Chest CT quantitative parameters in patients with acute exacerbation of chronic obstructive pulmonary disease:Correlations with blood eosinophil level

Objective To observe the correlations of chest CT quantitative parameters in patients with acute exacerbation of chronic obstructive pulmonary disease(AECOPD)with blood eosinophil(EOS)level.Methods Chest CT data of 162 AECOPD patients with elevated eosinophils were retrospectively analyzed.The patients were divided into low EOS group(n=105)and high EOS group(n=57)according to the absolute counting of blood EOS.The quantitative CT parameters,including the number of whole lung bronchi and the volume of blood vessels,low-attenuation area percentage(LAA%)of whole lung,of left/right lung and each lobe of lung,as well as the luminal diameter(LD),wall thickness(WT),wall area(WA)and WA percentage of total bronchial cross-section(WA%)of grade 3 to 8 bronchi were compared between groups.Spearman correlations were performed to analyze the correlations of quantitative CT parameters with blood EOS level.Results LAA%of the whole lung,of the left/right lung and each lobe of lung,as well as of the upper lobe of right lung LD grade 4,middle lobe of right lung WT grade 5,upper lobe of right lung WA grade 4,middle lobe of right lung WA grade 5 and lower lobe of left lung WA grade 3 in low EOS group were all higher than those in high EOS group(all P<0.05).Except for the upper lobe of right lung LD grade 4,the above quantitative CT indexes being significant different between groups were all weakly and negatively correlated with blood EOS level(r=-0.335 to-0.164,all P<0.05).Conclusion Chest CT quantitative parameters of AECOPD patients were correlated with blood EOS level,among which LAA%,a part of WT and WA were all weakly negatively correlated with blood EOS level.

Research progresses of dual-layer spectral detector CT for preoperative evaluation on colorectal cancer

Dual-layer spectral detector CT is a new spectrum CT imaging technology based on detector being able to obtain both images similar to true plain and spectral images in one time scanning.The reconstructed multi-parameter spectral images can not only improve image quality,enhance tissue contrast,increase the visualization and detection ability of occult lesions,but also provide qualitative and quantitative analysis of the lesions,so as to provide more imaging information and multi-dimensional diagnostic basis.The research progresses of dual-layer spectral detector CT for preoperative evaluation on colorectal cancer were reviewed in this article.

Preoperative CT radiomics models for predicting composition of in vivo urinary calculi

Objective To observe the value of preoperative CT radiomics models for predicting composition of in vivo urinary calculi.Methods Totally 543 urolithiasis patients were retrospectively enrolled and divided into calcium oxalate monohydrate stone group(group A,n=373),anhydrous uric acid stone group(group B,n=86),carbonate apatite group(group C,n=30),ammonium urate stone group(group D,n=28)and ammonium magnesium phosphate hexahydrate stone group(group E,n=26)according to the composition of calculi,also divided into training set and test set at the ratio of 7∶3.Radiomics features were extracted and screened based on plain CT images of urinary system.Five binary task models(model A—E corresponding to group A—E)and a quinary task model were constructed using least absolute shrinkage and selection operator algorithm for predicting the composition of calculi in vivo.Then receiver operating characteristic curves were drawn,and the area under the curves(AUC)were calculated to evaluate the predictive efficacy of binary task models,while the accuracy,precision,recall and F1 score were used to evaluate the predictive efficacy of the quinary task model.Results All binary task models had good efficacy for predicting the composition of urinary calculi in vivo,with AUC of 0.860—0.948 in training set and of 0.856—0.933 in test set.The accuracy,precision,recall and F1 score of the quinary task model for predicting the composition of in vivo urinary calculi was 82.25%,83.79%,46.23%and 0.596 in training set,respectively,while was 80.63%,75.26%,43.48%and 0.551 in test set,respectively.Conclusion Binary task radiomics models based on preoperative plain CT had good efficacy for predicting the composition of in vivo urinary calculi,while the quinary task radiomics model had high accuracy but relatively poor stability.

Artificial intelligence models based on non-contrast chest CT for measuring bone mineral density

Objective To observe the value of artificial intelligence(AI)models based on non-contrast chest CT for measuring bone mineral density(BMD).Methods Totally 380 subjects who underwent both non-contrast chest CT and quantitative CT(QCT)BMD examination were retrospectively enrolled and divided into training set(n=304)and test set(n=76)at a ratio of 8∶2.The mean BMD of L1—L3 vertebrae were measured based on QCT.Spongy bones of T5—T10 vertebrae were segmented as ROI,radiomics(Rad)features were extracted,and machine learning(ML),Rad and deep learning(DL)models were constructed for classification of osteoporosis(OP)and evaluating BMD,respectively.Receiver operating characteristic curves were drawn,and area under the curves(AUC)were calculated to evaluate the efficacy of each model for classification of OP.Bland-Altman analysis and Pearson correlation analysis were performed to explore the consistency and correlation of each model with QCT for measuring BMD.Results Among ML and Rad models,ML Bagging-OP and Rad Bagging-OP had the best performances for classification of OP.In test set,AUC of ML Bagging-OP,Rad Bagging-OP and DL OP for classification of OP was 0.943,0.944 and 0.947,respectively,with no significant difference(all P>0.05).BMD obtained with all the above models had good consistency with those measured with QCT(most of the differences were within the range of Ax-G±1.96 s),which were highly positively correlated(r=0.910—0.974,all P<0.001).Conclusion AI models based on non-contrast chest CT had high efficacy for classification of OP,and good consistency of BMD measurements were found between AI models and QCT.

Self-supervised learning artificial intelligence noise reduction technology based on the nearest adjacent layer in ultra-low dose CT of urinary calculi

Objective To observe the value of self-supervised deep learning artificial intelligence(AI)noise reduction technology based on the nearest adjacent layer applicated in ultra-low dose CT(ULDCT)for urinary calculi.Methods Eighty-eight urinary calculi patients were prospectively enrolled.Low dose CT(LDCT)and ULDCT scanning were performed,and the effective dose(ED)of each scanning protocol were calculated.The patients were then randomly divided into training set(n=75)and test set(n=13),and a self-supervised deep learning AI noise reduction system based on the nearest adjacent layer constructed with ULDCT images in training set was used for reducing noise of ULDCT images in test set.In test set,the quality of ULDCT images before and after AI noise reduction were compared with LDCT images,i.e.Blind/Referenceless Image Spatial Quality Evaluator(BRISQUE)scores,image noise(SD ROI)and signal-to-noise ratio(SNR).Results The tube current,the volume CT dose index and the dose length product of abdominal ULDCT scanning protocol were all lower compared with those of LDCT scanning protocol(all P<0.05),with a decrease of ED for approximately 82.66%.For 13 patients with urinary calculi in test set,BRISQUE score showed that the quality level of ULDCT images before AI noise reduction reached 54.42%level but raised to 95.76%level of LDCT images after AI noise reduction.Both ULDCT images after AI noise reduction and LDCT images had lower SD ROI and higher SNR than ULDCT images before AI noise reduction(all adjusted P<0.05),whereas no significant difference was found between the former two(both adjusted P>0.05).Conclusion Self-supervised learning AI noise reduction technology based on the nearest adjacent layer could effectively reduce noise and improve image quality of urinary calculi ULDCT images,being conducive for clinical application of ULDCT.

Automated body composition analysis system based on chest CT for evaluating content of muscle and adipose

Objective To establish a body composition analysis system based on chest CT,and to observe its value for evaluating content of chest muscle and adipose.Methods T7—T8 layer CT images of 108 pneumonia patients were collected(segmented dataset),and chest CT data of 984 patients were screened from the COVID 19-CT dataset(10 cases were randomly selected as whole test dataset,the remaining 974 cases were selected as layer selection dataset).T7—T8 layer was classified based on convolutional neural network(CNN)derived networks,including ResNet,ResNeXt,MobileNet,ShuffleNet,DenseNet,EfficientNet and ConvNeXt,then the accuracy,precision,recall and specificity were used to evaluate the performance of layer selection dataset.The skeletal muscle(SM),subcutaneous adipose tissue(SAT),intermuscular adipose tissue(IMAT)and visceral adipose tissue(VAT)were segmented using classical fully CNN(FCN)derived network,including FCN,SegNet,UNet,Attention UNet,UNET++,nnUNet,UNeXt and CMUNeXt,then Dice similarity coefficient(DSC),intersection over union(IoU)and 95 Hausdorff distance(HD)were used to evaluate the performance of segmented dataset.The automatic body composition analysis system was constructed based on optimal layer selection network and segmentation network,the mean absolute error(MAE),root mean squared error(RMSE)and standard deviation(SD)of MAE were used to evaluate the performance of automatic system for testing the whole test dataset.Results The accuracy,precision,recall and specificity of DenseNet network for automatically classifying T7—T8 layer from chest CT images was 95.06%,84.83%,92.27%and 95.78%,respectively,which were all higher than those of the other layer selection networks.In segmentation of SM,SAT,IMAT and overall,DSC and IoU of UNet++network were all higher,while 95HD of UNet++network were all lower than those of the other segmentation networks.Using DenseNet as the layer selection network and UNet++as the segmentation network,MAE of the automatic body composition analysis system for predicting SM,SAT,IMAT,VAT and MAE was 27.09,6.95,6.65 and 3.35 cm 2,respectively.Conclusion The body composition analysis system based on chest CT could be used to assess content of chest muscle and adipose.Among them,the UNet++network had better segmentation performance in adipose tissue than SM.

0D-1D coupling model and 3D fluid-structure interaction model based on coronary CT angiography for displaying hemodynamic characteristics of coronary artery stenosis

Objective To observe value of 0D-1D coupling model and 3D fluid-structure interaction(FSI)model based on coronary CT angiography(CCTA)for displaying hemodynamic characteristics of coronary artery stenosis.Methods Based on CCTA data of the stenosed left anterior descending branch(LAD)in a patient with coronary heart disease,an 0D-1D coupling model and 3D FSI model were built,respectively.Then hemodynamic characteristic indexes,including the pressure,flow velocity and wall shear stress(WSS)were obtained in every 0.01 s during 1 s at 5 sampling points(i.e.sampling point 1—5)using these 2 models,respectively,and the consistencies of the results between models were evaluated with Spearman correlation coefficient r s.Results The time consuming for construction of 0D-1D coupling model and 3D FSI model was 0.033 min and 704 min,respectively.Both models showed basically distribution of the pressure,flow velocity and WSS of the stenosed LAD.For more details,the pressure at the stenosed segment of LAD and the proximal segment of stenosis were both higher,which gradually decreased at the distal segment of stenosis,and the flow velocity at the proximal segment of stenosis was in a relatively slow and uniform condition,with significantly increased flow velocity and WSS at the stenosed segment.Compared with 3D FSI model,0D-1D vascular coupling model was relatively unrefined and lack of distal flow lines when displaying blood flow velocity.For sampling point 2 at the stenosed segment of LAD,no significant consistency for pressure between 2 models was found(P=0.118),but strong consistency for the flow velocity and WSS(r s=0.730,0.807,both P<0.05).The consistencies of pressure,flow velocity and WSS between 2 models at the proximal and distal segment of stenosis,i.e.1,3—5 sampling points were week to moderate(r s=0.237—0.669,all P<0.05).Conclusion 0D-1D coupling model exhibited outstanding computational efficiency and might provide relatively reasonable results,while 3D FSI model showed higher accuracy for details and streamline when simulating LAD stenosis.

骨外尤文肉瘤/外周原始神经外胚层肿瘤的CT及MRI表现

目的:探讨骨外尤文肉瘤/外周原始神经外胚层肿瘤的CT及MRI表现及临床特点,旨在提高对该病的认识。方法:回顾性分析12例经病理证实的骨外尤文肉瘤/外周原始神经外胚层肿瘤的CT及MRI资料并复习相关文献,总结其影像学特征。结果:病灶位于鼻口咽3例,舌1例,颌下1例,鼻腔鼻窦3例,颈根部1例,胸腔2例,空肠1例。4例同时行CT及MRI平扫+增强检查,5例行CT平扫+增强检查,3例行MRI平扫+增强检查。病变为浸润性生长的软组织肿块,体积较大,密度或信号不均,增强后强化程度不一,3例DWI高b值扩散受限呈混杂高/稍高信号,对应ADC图为低信号。12例均伴坏死,2例伴钙化,12例均伴邻近结构受压移位,9例伴周围组织受侵,5例伴骨质破坏,5例病灶内部或边缘见血管影,2例MRI检查见流空血管影,3例伴淋巴结转移。结论:骨外尤文肉瘤/外周原始神经外胚层肿瘤的CT及MRI表现具有一定的特征:病灶多表现为体积较大的软组织肿块,占位效应明显,囊变坏死多见,钙化少见,易侵犯周围组织,病灶周围或内部常可见流空血管影。

前列腺骨外尤文肉瘤/原始神经外胚层肿瘤一例

病例资料 患者,男,45岁,因排便困难伴血便入院。外院电子结肠镜提示前列腺-直肠瘘形成。肛门指诊:前列腺Ⅲ度肿大,指套退出后可见脓性及血性分泌物。实验室检查:前列腺特异性抗原(prostate specific antigen, PSA)浓度为2.386mg/mL。影像学检查:CT平扫示前列腺形态异常,可见混杂密度肿块,大小约11.6 cm×9.1 cm×10.4 cm, 病变周围可见积气,提示肠瘘形成可能(图1)。

MSCT联合CEA、CYFRA21-1、NSE在肺癌中诊断价值

搜集经病理学证实的50例肺癌及50例肺内良性病灶,所有患者均行胸部MSCT扫描并检测其血清CEA、CYFRA21-1、NSE水平。比较肺内良恶性病变血清肿瘤标记物水平。结果肺癌组血清肿瘤标记物CEA、CYFRA21-1、NSE高于良性病变组(P<0.05);CEA、CYFRA21-1、NSE和CT诊断肺癌敏感度分别为50.0%、42.0%、54.0%和74.0%,特意度分别为82.0%、86.0%、88.0%和84.0%。CT联合三项肿瘤标记物诊断肺癌敏感度88.0%,特异度80.0%。CT联合血清肿瘤标志物检测可提高肺癌诊断敏感性,而特异度稍降低。

多层螺旋CT血管成像和彩色多普勒超声诊断下肢动脉硬化闭塞症的对比研究

目的对比多层螺旋CT血管成像(MSCTA)和彩色多普勒超声在下肢动脉硬化闭塞症诊断的价值。方法收集经彩色多普勒超声、MSCTA、DSA诊断下肢动脉硬化闭塞症的影像资料,将前两者检查结果与DSA结果进行对比分析。结果彩色多普勒超声显示下肢动脉的解剖形态和血流动力学,诊断下肢动脉硬化闭塞症敏感度、特异度、诊断符合率分别为88.04%、90.69%、88.77%;MSCTA显示下肢动脉三维结构,同时显示侧支动脉和闭塞远端的血管,诊断下肢动脉硬化闭塞症敏感度、特异度、诊断符合率分别为97.96%、96.90%、97.66%。结论 MSCTA可成为诊断动脉硬化闭塞症较理想的无创性影像学方法。

64排螺旋CT冠状动脉前瞻性心电门控成像:100ms与200ms曝光时间窗对图像质量及辐射剂量的影响

目的:比较64排螺旋CT冠状动脉造影(CTCA)100ms与200ms曝光时间窗对图像质量及有效剂量的影响。方法:连续选取90例疑似冠状动脉疾病患者,并随机分为两组:研究组45例,行前瞻性心电门控扫描,曝光时间窗100ms,选择自动毫安技术;对照组45例,行前瞻性心电门控扫描,曝光时间窗200ms,根据患者体重选择管电流(400mA/500mA)。分别计算两组的平均有效剂量(ED),并进行统计学分析。同时对两组患者冠状动脉的图像质量进行评估。结果:两组平均ED分别为(3.72±1.010)msv和(5.197±1.104)msv,差异具有统计学意义(t=6.285,P<0.05),两组图像质量的差异无统计学意义(t=1.356,P>0.05)。结论:当心率稳定且<65BPM时,64排螺旋CT冠状动脉前瞻性心电门控成像:100ms曝光时间窗可进一步减少辐射剂量,同时图像质量基本不受影响。

急性胰腺炎严重程度与CT表现的相关性

目的：评价螺旋CT对急性胰腺炎严重程度的评估价值。方法：对45倒经临床证实的急性胰腺炎患者的CT表现进行评分，井分析其与临床严重程度的相关性。结果：O～3分22例，经抗炎支持等治疗后很快好转，治愈出院；4—6分12例，病情较重，病程较长，治疗过程中病情有反复，3例经外科手术治疗；7—10分11例，病情危重，有1例死亡。结论：螺旋CT能对急性胰腺炎的严重程度作出准确评价，CT表现评分与临床严重程度具有较好相关性。

外生性肝癌误诊为胃肠道间质瘤1例

本文报告经手术病理证实的外生性肝癌误诊为胃肠道间质瘤1例。报告如下。 患者女．22岁。体检发现腹腔内占位6天。不伴有腹痛、腹胀．无恶心，呕吐发热、咳嗽等不适。15＋年前患者乙肝小三阳病史。实验室检查：肝功异常，乙肝标志物（＋）。甲胎蛋白AFP阳性．AFP〉576．40ng／ml。HBV（＋）．AST72．6．ALT69．4。

胃肠道恶性淋巴瘤的CT/MRI诊断

目的提高胃肠道恶性淋巴瘤的CT/MRI诊断水平。方法回顾性分析经手术病理证实的16例胃肠道恶性淋巴瘤的CT/MRI平扫+增强表现。结果 16例经手术病理证实的淋巴瘤均为非霍杰金淋巴瘤,12例为B细胞来源,3例为小淋巴细胞型,1例为T细胞型。10例为胃壁肿瘤,5例为小肠肿瘤,1例为结肠肿瘤。13例表现为弥漫增厚型,3例表现为溃疡型。增强扫描后15例明显均匀强化,1例不均匀强化。结论 CT/MRI对胃肠道恶性淋巴瘤具有较高诊断价值。

多层螺旋CT血管成像在脑动脉瘤夹闭术后评价中的应用

目的评估16层螺旋CT血管成像(CTA)在脑动脉瘤夹闭术后动脉瘤残留和载瘤动脉开放评价中的准确性和临床应用价值。方法44例患者的47个夹闭动脉瘤纳入本研究。患者均行16层螺旋CTA和数字减影血管造影(DSA)检查,DSA作为参考标准。两名神经放射科医师独立评价CTA和DSA的图像质量、动脉瘤残留和载瘤动脉的开放。结果16层螺旋CTA检出3个动脉瘤有残留,靠近钛夹的全部载瘤动脉可以被准确评价。16层螺旋CTA检出动脉瘤残留的敏感性、特异性和准确性分别是100%(97.5%CI:29.2～100%)、100%(97.5%CI:92.0～100%)和100%(97.5%CI:92.5～100%)。阳性和阴性预测率均为100%。结论16层螺旋CTA在诊断脑动脉瘤夹闭术后的残留和载瘤动脉开放方面具有很高的准确性,可以作为动脉瘤夹闭术后随访的首选影像检查。

Magnetic resonance imaging in distinguishing malignant from benign pleural disease

Objective To explore the role of magnetic resonance imaging (MRI) in distinguishing malignant from benign pleural disease.Methods All 64 patients were examined with both computed tomography (CT) and MRI. The morphologic features of pleural lesions and MR signal intensity on T1-weighted, T2-weighted and contrast-enhanced T1-weighted images were evaluated.Results Mediastinal pleural involvement, circumferential pleural thickening, nodularity, irregularity of pleural contour, and infiltration of the chest wall and/or diaphragm were most suggestive of a malignant cause on CT and MR images. Contrary to what has been reported in the literature, pleural thickness greater than 1?cm either on CT or on MRI did not reveal a significant difference between malignant and benign pleural disease (P>0.05, chi-square test). Using morphologic features in combination with signal intensity features, MRI had a sensitivity of 98% and a specificity of 92% in the detection of pleural malignancy. Conclusions Compared with those on CT, the morphologic features on MRI allowed a mostly equal and in some cases superior detection and evaluation of the spread of pleural disease. In combination with signal intensity and morphologic features, MRI is very useful in distinguishing malignant from benign pleural disease.

Computerized tomography pleurography: a new method for detecting minor pleural lesion

Objectives To evaluate the ability of CT pleurography (CTP) in detecting minor pleural lesions in patients with pleural effusion and to assess its value in distinguishing malignant from benign pleural lesions. Methods A prospective study of 50 patients with pleural effusion was conducted using conventional CT and CTP. CT scan was run after injecting an appropriate amount of contrast medium into the pleural cavity. Results In 24 patients, all lesions detected by conventional CT were demonstrated by CTP. In 13 of 24 patients, CT pleurography detected additional lesions. In 20 of 26 patients with negative findings on conventional CT, CTP was capable of demonstrating the presence of pleural lesions. The sensitivity, specificity and accuracy of detecting pleural lesions were 25%, 100% and 30% for conventional CT, 86%, 100% and 87% for CTP, respectively. Conclusion CTP is superior to conventional CT in detecting and for the differential diagnosis benign and malignant pleural lesions.