Objective: To investigate the detection and diagnostic efficacy of chest radiographs for ≤30 mm pulmonary nodules and the factors affecting them, and to compare the level of consistency among readers. Methods: A total of 43 patients with asymptomatic pulmonary nodules who consulted in Cancer Hospital, Chinese Academy of Medical Sciences from 2012 to 2014 and had chest CT and X-ray chest radiographs during the same period were retrospectively selected, and one nodule ≤30 mm was visible on chest CT images in the whole group (total 43 nodules in the whole group). One senior radiologist with more than 20 years of experience in imaging diagnosis reviewed CT images and recording the size, morphology, location, and density of nodules was selected retrospectively. Six radiologists with different levels of experience (2 residents, 2 attending physicians and 2 associate chief physicians independently reviewed the chest images and recorded the time of review, nodule detection, and diagnostic opinion. The CT imaging characteristics of detected and undetected nodules on X images were compared, and the factors affecting the detection of nodules on X-ray images were analyzed. Detection sensitivity and diagnosis accuracy rate of 6 radiologists were calculated, and the level of consistency among them was compared to analyze the influence of radiologists' seniority and reading time on the diagnosis results. Results: The number of nodules detected by all 6 radiologists was 17, with a sensitivity of detection of 39.5%(17/43). The number of nodules detected by ≥5, ≥4, ≥3, ≥2, and ≥1 physicians was 20, 21, 23, 25, and 28 nodules, respectively, with detection sensitivities of 46.5%, 48.8%, 53.5%, 58.1%, and 65.1%, respectively. Reasons for false-negative result of detection on X-ray images included the size, location, density, and morphology of the nodule. The sensitivity of detecting ≤30 mm, ≤20 mm, ≤15 mm, and ≤10 mm nodules was 46.5%-58.1%, 45.9%-54.1%, 36.0%-44.0%, and 36.4% for the 6 radiologists, respectively; the diagnosis accuracy rate was 19.0%-85.0%, 16.7%-6.5%, 18.2%-80.0%, and 0%-75.0%, respectively. The consistency of nodule detection among 6 doctors was good (Kappa value: 0.629-0.907) and the consistency of diagnostic results among them was moderate or poor (Kappa value: 0.350-0.653). The higher the radiologist's seniority, the shorter the time required to read the images. The reading time and the seniority of the radiologists had no significant influence on the detection and diagnosis results (P>0.05). Conclusions: The ability of radiographs to detect lung nodules ≤30 mm is limited, and the ability to determine the nature of the nodules is not sufficient, and the increase in reading time and seniority of the radiologists will not improve the diagnostic accuracy. X-ray film exam alone is not suitable for lung cancer diagnosis.
Humans ; Retrospective Studies ; Solitary Pulmonary Nodule/diagnostic imaging* ; Radiography ; Multiple Pulmonary Nodules/diagnostic imaging* ; Tomography, X-Ray Computed/methods* ; Lung Neoplasms/diagnostic imaging* ; Sensitivity and Specificity ; Radiographic Image Interpretation, Computer-Assisted/methods*

Objective To explore the optimal parameters for virtual mono-energetic imaging of liver solid lesions. Methods A retrospective analysis was performed on 60 patients undergoing contrast-enhanced spectral CT of the abdomen.The iodine concentration values of hepatic arterial phase images and the CT values of different mono-energetic images were measured.The correlation coefficient and coefficient of variation were calculated. Results The average correlation coefficients between iodine concentrations and CT values of hepatic solid lesion images at 40,45,50,55,60,65,and 70 keV were 0.996,0.995,0.993,0.989,0.978,0.970,and 0.961,respectively.The correlation coefficients at 40(P=0.007),45(P=0.022),50 keV (P=0.035)were higher than that at 55 keV,and the correlation coefficients at 40 keV(P=0.134) and 45 keV(P=0.368) had no significant differences from that at 50 keV.The coefficients of variation of the CT values at 40,45,and 50 keV were 0.146,0.154,and 0.163,respectively. Conclusion The energy of 40 keV is optimal for virtual mono-energetic imaging of liver solid lesions in the late arterial phase,which is helpful for the diagnosis of liver diseases.
Humans ; Tomography, X-Ray Computed ; Retrospective Studies ; Abdomen ; Iodine ; Liver/diagnostic imaging* ; Signal-To-Noise Ratio ; Radiographic Image Interpretation, Computer-Assisted/methods*

Objective To evaluate the impact of deep learning reconstruction algorithm on the image quality of head and neck CT angiography (CTA) at 100 kVp. Methods CT scanning was performed at 100 kVp for the 37 patients who underwent head and neck CTA in PUMC Hospital from March to April in 2021.Four sets of images were reconstructed by three-dimensional adaptive iterative dose reduction (AIDR 3D) and advanced intelligent Clear-IQ engine (AiCE) (low,medium,and high intensity algorithms),respectively.The average CT value,standard deviation (SD),signal-to-noise ratio (SNR),and contrast-to-noise ratio (CNR) of the region of interest in the transverse section image were calculated.Furthermore,the four sets of sagittal maximum intensity projection images of the anterior cerebral artery were scored (1 point:poor,5 points:excellent). Results The SNR and CNR showed differences in the images reconstructed by AiCE (low,medium,and high intensity) and AIDR 3D (all P<0.01).The quality scores of the image reconstructed by AiCE (low,medium,and high intensity) and AIDR 3D were 4.78±0.41,4.92±0.27,4.97±0.16,and 3.92±0.27,respectively,which showed statistically significant differences (all P<0.001). Conclusion AiCE outperformed AIDR 3D in reconstructing the images of head and neck CTA at 100 kVp,being capable of improving image quality and applicable in clinical examinations.
Humans ; Computed Tomography Angiography/methods* ; Radiation Dosage ; Deep Learning ; Radiographic Image Interpretation, Computer-Assisted/methods* ; Signal-To-Noise Ratio ; Algorithms

Early screening based on computed tomography (CT) pulmonary nodule detection is an important means to reduce lung cancer mortality, and in recent years three dimensional convolutional neural network (3D CNN) has achieved success and continuous development in the field of lung nodule detection. We proposed a pulmonary nodule detection algorithm by using 3D CNN based on a multi-scale attention mechanism. Aiming at the characteristics of different sizes and shapes of lung nodules, we designed a multi-scale feature extraction module to extract the corresponding features of different scales. Through the attention module, the correlation information between the features was mined from both spatial and channel perspectives to strengthen the features. The extracted features entered into a pyramid-similar fusion mechanism, so that the features would contain both deep semantic information and shallow location information, which is more conducive to target positioning and bounding box regression. On representative LUNA16 datasets, compared with other advanced methods, this method significantly improved the detection sensitivity, which can provide theoretical reference for clinical medicine.
Algorithms ; Humans ; Lung Neoplasms/diagnostic imaging* ; Neural Networks, Computer ; Radiographic Image Interpretation, Computer-Assisted/methods* ; Tomography, X-Ray Computed/methods*

Objective To evaluate the effect of a deep learning reconstruction (DLR) method on the visibility of contrast-enhanced CT images of the biliary system by comparing it with different iterative reconstruction algorithms including the adaptive iterative dose reduction 3D (AIDR 3D) algorithm,forward projected model based iterative reconstruction solution (FIRST),and filtered back projection (FBP) algorithm. Methods A total of 30 patients subjected to abdominal contrast-enhanced CT and diagnosed with dilatation of common bile duct or extrahepatic bile duct were retrospectively included in this study.The images of the portal phase were reconstructed via four different algorithms (FBP,AIDR 3D,FIRST,and DLR).Signal to noise ratio (SNR) and contrast to noise ratio (CNR) of the dilated bile duct,liver parenchyma,measurable bile duct lesions,and image noise were compared between the four datasets.In subjective analyses,two radiologists independently scored the image quality (best:4 points,second:3 points;third:2 points;fourth:1 point) of the four datasets based on the noise and image visual quality of the biliary system.The Friedman and the Bonferroni-Dunn post-hoc tests were performed for comparison. Results The DLR images (bile duct:4.42±0.87;liver parenchyma:3.78±1.47) yielded higher CNR than the FBP (bile duct:2.21±1.02,P<0.001;liver parenchyma:1.43±1.29,P<0.001),AIDR 3D (bile duct:2.81±0.91,P=0.024;liver parenchyma:2.39±1.94,P=0.278),and FIRST (bile duct:2.51±1.24,P<0.001;liver parenchyma:2.45±1.81,P=0.003) images.Furthermore,the DLR images had higher SNR (bile duct:1.39±0.85,liver parenchyma:9.75±1.90) than the FBP (bile duct:0.86±0.63,P<0.001;liver parenchyma:3.31±1.12,P<0.001) and FIRST (bile duct:1.01±0.61,P=0.013;liver parenchyma:5.73±1.37,P<0.001) images,and showed lower noise (10.51±3.53) than the FBP(4.10±3.92,P<0.001),AIDR 3D (15.72±2.41,P=0.032),and FIRST (17.20±3.82,P<0.001) images.SNR and CNR showed no significant differences between FIRST and AIDR 3D images (all P>0.05).DLR images [4(4,4)] obtained higher score than FPB [1(1,1),P<0.001],AIDR3D[3 (2,3),P=0.029],and FIRST[2 (2,3),P<0.001] images. Conclusion DLR algorithm improved the subjective and objective quality of the contrast-enhanced CT image of the biliary system.
Biliary Tract ; Deep Learning ; Humans ; Imaging, Three-Dimensional ; Radiographic Image Interpretation, Computer-Assisted/methods* ; Retrospective Studies ; Tomography, X-Ray Computed/methods*

Objective To determine the appropriate averaging strategy for pancreatic perfusion datasets to create images for routine reading of insulinoma.Methods Thirty-nine patients undergoing pancreatic perfusion CT in Peking Union Medical College Hospital and diagnosed as insulinoma by pathology were enrolled in this retrospective study.The time-density curve of abdominal aorta calculated by software dynamic angio was used to decide the timings for averaging.Five strategies,by averaging 3,5,7,9 and 11 dynamic scans in perfusion,all including peak enhancement of the abdominal aorta,were investigated in the study.The image noise,pancreas signal-to-noise ratio(SNR),lesion contrast and lesion contrast-to-noise ratio(CNR)were recorded and compared.Besides,overall image quality and insulinoma depiction were also compared.ANOVA and Friedman's test were performed.Results The image noise decreased and the SNR of pancreas increased with the increase in averaging time points(all P<0.001).The lesion contrast(69.81±41.35)averaged from 5 scans showed no significant difference compared with that(72.77±45.25)averaged from 3 scans(P=0.103),both of which were higher than that in other groups(all P≤0.001).The lesion CNRs of the last four groups showed no significant difference(all P>0.99)and were higher than that of the first group(all P<0.05).There was no significant difference in overall image quality among the 5 groups(P=0.977).Conclusions Image averaged from 5 scans showed moderate image noise,pancreas SNR and relatively high lesion contrast and lesion CNR.Therefore,it is advised to be used in image averaging to detect insulinoma.
Contrast Media ; Humans ; Insulinoma/diagnostic imaging* ; Pancreas/diagnostic imaging* ; Pancreatic Neoplasms/diagnostic imaging* ; Perfusion ; Radiographic Image Interpretation, Computer-Assisted ; Reading ; Retrospective Studies ; Signal-To-Noise Ratio

Objective To identify the optimal mono-energetic enhanced spectral CT for renal cortex in cortical phase based on the iodine concentration. Methods Fifty patients with normal renal function received the abdominal enhanced spectral CT examination.The iodine concentration and CT values of the multiple mono-energetic spectral images were measured on renal cortex in cortical phase,and the correlation between the iodine concentration and the CT values and the coefficient of variation(CV)were analyzed. Results The correlation analysis demonstrated that the correlation coefficient was 0.994,0.994,0.993,0.987,0.976,0.960,and 0.938 between mono-energetic spectral CT images(40-100 keV with interval 10 keV,respectively)and iodine concentration(all
Contrast Media ; Humans ; Iodine ; Kidney Cortex/diagnostic imaging* ; Radiographic Image Interpretation, Computer-Assisted ; Tomography, X-Ray Computed

To evaluate the effect of monochromatic energy image on inferior vena cava imaging quality on dual-layer detector spectral CT. Totally 39 patients who were clinically suspected of abdominal disease and referred to perform contrast-enhanced computed tomography(CT)were prospectively enrolled and underwent abdominal examination using a single-source,dual-detector spectral CT.The delayed phase scan was performed 3 minutes after injection of 60 ml of iopamidol(320 mg/ml)at a rate of 3 ml/s.The raw images were reconstructed to obtain conventional mixed energy images and spectral based images(SBI).The 40,50,60,and 70 keV single energy images were obtained.The CT value,noise,and signal-to-noise(SNR)of inferior vena cava and the contrast-to-noise(CNR)of inferior vena cava relative to psoas major on conventional mixed energy images and the 40,50,60,70 keV single energy images were measured.The SNRs and CNRs on monoenergetic 40-70 keV images were compared with polychromatic 120 kVp images.ANOVA was used to compare the CT value,noise,SNR,and CNR among these five groups.The optimal monoenergetic image set was chosen. The differences in CT value,noise,SNR,CNR of inferior vena cava were statistically significant among five groups(all <0.05).The SNR and CNR in 40 keV group and 50 keV group were significantly higher than those in other groups(all <0.05).The SNR of 40 keV group was significantly higher than that of 50 keV group(=0.002).The CNR of 40 keV group was not statistical different compared with that of 50 keV group(=0.630). 40 keV is the optimal monoenergetic energy level for the inferior vena cava on dual-layer detector spectral CT and may be valuable for the diagnosis of inferior vena cava disease.
Abdomen ; Humans ; Radiographic Image Interpretation, Computer-Assisted ; Signal-To-Noise Ratio ; Tomography, X-Ray Computed ; Vena Cava, Inferior

To explore the value of the third generation dual-source computed tomography (CT) convolution kernel in display of pulmonary ground-glass nodule (GGN) in transverse image reconstruction.
 Methods: A total of 52 lung adenocarcinoma patients with lung CT data were selected from February 2018 to January 2019 for this study. The pulmonary CT data were reconstructed by convolutional nucleus B157, Br54, and Br49. The signal-to-noise ratio (SNR), the contrast-to-noise ratio (CNR), and the standard deviation (SD) of the image at the GGN were used as the objective evaluation standard of image quality. Subjective image quality was scored by 2 radiologists from 3 aspects (overall image quality, noise, and lesion outline).
 Results: Objective image quality evaluation, SNR and CNR of reconstructed convolution kernel Br49 (SNR: 11.36±5.39, CNR: 7.19±4.29), Br54 (SNR: 8.30±3.35, CNR: 5.09±2.86) are greater than those of Bl57 (SNR: 4.18±2.10, CNR: 3.25±1.78; all P<0.01). SD of reconstructed convolution kernel Br49 (61.80±20.17) and Br54 (80.45±20.31) is smaller than that of Bl57 (137.92±31.11, both P<0.01). In the subjective image quality evaluation, the overall image quality score 5.0(4.5, 5.0) of Br54 was higher than that of all other images [Br49: 3.0(3.0, 4.0), Bl57: 3.0(3.0, 3.5); both P<0.05]. The Br54 image showed that the lesion contour ability score 5.0(4.0, 5.0) was higher than all other images [Br49: 4.0(4.0, 5.0), Bl57: 3.0(3.0, 3.0); both P<0.05]; Br49 image noise score 3.0(3.0, 3.0) is the lowest one [Br54 4.0(4.0, 4.0), Bl57 5.0(5.0, 5.0); both P<0.05].
 Conclusion: The reasonable selection of CT convolution kernel plays an important role in the subjective and objective image quality of GGN. It is suggested that Br54 should be used as the reconstruction of convolutional kernel in pulmonary ground glass nodules, which is helpful for doctors to find and diagnose GGN.
Algorithms ; Humans ; Radiation Dosage ; Radiographic Image Interpretation, Computer-Assisted ; Signal-To-Noise Ratio ; Tomography, X-Ray Computed

BACKGROUND/AIMS: A risk prediction model for the development of hepatocellular carcinoma (HCC) from indeterminate nodules detected on computed tomography (CT) (Rad(CT) score) in patients with chronic hepatitis B (CHB)-related cirrhosis was proposed. We validated this model for indeterminate nodules on magnetic resonance imaging (MRI).METHODS: Between 2013 and 2016, Liver Imaging Reporting and Data System (LI-RADS) 2/3 nodules on MRI were detected in 99 patients with CHB. The Rad(CT) score was calculated.RESULTS: The median age of the 72 male and 27 female subjects was 58 years. HCC history and liver cirrhosis were found in 47 (47.5%) and 44 (44.4%) patients, respectively. The median Rad(CT) score was 112. The patients with HCC (n=41, 41.4%) showed significantly higher Rad(CT) scores than those without (median, 119 vs. 107; P=0.013); the Chinese university-HCC and risk estimation for HCC in CHB (REACH-B) scores were similar (both P>0.05). Arterial enhancement, T2 hyperintensity, and diffusion restriction on MRI were not significantly different in the univariate analysis (all P>0.05); only the Rad(CT) score significantly predicted HCC (hazard ratio [HR]=1.018; P=0.007). Multivariate analysis showed HCC history was the only independent HCC predictor (HR=2.374; P=0.012). When the subjects were stratified into three risk groups based on the Rad(CT) score (<60, 60–105, and >105), the cumulative HCC incidence was not significantly different among them (all P>0.05, log-rank test).CONCLUSIONS: HCC history, but not Rad(CT) score, predicted CHB-related HCC development from LI-RADS 2/3 nodules. New risk models optimized for MRI-defined indeterminate nodules are required.
Asian Continental Ancestry Group ; Carcinoma, Hepatocellular ; Diffusion ; Female ; Fibrosis ; Hepatitis B ; Hepatitis B, Chronic ; Hepatitis, Chronic ; Humans ; Incidence ; Information Systems ; Liver ; Liver Cirrhosis ; Liver Neoplasms ; Magnetic Resonance Imaging ; Male ; Multivariate Analysis ; Radiographic Image Interpretation, Computer-Assisted ; Risk Assessment