the 2017 COPD grading standard, the group cases: GOLD1 class 8 cases, GOLD2 class 33 cases, GOLD3 class 27 cases, GOLD 4 level 15 cases, left lung LAA%, right lower lobe LAA%, right lung LAA%, LAA% lung and FEV1/F. VC, FEV1, FEV1% predicted values, FEF25 and FEF50 were correlated. Two the lower lobe of the lung was also associated with PEF and FEF75. Two lower lobe LAA% and lung function limited parameters (FEV1, FEV1% predictive value, FEV1/FVC, PEF) were significantly correlated with the two lung The leaf LAA% was correlated (r=-0.473, P=0.026). Two the difference between LAA% in the lower lobe of the lung in GOLD1 and GOLD3 was statistically significant. The difference between the LAA% in the lower lobe of the left lung was statistically significant between GOLD 1 and GOLD4. Except for the middle lobe LAA% of the right lung, the difference between the other lung lobes was statistically significant between the GOLD2 level and the grade. There was a significant difference between the GOLD2 and GOLD4 levels on the right lung LAA%, and the difference between the FEV1% prediction values in the GOLD group was statistically significant. Conclusion: two the LAA% in the lower lobe of the lung has a significant correlation with the limited parameters of the pulmonary function (FEV1, FEV1% prediction, FEV1/FVC, FEF50, PEF), and there is no significant correlation with the lung function. Two the upper lobe of the lung is not significantly correlated. It is pertinence. Therefore, it is suggested that different pulmonary lobectomy LAA% can evaluate the damage location and serious damage of lung function in COPD patients, and provide the relevant basis for the clinical formulation of different treatment schemes. The second part of high resolution CT image is used by computer post-processing technique to determine the subtype of emphysema. An automatic identification of reduplicated, unbiased, and more accurate emphysema subtypes to deeply understand the different morphologic and pathological changes of emphysema, thus providing a new idea for clinicians to develop individualized treatment schemes for COPD patients. Materials and methods: GE64 row LightspeedVCT at the end of deep inhalation for the whole lung 6 cases (typical cases of emphysema) (2 cases of emphysema) and 6 cases of normal group were collected, and the reconstructed data were introduced into ITK-SNAP software, and then different colors representing different emphysema subtypes and normal lung tissues were used for image tagging: normal lung tissue (Normaltissue, NT) used red label, lobule. Centrilobular Emphysema (CLE) used green tagging, full lobular emphysema (Panlobular Emphysema, PLE) using blue tagging, paranatal emphysema (Paraseptal Emphysema, PSE) using yellow for tagging. 1000 unoverlapped abnormal ROI (Region of) at the end of all the cases were randomly selected. Interest), each abnormal ROI has a corresponding label (label): including CLE, PLE, PSE, and collect 1000 unoverlapped normal lung tissues (Normaltissue, NT) ROI. in a total of 6000 non overlapping normal groups (Normaltissue, NT) ROI., and take 1000 non overlapping normal tissues (NT) in the ROI. case group from 6000 abnormal lungs in each type of emphysema. The subtype (CLE, PSE, PLE) randomly selected 1000 ROI, a total of 3000, and 1000 ROI of the normal group as a training sample (training samples), and then trained the classifier that could automatically identify the emphysema subtype. Then the 3000 remaining ROI and 1000 normal tissue ROI were left in the case group, and each type of emphysema subtype (CLE, PSE, PLE) and positive was in each class. NT (NT) was randomly selected as a test sample (test samples). Using a computer to process Intensity (INT), Rotation invariant Local Binary Patterns (RILBPs), three methods were used to automatically identify the various emphysema subtypes. The test results were compared with the artificial annotation results and the classification accuracy was calculated. The above tests were tested. The experiment was repeated 5 times and the average value of 5 classification accuracy was used as the final classification accuracy. Results: the classification accuracy of the subtype of emphysema was automatically identified by three methods of computer post-processing INT, RILBPs and INT+RILBPs, respectively: only CLE, PLE, PSE, three emphysema subtypes were tested, the accuracy INT method was 88.28%, RILBPs method was 86.46%, I. NT+RILBPs method 94.62%; among the three methods, the classification accuracy of the lobular central emphysema (CLE) was the highest. The classification accuracy decreased after adding the normal tissue (NT), the INT method was 72.09%, the RILBPs method was 67.34%, and the INT+RILBPs method 84.29%. had the highest classification accuracy except for the total lobular emphysema (PLE) in the INT method, and the other two methods were all. The classification precision of CLE is the highest. This shows that INT+RILBPs has the highest classification precision, and the classification precision of CLE is higher in emphysema subtype. Conclusion: computer after processing INT, RILBPs, INT+RILBPs three methods to compare and analyze the classification accuracy of emphysema subtype by computer, the precision of INT+RILBPs method is obviously higher than that of other two. The INT+RILBPs method only extracts CLE, PLE, and PSE three emphysema subtypes, which is significantly higher than the classification precision.INT+RILBPs after adding normal lung tissue. This method can provide diagnostic basis for the automatic identification of emphysema subtype.
【學位授予單位】：浙江大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：R563.9;R816.41
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