X线体层机对气管、支气管疾病进行体层检查的诊断价值

气管、支气管体层检查对气管、支气管、肺门与纵隔等疾病有较高的诊断与鉴别诊断价值,已成为胸部X线检查的重要方法之一,尤其对气管、支气管疾病的定位、定性诊断更有重要价值。我院近5年来作支气管体层检查800多例,从中选择临床资料齐全、结合纤支镜检或/和手术病理证实的360例气管、支气管系列疾病进行分析报道如下。

X射线成像技术在口腔临床中的应用

X射线成像技术在口腔临床中有非常重要的作用。随着X射线成像技术的发展,也逐渐从模拟技术到如今的数字化成像,这些成像技术包括口内牙科X线机、口腔曲面体层X线机、口腔颌面锥形束计算机体层摄影设备等设备日益广泛应用于口腔临床。本文综述了几种X线设备的技术原理和临床应用情况,为相关技术人员及医学研究人员提供参考。

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.

Coincidence of hepatocelluar carcinoma and hepatic angiomyolipomas in tuberous sclerosis complex: A case report

Tuberous sclerosis complex (TSC) is a dominantly inherited disorder which characterized by the growth of harmatomatous in multiple organs. Unlike the common development of renal angiomyolipoma, hepatic angiomyolipoma rarely occur in patients with TSC. We report here a patient with hepatic angiomyolipomas and concurrent hepatocellular carcinoma in TSC. This represents the first reported case in English literature. In this patient, multiple hepatic angiomyolipomas were diagnosed with recognition of their fat components and typical clinical settings. Hepatocellular carcinoma in the left liver lobe was definitely diagnosed by US guided biopsy. In such clinical settings, fat containing lesions in liver can be reasonably treated as angiomyolipomas, but non fat containing lesions must be differentiated from hepatocellular carcinoma, imaging guided biopsy can be adopted to confirm the diagnosis.

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.

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.

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.

胃窦异位胰腺伴粘液囊肿一例

病例资料患者,男,26岁,无明显诱因出现上腹部不适,进食后加重,伴反酸、烧心、食欲减退。查体未及阳性体征。全腹增强CT示:胃窦前方见一囊性肿块(图1),边缘清楚,囊液密度均匀,囊壁局部增厚,最厚处约0.6 cm。病变与胃窦前壁关系密切,胃窦及十二指肠球部呈受压样改变。

运动相关下肢应力性骨折的临床影像学分析

应力性骨折又称疲劳骨折,患者多无急性外伤史,为持续的积累性损伤,多为不完全性骨折。笔者收集了38例经临床随访证实的与运动相关下肢应力性骨折患者的临床及影像学资料,并结合国内外文献,对该类患者的临床影像学特征进行了分析,现总结报告如下。1临床资料本组38例,男20例,女18例。年龄6～86岁,中位数19岁。全部病例均因下肢疼痛就诊,无明确外伤史,但均有长期运动史或短期内大量运动史。

主动脉夹层影像学对照研究16例

目的探讨64层螺旋CT血管成像(64SCTA)、血管造影(DSA)及彩超三种检查方法对主动脉夹层的诊断价值。方法对16例有完整的64SCTA、彩超和数字减影血管造影(DSA)资料的DebakeyⅢ主动脉夹层患者进行回顾性分析总结,对比分析3种方法对主动脉夹层的诊断价值。结果64SCTA显示破口共计54个,撕裂的内膜片26个;DSA则分别显示为40个和18个;彩超共提示20个破口,撕裂的内膜片共计18个。结论64SCTA对主动脉夹层破口和撕裂的内膜片检出的敏感性高于DSA和彩超。64层螺旋CT血管成像对主动脉夹层的诊断优于DSA和彩超。

寰枢椎椎弓根影像学定位的临床应用研究

目的：利用影像学资料测量寰枢椎椎弓根的各种数据．为临床应用提供客观依据，提高寰枢椎椎弓根内固定手术置钉的1次成功率。方法：2001．10～2007．9月．对200份寰枢椎CR、DRX线片、MRI及64排CT片，通过图像储存传输系统（简称Pacs）分析，测量寰枢椎椎弓根安全进钉点及安全进钉角度。利用自制的寰枢椎定位导向器，依据所测的安全进钉点及安全进钉角度对48例患者应用192枚寰枢椎椎弓根行经椎弓根内固定手术。结果：通过对这200份影像学资料分析测量后认为寰椎椎弓根安全进钉点：左侧（19．93±1．32）mm．右侧（19．16±1．30）mm；寰椎椎弓根向内侧安全进钉角度：左侧（23．72±2．09）°，右侧（23．35±1．91）°；寰椎向头侧安全进钉角度（9．00±1．20）°。枢椎椎弓根安全进钉点：左侧（13．14±0．82）mm，右侧（13．85±0．79）mm；枢椎椎弓根向内侧安全进钉角度：左（24．52±1．26）°，右侧（20．42±1．42）°；枢椎向头侧安全进钉角度（25．00±3．00）°。48例病人平均随访10．6个月，按JOA评分标准，优31例，良14例，可2例．差1例，优良率93．75％。结论：利用对CR、DRX线片、MRI及64排CT片等影像学资料，测量寰枢椎椎弓根各种数据，设计简便、程序简化、个性化强，对寰枢椎椎弓根内固定手术的实际操作有良好的指导意义。

分析不同参数对胸部增强CT扫描辐射剂量的影响

目的:分析胸部增强X射线计算机断层成像(CT)检查的辐射剂量数据,探讨胸部增强CT扫描的诊断剂量及其影响因素。方法:回顾性分析对比在4台CT设备上扫描的297例患者扫描数据,按设备型号分为设备A组、设备B组、设备C组和设备D组,比较CT设备间扫描序列的差别,统计分析CT容积剂量指数(CTDI_(vol))、剂量长度乘积(DLP)和有效剂量(ED)。结果:4组设备对所有病例扫描的CTDIvol值分别为(12.18±4.13)mGy、(8.86±3.18)mGy、(10.70±5.18)mGy和13.59 mGy,之间比较差异有统计学意义(F=38.58,P<0.05);4组病例的DLP值分别为(433.54±145.18)mGy·cm、(311.62±112.75)mGy·cm、(368.04±181.22)mGy·cm和(475.75±34.25)mGy·cm,之间比较差异有统计学意义(F=36.33,P<0.05);4组病例的ED值分别为(6.07±2.03)、(4.36±1.58)、(5.15±2.54)和(6.66±0.48)m Sv,之间比较差异有统计学意义(F=36.32,P<0.05)。辐射剂量两两比较均有差异。结论:胸部增强CT扫描患者的受照剂量与CT机型号及采集参数相关,自动管电流模式下患者的有效剂量低于固定管电流;角度射线管电流调制技术会有效降低患者有效剂量,临床中可以通过优化采集条件降低患者的受照剂量。

原发性上纵隔占位性病变的CT分析与鉴别诊断

随着现代医学影像检查手段的发展,CT检查对纵隔肿瘤的发现越来越容易.一般来说,对原发性纵隔肿瘤的定位比定性重要,而CT对肿瘤的定位有明显的优势.笔者就近年来搜集的上纵隔占位性病变的CT表现分析及鉴别诊断报道如下.

螺旋CT在肾上腺钝性损伤中的诊断价值

目的:讨论螺旋CT在肾上腺钝性损伤中的诊断价值。方法:回顾性分析诊断明确的11例肾上腺钝性损伤患者的CT表现。结果:右侧肾上腺损伤9例(81.82%),左侧2例(18.18%)。患者中:形成血肿7例(63.63%),血肿表现为圆形或卵圆形肿块影,有2例为迟发性血肿;肾上腺挫伤4例(36.37%),表现为肾上腺局限性或弥散性增粗、肿胀,CT复查表现为肾上腺血肿体积缩小,密度从边缘开始逐渐降低,肾上腺形态缓慢恢复。7例中5例完全吸收,1例形成机化结节,1例形成假性囊肿。4例肾上腺挫伤者经随访复查,其肾上腺形态逐步恢复。结论:肾上腺钝性损伤具有特征性CT表现,螺旋CT对肾上腺钝性损伤具有重要诊断价值,可作为首选方法。