胸部CT结合AI诊断系统对疑似肺结节患者的诊断及对结节类型的评估价值

摘要 目的：探讨胸部CT结合AI诊断系统对疑似肺结节患者的诊断及对结节类型的评估价值。方法：选取2019年12月-2020年12月在我院进行CT检查的358例疑似肺结节患者,将其按照随机数字表法分为两组：对照组（放射科医生根据CT扫描结果,通过人工阅片分析记录检出结节数量和影像特征）,观察组（将CT扫描结果导入AI辅助诊断系统,经AI运算得到结节检出数量和影像特征）。AI辅助系统IMsight用于肺结节的图像分析和自动检测。通过组织病理学确定结节的良恶性。绘制受试者工作特征曲线（ROC）曲线以评估AI和CT结合图像的诊断价值。结果：病理结果最后确诊结节数量736个,恶性结节139个（18.89 %）,良性结节597个（81.11 %）。观察组诊断结节数量717个,检出率97.42%,对照组诊断出结节数量603个,检出率81.93 %。观察组较对照组的结节检出率、阳性检出率升高（P<0.05）,漏检率和假阴性率均显著降低（P<0.05）。当结节小于10 mm时,观察组较对照组的检出率升高（P<0.05）,观察组较对照组对磨玻璃密度结节和实性结节检出率升高（P<0.05）,观察组较对照组位于胸膜结节检出率升高（P<0.05）。观察组较对照组AUC（P<0.05）,表明AI系统下的结节检出准确率高。ROC曲线显示观察组的敏感性和特异性分别为88.39%和89.68 %,对照组的敏感性和特异性分别为75.24 %和82.34 %,观察组较对照组的ROC曲线敏感性和特异性升高（P<0.05）。结论：AI辅助诊断系统可有效提高肺结节的检出率,减少误检率及漏检率,值得在肺结节CT检测中应用推广。     

Automatic detection of pulmonary nodules in CT images by incorporating 3D tensor filtering with local image feature analysis

Computer-aided detection (CAD) technology has been developed and demonstrated its potential to assist radiologists in detecting pulmonary nodules especially at an early stage. In this paper, we present a novel scheme for automatic detection of pulmonary nodules in CT images based on a 3D tensor filtering algorithm and local image feature analysis. We first apply a series of preprocessing steps to segment the lung volume and generate the isotropic volumetric CT data. Next, a unique 3D tensor filtering approach and local image feature analysis are used to detect nodule candidates. A 3D level set segmentation method is used to correct and refine the boundaries of nodule candidates subsequently. Then, we extract the features of the detected candidates and select the optimal features by using a CFS (Correlation Feature Selection) subset evaluator attribute selection method. Finally, a random forest classifier is trained to classify the detected candidates. The performance of this CAD scheme is validated using two datasets namely, the LUNA16 (Lung Nodule Analysis 2016) database and the ANODE09 (Automatic Nodule Detection 2009) database. By applying a 10-fold cross-validation method, the CAD scheme yielded a sensitivity of 79.3% at an average of 4 false positive detections per scan (FP/Scan) for the former dataset, and a sensitivity of 84.62% and 2.8 FP/Scan for the latter dataset, respectively. Our detection results show that the use of 3D tensor filtering algorithm combined with local image feature analysis constitutes an effective approach to detect pulmonary nodules.     

基于三维卷积神经网络的肺结节探测与定位方法

提出一种基于三维卷积神经网络对肺部计算机断层扫描图像(CT)进行肺结节自动探测及定位的方法.基于开源数据集LUNA16开展研究,对数据进行像素归一化、坐标转换等预处理,对正样本使用随机平移、旋转和翻转的方式进行扩充,对负样本进行随机采样.搭建了三维卷积神经网络并在训练过程中调整网络参数,直到得到性能最佳的网络.此外还设...     

Can Spectral CT Imaging Improve the Differentiation between Malignant and Benign Solitary Pulmonary Nodules?

Purpose

To quantitatively assess the value of dual-energy CT (DECT) in differentiating malignancy and benignity of solitary pulmonary nodules.

Materials and Methods

Sixty-three patients with solitary pulmonary nodules detected by CT plain scan underwent contrast enhanced CT scans in arterial phase (AP) and venous phase (VP) with spectral imaging mode for tumor type differentiation. The Gemstone Spectral Imaging (GSI) viewer was used for image display and data analysis. Region of interest was placed on the relatively homogeneous area of the nodule to measure iodine concentration (IC) on iodine-based material decomposition images and CT numbers on monochromatic image sets to generate spectral HU curve. Normalized IC (NIC), slope of the spectral HU curve (λ_HU) and net CT number enhancement on 70keV images were calculated. The two-sample t-test was used to compare quantitative parameters. Receiver operating characteristic curves were generated to calculate sensitivity and specificity.

Results

There were 63 nodules, with 37 malignant nodules (59%) and 26 benign nodules (41%). NIC, λ_HU and net CT number enhancement on 70keV images for malignant nodules were all greater than those of benign nodules. NIC and λ_HU had intermediate to high performances to differentiate malignant nodules from benign ones with the areas under curve of 0.89 and 0.86 respectively in AP, 0.96 and 0.89 respectively in VP. Using 0.30 as a threshold value for NIC in VP, one could obtain sensitivity of 93.8% and specificity of 85.7% for differentiating malignant from benign solitary pulmonary nodules. These values were statistically higher than the corresponding values of 74.2% and 53.8% obtained with the conventional CT number enhancement.

Conclusions

DECT imaging with GSI mode provides more promising value in quantitative way for distinguishing malignant nodules from benign ones than CT enhancement numbers.     

Pulmonary Nodule Detection in Patients with a Primary Malignancy Using Hybrid PET/MRI: Is There Value in Adding Contrast-Enhanced MR Imaging?

Purpose

To investigate the added value of post-contrast VIBE (volumetric-interpolated breath-hold examination) to PET/MR imaging for pulmonary nodule detection in patients with primary malignancies.

Materials and Methods

This retrospective institutional review board–approved study, with waiver of informed consent, included 51 consecutive patients who underwent ¹⁸F-fluorodeoxyglucose (FDG) PET/MR followed by PET/CT for cancer staging. In all patients, the thorax was examined with pre-and post-contrast VIBE MR with simultaneous PET acquisition. Two readers blinded to the patients’ data independently recorded their level of suspicion for pulmonary nodules based on PET, pre-contrast VIBE, and fused PET/MR images (first session), and reassessed them 4-weeks later after addition of post-contrast VIBE (second session). Jackknife alternative free-response receiver-operating-characteristic (JAFROC) analysis was performed, with PET/CT as the reference standard.

Results

A total of 151 pulmonary nodules (44 FDG-avid, 107 non-FDG-avid nodules) were detected on PET/CT, including 62 nodules≥5mm in diameter and 89 nodules<5mm. In the first session, the average nodule detection rate was 53.3% for all nodules, 97.7% for FDG-avid, 35.0% for non-FDG-avid nodules, 87.9% for nodules≥5mm and 29.2% for nodules<5mm. In the second session, the average detection rate was 53.3% for all nodules, 97.7% for FDG-avid, 35.0% for non-FDG-avid nodules, 85.5% for nodules≥5mm and 30.9% for nodules<5mm. The average JAFROC figure-of-merit was 0.837 in the first session and 0.848 in the second session. There were no significant differences in detection performance between sessions (P=0.48).

Conclusion

The addition of post-contrast VIBE to hybrid PET/MR imaging provided no additional value in the detection of pulmonary nodules.     

128层螺旋CT 低剂量扫描检查孤立性肺内结节性病灶的研究

目的:探讨多排螺旋CT(128排)低剂量扫描参数下检查孤立性肺内结节的可行性研究。方法:随机连续搜集我院低剂量(30m A)CT肺体检者,发现肺内结节病灶患者13例,对其进一步以常规剂量(350m A)CT精细扫描,比较低剂量扫描及常规剂量扫描肺结节大小差异。结果:两种剂量扫描策略均检查出46枚结节。常规剂量与低剂量测得各部位结节体积分别为:肺尖部:(431.3±92.8)mm~3,(658.4±94.4)mm~3,肺中部:(3025.8±526.7)mm~3,(2989.4±520.4)mm~3,肺底部:(1241.5±438.9)mm~3,(1266.0±447.6)mm~3,肺尖部肺结节大小差异明显,肺中部及肺底部肺结节大小均无显著性差异(P0.05)。常规剂量与低剂量测得结节体积(除外肺尖部位结节5枚)分别为,组1:(39.8±14.6)mm~3,(40.7±15.5)mm~3;组2:(202±106.3)mm~3,(204.1±103.6)mm~3;组3:(4179.7±4410.4)mm~3,(4190.5±4487.2)mm~3。三组组内测量结果均无显著性差异(P0.05)。常规剂量与低剂量测得非实性结节密度[(-68.3±24.2)HU,(-64.6±22.8)HU]及实性结节结节密度[(97.5±69.5)HU,(107.2±90)HU]均无统计学差异(P0.05)。结论:低剂量更加有利于肺内孤立结节患者扫描复查病灶,可以应用推广。     

Utility of fast non-local means (FNLM) filter for detection of pulmonary nodules in chest CT for pediatric patient

PurposeThis study was aimed to evaluate the utility based on imaging quality of the fast non-local means (FNLM) filter in diagnosing lung nodules in pediatric chest computed tomography (CT).MethodsWe retrospectively reviewed the chest CT reconstructed with both filtered back projection (FBP) and iterative reconstruction (IR) in pediatric patients with metastatic lung nodules. After applying FNLM filter with six h values (0.0001, 0.001, 0.01, 0.1, 1, and 10) to the FBP images, eight sets of images including FBP, IR, and FNLM were analyzed. The image quality of the lung nodules was evaluated objectively for coefficient of variation (COV), contrast to noise ratio (CNR), and point spread function (PSF), and subjectively for noise, sharpness, artifacts, and diagnostic acceptability.ResultsThe COV was lowest in IR images and decreased according to increasing h values and highest with FBP images (P < 0.001). The CNR was highest with IR images, increased according to increasing h values and lowest with FBP images (P < 0.001). The PSF was lower only in FNLM filter with h value of 0.0001 or 0.001 than in IR images (P < 0.001). In subjective analysis, only images of FNLM filter with h value of 0.0001 or 0.001 rarely showed unacceptable quality and had comparable results with IR images. There were less artifacts in FNLM images with h value of 0.0001 compared with IR images (p < 0.001).ConclusionFNLM filter with h values of 0.0001 allows comparable image quality with less artifacts compared with IR in diagnosing metastatic lung nodules in pediatric chest CT.     

Application of CT images in the diagnosis of lung cancer based on finite mixed model

ObjectiveInvestigating the application of CT images when diagnosing lung cancer based on finite mixture model is the objective. Method: 120 clean healthy rats were taken as the research objects to establish lung cancer rat model and carry out lung CT image examination. After the successful CT image data preprocessing, the image is segmented by different methods, which include lung nodule segmentation on the basis of Adaptive Particle Swarm Optimization – Gaussian mixture model (APSO-GMM), lung nodule segmentation on the basis of Adaptive Particle Swarm Optimization – gamma mixture model (APSO-GaMM), lung nodule segmentation based on statistical information and self-selected mixed distribution model, and lung nodule segmentation based on neighborhood information and self-selected mixed distribution model. The segmentation effect is evaluated. Results: Compared with the results of lung nodule segmentation based on statistical information and self-selected mixed distribution model, the Dice coefficient of lung nodule segmentation based on neighborhood information and self-selected mixed distribution model is higher, the relative final measurement accuracy is smaller, the segmentation is more accurate, but the running time is longer. Compared with APSO-GMM and APSO-GaMM, the dice value of self-selected mixed distribution model segmentation method is larger, and the final measurement accuracy is smaller. Conclusion: Among the five methods, the dice value of the self-selected mixed distribution model based on neighborhood information is the largest, and the relative accuracy of the final measurement is the smallest, indicating that the segmentation effect of the self-selected mixed distribution model based on neighborhood information is the best.     

Benign-malignant pulmonary nodule classification in low-dose CT with convolutional features

PurposeLow-Dose Computed Tomography (LDCT) is the most common imaging modality for lung cancer diagnosis. The presence of nodules in the scans does not necessarily portend lung cancer, as there is an intricate relationship between nodule characteristics and lung cancer. Therefore, benign-malignant pulmonary nodule classification at early detection is a crucial step to improve diagnosis and prolong patient survival. The aim of this study is to propose a method for predicting nodule malignancy based on deep abstract features.MethodsTo efficiently capture both intra-nodule heterogeneities and contextual information of the pulmonary nodules, a dual pathway model was developed to integrate the intra-nodule characteristics with contextual attributes. The proposed approach was implemented with both supervised and unsupervised learning schemes. A random forest model was added as a second component on top of the networks to generate the classification results. The discrimination power of the model was evaluated by calculating the Area Under the Receiver Operating Characteristic Curve (AUROC) metric.ResultsExperiments on 1297 manually segmented nodules show that the integration of context and target supervised deep features have a great potential for accurate prediction, resulting in a discrimination power of 0.936 in terms of AUROC, which outperformed the classification performance of the Kaggle 2017 challenge winner.ConclusionEmpirical results demonstrate that integrating nodule target and context images into a unified network improves the discrimination power, outperforming the conventional single pathway convolutional neural networks.     

Detection of Time-Varying Structures by Large Deformation Diffeomorphic Metric Mapping to Aid Reading of High-Resolution CT Images of the Lung

Objectives

To evaluate the accuracy of advanced non-linear registration of serial lung Computed Tomography (CT) images using Large Deformation Diffeomorphic Metric Mapping (LDDMM).

Methods

Fifteen cases of lung cancer with serial lung CT images (interval: 62.2±26.9 days) were used. After affine transformation, three dimensional, non-linear volume registration was conducted using LDDMM with or without cascading elasticity control. Registration accuracy was evaluated by measuring the displacement of landmarks placed on vessel bifurcations for each lung segment. Subtraction images and Jacobian color maps, calculated from the transformation matrix derived from image warping, were generated, which were used to evaluate time-course changes of the tumors.

Results

The average displacement of landmarks was 0.02±0.16 mm and 0.12±0.60 mm for proximal and distal landmarks after LDDMM transformation with cascading elasticity control, which was significantly smaller than 3.11±2.47 mm and 3.99±3.05 mm, respectively, after affine transformation. Emerged or vanished nodules were visualized on subtraction images, and enlarging or shrinking nodules were displayed on Jacobian maps enabled by highly accurate registration of the nodules using LDDMM. However, some residual misalignments were observed, even with non-linear transformation when substantial changes existed between the image pairs.

Conclusions

LDDMM provides accurate registration of serial lung CT images, and temporal subtraction images with Jacobian maps help radiologists to find changes in pulmonary nodules.     

A New Method of Detecting Pulmonary Nodules with PET/CT Based on an Improved Watershed Algorithm

Background

Integrated ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) is widely performed for staging solitary pulmonary nodules (SPNs). However, the diagnostic efficacy of SPNs based on PET/CT is not optimal. Here, we propose a method of detection based on PET/CT that can differentiate malignant and benign SPNs with few false-positives.

Method

Our proposed method combines the features of positron-emission tomography (PET) and computed tomography (CT). A dynamic threshold segmentation method was used to identify lung parenchyma in CT images and suspicious areas in PET images. Then, an improved watershed method was used to mark suspicious areas on the CT image. Next, the support vector machine (SVM) method was used to classify SPNs based on textural features of CT images and metabolic features of PET images to validate the proposed method.

Results

Our proposed method was more efficient than traditional methods and methods based on the CT or PET features alone (sensitivity 95.6%; average of 2.9 false positives per scan).     

Non-rigid registration of serial dedicated breast CT,longitudinal dedicated breast CT and PET/CT images using the diffeomorphic demons method

Rationale and objectivesDedicated breast CT and PET/CT scanners provide detailed 3D anatomical and functional imaging data sets and are currently being investigated for applications in breast cancer management such as diagnosis, monitoring response to therapy and radiation therapy planning. Our objective was to evaluate the performance of the diffeomorphic demons (DD) non-rigid image registration method to spatially align 3D serial (pre- and post-contrast) dedicated breast computed tomography (CT), and longitudinally-acquired dedicated 3D breast CT and positron emission tomography (PET)/CT images.MethodsThe algorithmic parameters of the DD method were optimized for the alignment of dedicated breast CT images using training data and fixed. The performance of the method for image alignment was quantitatively evaluated using three separate data sets; (1) serial breast CT pre- and post-contrast images of 20 women, (2) breast CT images of 20 women acquired before and after repositioning the subject on the scanner, and (3) dedicated breast PET/CT images of 7 women undergoing neo-adjuvant chemotherapy acquired pre-treatment and after 1 cycle of therapy.ResultsThe DD registration method outperformed no registration (p < 0.001) and conventional affine registration (p ≤ 0.002) for serial and longitudinal breast CT and PET/CT image alignment. In spite of the large size of the imaging data, the computational cost of the DD method was found to be reasonable (3–5 min).ConclusionsCo-registration of dedicated breast CT and PET/CT images can be performed rapidly and reliably using the DD method. This is the first study evaluating the DD registration method for the alignment of dedicated breast CT and PET/CT images.     

Artificial intelligence in image reconstruction: The change is here

Innovations in CT have been impressive among imaging and medical technologies in both the hardware and software domain. The range and speed of CT scanning improved from the introduction of multidetector-row CT scanners with wide-array detectors and faster gantry rotation speeds. To tackle concerns over rising radiation doses from its increasing use and to improve image quality, CT reconstruction techniques evolved from filtered back projection to commercial release of iterative reconstruction techniques, and recently, of deep learning (DL)-based image reconstruction. These newer reconstruction techniques enable improved or retained image quality versus filtered back projection at lower radiation doses. DL can aid in image reconstruction with training data without total reliance on the physical model of the imaging process, unique artifacts of PCD-CT due to charge sharing, K-escape, fluorescence x-ray emission, and pulse pileups can be handled in the data-driven fashion. With sufficiently reconstructed images, a well-designed network can be trained to upgrade image quality over a practical/clinical threshold or define new/killer applications. Besides, the much smaller detector pixel for PCD-CT can lead to huge computational costs with traditional model-based iterative reconstruction methods whereas deep networks can be much faster with training and validation. In this review, we present techniques, applications, uses, and limitations of deep learning-based image reconstruction methods in CT.     

A study to determine the impact of IMPT optimization techniques on prostate synthetic CT image sets dose comparison against CT image sets

BackgroundThe objective of this study is to determine the impact of intensity modulated proton therapty (IMPT) optimization techniques on the proton dose comparison of commercially available magnetic resonance for calculating attenuation (MRCA T) images, a synthetic computed tomography CT (sCT) based on magnetic resonance imaging (MRI) scan against the CT images and find out the optimization technique which creates plans with the least dose differences against the regular CT image sets.Material and methodsRegular CT data sets and sCT image sets were obtained for 10 prostate patients for the study. Six plans were created using six distinct IMPT optimization techniques including multi-field optimization (MFO), single field uniform dose (SFUD) optimization, and robust optimization (RO) in CT image sets. These plans were copied to MRCA T, sCT datasets and doses were computed. Doses from CT and MRCA T data sets were compared for each patient using 2D dose distribution display, dose volume histograms (DVH), homogeneity index (HI), conformation number (CN) and 3D gamma analysis. A two tailed t-test was conducted on HI and CN with 5% significance level with a null hypothesis for CT and sCT image sets.ResultsAnalysis of ten CT and sCT image sets with different IMPT optimization techniques shows that a few of the techniques show significant differences between plans for a few evaluation parameters. Isodose lines, DVH, HI, CN and t-test analysis shows that robust optimizations with 2% range error incorporated results in plans, when re-computed in sCT image sets results in the least dose differences against CT plans compared to other optimization techniques. The second best optimization technique with the least dose differences was robust optimization with 5% range error.ConclusionThis study affirmatively demonstrates the impact of IMPT optimization techniques on synthetic CT image sets dose comparison against CT images and determines the robust optimization with 2% range error as the optimization technique which gives the least dose difference when compared to CT plans.     

Lung tumors on multimodal radiographs derived from grating-based X-ray imaging – A feasibility study

PurposeThe purpose of this study was to assess whether grating-based X-ray imaging may have a role in imaging of pulmonary nodules on radiographs.Materials and methodsA mouse lung containing multiple lung tumors was imaged using a small-animal scanner with a conventional X-ray source and a grating interferometer for phase-contrast imaging. We qualitatively compared the signal characteristics of lung nodules on transmission, dark-field and phase-contrast images. Furthermore, we quantitatively compared signal characteristics of lung tumors and the adjacent lung tissue and calculated the corresponding contrast-to-noise ratios.ResultsOf the 5 tumors visualized on the transmission image, 3/5 tumors were clearly visualized and 1 tumor was faintly visualized in the dark-field image as areas of decreased small angle scattering. In the phase-contrast images, 3/5 tumors were clearly visualized, while the remaining 2 tumors were faintly visualized by the phase-shift occurring at their edges. No additional tumors were visualized in either the dark-field or phase-contrast images. Compared to the adjacent lung tissue, lung tumors were characterized by a significant decrease in transmission signal (median 0.86 vs. 0.91, p = 0.04) and increase in dark-field signal (median 0.71 vs. 0.65, p = 0.04). Median contrast-to-noise ratios for the visualization of lung nodules were 4.4 for transmission images and 1.7 for dark-field images (p = 0.04).ConclusionLung nodules can be visualized on all three radiograph modalities derived from grating-based X-ray imaging. However, our initial data suggest that grating-based multimodal X-ray imaging does not increase the sensitivity of chest radiographs for the detection of lung nodules.     

Preoperative Prediction of Ki-67 Labeling Index By Three-dimensional CT Image Parameters for Differential Diagnosis Of Ground-Glass Opacity (GGO)

The aim of this study was to predict Ki-67 labeling index (LI) preoperatively by three-dimensional (3D) CT image parameters for pathologic assessment of GGO nodules. Diameter, total volume (TV), the maximum CT number (MAX), average CT number (AVG) and standard deviation of CT number within the whole GGO nodule (STD) were measured by 3D CT workstation. By detection of immunohistochemistry and Image Software Pro Plus 6.0, different Ki-67 LI were measured and statistically analyzed among preinvasive adenocarcinoma (PIA), minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC). Receiver operating characteristic (ROC) curve, Spearman correlation analysis and multiple linear regression analysis with cross-validation were performed to further research a quantitative correlation between Ki-67 labeling index and radiological parameters. Diameter, TV, MAX, AVG and STD increased along with PIA, MIA and IAC significantly and consecutively. In the multiple linear regression model by a stepwise way, we obtained an equation: prediction of Ki-67 LI=0.022*STD+0.001* TV+2.137 (R=0.595, R’s square=0.354, p<0.001), which can predict Ki-67 LI as a proliferative marker preoperatively. Diameter, TV, MAX, AVG and STD could discriminate pathologic categories of GGO nodules significantly. Ki-67 LI of early lung adenocarcinoma presenting GGO can be predicted by radiologic parameters based on 3D CT for differential diagnosis.     

Computer-aided detection of pulmonary nodules using dynamic self-adaptive template matching and a FLDA classifier

Improving the performance of computer-aided detection (CAD) system for pulmonary nodules is still an important issue for its future clinical applications. This study aims to develop a new CAD scheme for pulmonary nodule detection based on dynamic self-adaptive template matching and Fisher linear discriminant analysis (FLDA) classifier. We first segment and repair lung volume by using OTSU algorithm and three-dimensional (3D) region growing. Next, the suspicious regions of interest (ROIs) are extracted and filtered by applying 3D dot filtering and thresholding method. Then, pulmonary nodule candidates are roughly detected with 3D dynamic self-adaptive template matching. Finally, we optimally select 11 image features and apply FLDA classifier to reduce false positive detections. The performance of the new method is validated by comparing with other methods through experiments using two groups of public datasets from Lung Image Database Consortium (LIDC) and ANODE09. By a 10-fold cross-validation experiment, the new CAD scheme finally has achieved a sensitivity of 90.24% and a false-positive (FP) of 4.54 FP/scan on average for the former dataset, and a sensitivity of 84.1% with 5.59 FP/scan for the latter. By comparing with other previously reported CAD schemes tested on the same datasets, the study proves that this new scheme can yield higher and more robust results in detecting pulmonary nodules.     

探讨CT技术在肺结节疾病诊断中的应用

目的：探讨CT技术在肺结节疾病诊断中的应用。利用CT技术提供的影像学特征,及时准确的诊断肺结节疾病,从而更具体更全面的提高对该疾病的认识,减少误诊的发生。方法：回顾性分析我院2010年-2012年确诊的符合相应临床诊治标准的肺结节患者48例的临床资料,通过CT扫描分析其具体的特征表现。结果：在所选的48例中,纵隔淋巴结增大45例,双侧肺门淋巴结增大44例,胸部淋巴结增大患者48例。肺部病变者33例,其中单发结节患者2例,多发结节患者25例,支气管血管束增粗者14例,磨玻璃样影案例者9例,实变案例5例。胸膜病发患者13例。结论：我们通过分析发现,胸部有典型影像学表现形式的肺结节病例诊断较容易,无典型影像学表现的患者诊断较为困难。因此我们认为利用CT技术诊断肺结节疾病具有特异性意义,值得临床医生重视。     

实验猕猴肺部CT影像断层初步观察

目的应用CT技术对成年实验猕猴胸部肺窗进行断层扫描观察,探讨CT技术对猕猴肺部疾病的临床诊断意义,建立正常猴肺部CT断层扫描图谱,为CT技术在猕猴解剖学的研究、疾病的临床诊断及科学实验方面的应用,提供影像学的基础资料。方法经过触诊、叩诊、听诊、体温、呼吸率、心率、呼吸运动、血液常规等检查,选择健康猴10只,雌雄各半,年龄分别为5～10岁,进行肺部CT断层扫描检测。试验猴全身麻醉后,置于CT诊断床上,取头前尾后仰卧位进行肺部扫描,获取肺窗扫描图像。对具有解剖意义的扫描图像的每个层面的主要结构（肺叶、气管、动脉血管、静脉血管等）进行标注。结果 （1）获得具有解剖意义的肺窗扫描图像13张。（2）在断层扫描的图像中,肺、气管、较大血管等组织器官界面清晰。肺为左右两侧,左肺分为上叶、中叶、下叶,右肺分为上叶、中叶、下叶、奇叶四部分。不同的断层面分别可见肺部左主支气管、右主支气管、支气管、血管等组织。（3）肺部较小或细小的血管、神经组织界面不清晰。结论 （1）应用CT获得的正常猕猴胸部肺窗断层扫描图像表明,正常健康猴双肺纹理清晰,走行自然,肺野透光度良好,双肺无异常实质病变影像。（2）获得了健康猕猴肺部的CT影像学资料,为猕猴肺部疾病的诊断,提供了一种安全、方便又准确的新依据,建立了成年健康猕猴肺部CT断层解剖研究的背景资料。     

慢性阻塞性肺疾病合并肺曲菌病一例及文献复习

目的:提高对慢性阻塞性肺疾病合并侵袭性肺曲菌病(COPD合并IPA)临床特点、诊断及治疗的认识.方法:回顾性分析2011年4月收治的一例COPD合并IPA患者的临床资料及诊治经过,并复习相关文献.结果:男患,“咳嗽、咳痰30余年,气短3年,加重1月余”入院,肺部CT示双肺多发结节影、空洞影,经抗炎、抗念珠菌治疗无效,CT下肺结节病灶活检病理示肺曲菌.抗曲菌治疗后症状好转、肺部影像明显吸收.结论:COPD合并IPA正逐渐引起重视,临床特征无明显特异性,肺部影像以结节影、空洞影多见,早期常规治疗无效时应积极抗曲菌治疗,可明显改善症状,降低死亡率,病理活检是确诊的依据.