Objective To explore the value of deep learning image reconstruction(DLIR)algorithm to improve the quality of low keV monochromatic portal vein images of energy spectrum CT.Methods Fifty patients who underwent enhanced upper abdominal energy spectrum CT scan were selected.Mixed-model adaptive statistical iterative reconstruction-Veo(50%ASIR-V)algorithm and high-deep learning image reconstruction(DLIR-H)algorithm were used to obtain monochromatic images at 40-70 keV(with intervals of 10 keV).The CT and standard deviation(SD)values of the portal vein trunk,left and right branches,and erector spinae muscle were measured in the transverse position,and the signal-to-noise ratio(SNR)and portal vein contrast-to-noise ratio(CNR)were calculated for objective evaluation.The portal vein image quality between the two algorithms and different energy was subjectively scored by two physicians.Results In terms of objective evaluation:compared with 50%ASIR-V,the CNR and SNR of portal vein in monochromatic DLIR-H images at the same keV between 40-70 keV energy levels were increased while the SD value was decreased(P＜0.05),and the CT value was unchanged;there was no statistical difference in the magnitude of change in CNR between the two algorithms at different energy levels(P＞0.05);there was a statistically significant difference in the magnitude of change in SNR and SD value(P＜0.05)and the magnitude of change was the largest at 40 keV;comparison between different energy levels of DLIR-H,the CNR and SD value of 40 keV DLIR-H were the highest(P＜0.05),and there was no significant difference in the SNR(P＞0.05).In terms of subjective evaluation:there was no significant difference between the subjective scores of the two algorithms at the same keV from 40-70 keV(P＞0.05),and the two reconstruction algorithms at 40 keV and 50 keV had the highest subjective scores between different keV.Conclusion The DLIR algorithm can reduce the noise of low keV monochromatic images,improve the image quality of portal vein.

Objective To explore the value of deep learning image reconstruction(DLIR)algorithm to improve the quality of low keV monochromatic portal vein images of energy spectrum CT.Methods Fifty patients who underwent enhanced upper abdominal energy spectrum CT scan were selected.Mixed-model adaptive statistical iterative reconstruction-Veo(50%ASIR-V)algorithm and high-deep learning image reconstruction(DLIR-H)algorithm were used to obtain monochromatic images at 40-70 keV(with intervals of 10 keV).The CT and standard deviation(SD)values of the portal vein trunk,left and right branches,and erector spinae muscle were measured in the transverse position,and the signal-to-noise ratio(SNR)and portal vein contrast-to-noise ratio(CNR)were calculated for objective evaluation.The portal vein image quality between the two algorithms and different energy was subjectively scored by two physicians.Results In terms of objective evaluation:compared with 50%ASIR-V,the CNR and SNR of portal vein in monochromatic DLIR-H images at the same keV between 40-70 keV energy levels were increased while the SD value was decreased(P＜0.05),and the CT value was unchanged;there was no statistical difference in the magnitude of change in CNR between the two algorithms at different energy levels(P＞0.05);there was a statistically significant difference in the magnitude of change in SNR and SD value(P＜0.05)and the magnitude of change was the largest at 40 keV;comparison between different energy levels of DLIR-H,the CNR and SD value of 40 keV DLIR-H were the highest(P＜0.05),and there was no significant difference in the SNR(P＞0.05).In terms of subjective evaluation:there was no significant difference between the subjective scores of the two algorithms at the same keV from 40-70 keV(P＞0.05),and the two reconstruction algorithms at 40 keV and 50 keV had the highest subjective scores between different keV.Conclusion The DLIR algorithm can reduce the noise of low keV monochromatic images,improve the image quality of portal vein.

Objective To explore the application value of deep learning image reconstruction(DLIR)with coronary computed tomography angiography(CCTA)using 100 kV.Methods Sixty patients who underwent CCTA were selected.The tube voltage of 100 kV,noise index of 24 were applied.The 60％adaptive statistical iterative reconstruction-Veo(ASIR-V)and DLIR-low(DLIR-L),DLIR-medium(DLIR-M)and DLIR-high(DL1R-H)were reconstructed.The CT values and standard deviation(SD)values of the aortic root,left main artery,left anterior descending artery,left circumflex artery,right coronary artery and pericardial fat of the four groups of images were measured,and the signal-to-noise ratio(SNR)and contrast-to-noise ratio(CNR)were calculated.Two radiologists with five-year working experience subjectively evaluated the image quality using a five-point method double-blindly.Results The differences in noise(SD values),SNR values and CNR values among the four groups of images were statistically significant(P＜0.001).As the reconstruction gradually changed of 60％ASIR-V,DLIR-L,DLIR-M and DLIR-H,the coronary SD values gradually decreased,while the SNR values and CNR values gradually increased,among which the DLIR-H had the lowest SD values and the highest SNR values and CNR values.The subjective scores of the four groups of images by two radiologists showed good consistency(Kappa value=0.929,P＜0.001),and the subjective scores were all above 3 points which met the clinical diagnosis criteria.The subjective scores of DLIR-L,DLIR-M and DLIR-H were significantly higher than those of 60％ASIR-V(P＜0.001),with the DLIR-H achieving the highest subjective score.Conclusion DLIR can significantly reduce image SD value and improve image quality of CCTA with 100 kV,among which DLIR-H has the best effect on improving CCTA image quality.

Purpose To investigate the feasibility of CT-based feature tracking technology to quantify left ventricular myocardial strain(MS)and its significance in hypertrophic obstructive cardiomyopathy(HOCM).Materials and Methods A total of 35 HOCM patients who underwent cardiac coronary angiography from March 2019 to December 2021 in the First Affiliated Hospital of the Air Force Military Medical University were retrospectively included,and a total of 60 cases who were negative for cardiac coronary angiography among those who visited the hospital with suspected coronary artery disease were randomly enrolled.Conventional cardiac functional parameters and MS parameters were quantified via post-processing software,and differences of parameters between the groups were analyzed.The diagnostic efficacy of MS parameters for HOCM was further evaluated.Results Compared to the control group,the HOCM group exhibited significant increases in various conventional left ventricular functional parameters,including left ventricular wall thickness,mass,mass index,end-diastolic volume and stroke volume(t=2.119 to 24.861,all P<0.05).However,there were no statistically significant differences in end-systolic volume and cardiac output between the two groups(P>0.05).The global longitudinal and radial strain values of HOCM group were significantly lower than those of control group(t=12.857,-6.427,P<0.01),while the endocardial global circumferential strain of HOCM group was significantly higher than that of control group(t=-2.369,P<0.05).Among MS parameters,global longitudinal strain exhibited the best diagnostic efficacy for HOCM,with an area under the curve of 0.997.A cutoff value of≤20.78%for global longitudinal strain showed that the sensitivity and specificity was 100%and 95%,respectively.Conclusion The MS parameters quantified by the CT-based feature tracking technique are superior to left ventricular ejection fraction in quantifying left ventricular function,with the highest sensitivity and specificity for early myocardial function impairment of longitudinal strain.In addition,the technique has good repeatability and is expected to become a new indicator for the assessment of myocardial function in HOCM.

Objective To establish a neural network model based on enhanced CT for distinguishing ISUP grade of clear cell renal cell carcinoma (ccRCC). Methods We collected 131 cases of ccRCC, with 92 cases of low ISUP grade and 39 cases of high ISUP grade. Patients were divided into training set and validation set according to 5:5 stratified sampling. The enhanced CT images of each ccRCC patient were evaluated by the radiologist. Recursive feature elimination (RFE) was used to reduce the dimension of patients' general features and enhanced CT features, which was used for neural network modeling and validation. Results Patients' general features and enhanced CT features were verified by RFE method and then reduced to 14 features. The top 5 features were growth pattern, necrosis, enlargement of lymph nodes, tumor size and capsule. The AUC of the neural network model based on these 5 features in training set was 0.8844 (95%CI: 0.8062-0.9626), sensitivity was 89.47% and specificity was 82.61%; and those in validation set were 0.7924 (95%CI: 0.6567-0.9280), 75.00% and 86.96%, respectively. Conclusion The neural network model of ccRCC ISUP grade based on enhanced CT has relatively high diagnostic efficiency.

Objective:To explore the injury effect and its possible mechanism of amiodarone on human umbilical vein endothelial cells (HUVECs).Methods:After 3 generations of cultivation, the HUVECs were seeded in 96-well plates and incubated with amiodarone (0, 10, 20, 30, and 60 μmol/L) for 24 hours. The cell viability was detected using cell counting kit 8 (CCK-8) assay and the relative viability of cells incubated with different concentrations of amiodarone were calculated by taking the cell viability of the 0 μmol/L group as 100%. The concentration of amiodarone at which cell viability was reduced to 70% was selected for subsequent experiments. The effect of amiodarone of this concentration on the activity of HUVECs after action for different time (6, 12, 24, 36, and 48 hours) was detected using the CCK-8 assay. HUVECs cultured with amiodarone of this concentration were set as the experimental groups and those without amiodarone were set as the control group. Apoptosis rate of HUVECs was detected by Annexin V-FITC/P flow cytometry; the protein and mRNA expression levels of B-cell lymphoma 2 (Bcl-2), Bcl-2-associated X protein (Bax), Caspase-3, interleukin 10 (IL-10), IL-1β, IL-6, and tumor necrosis factor alpha (TNF-α) were detected using western blotting and real-time fluorescence quantitative polymerase chain reaction, respectively; the reactive oxygen species (ROS) was detected by DCFH-DA fluorescence probe assay; the superoxide dismutase (SOD) activity was detected by water-soluble tetrazolium-1 assay; the reduced glutathione (GSH) content was detected by microplate assay.Results:The viabilities of HUVECs incubated with amiodarone at concentration of 10, 20, 30, and 60 μmol/L for 24 hours were (88.82±2.64)%, (74.96±1.75)%, (64.95±2.10)%, and (18.57±0.65)%, respectively; differences were all significant (all P<0.01) between each experiment group and control group, as well as between each experiment group. Amiodarone at a concentration of 30 μmol/L was used for subsequent experiments. After incubating with 30 μmol/L amiodarone for 6, 12, 24, 36, and 48 hours, the viabilities of HUVECs were (90.19±1.88)%, (82.81±2.51)%, (75.33±1.37)%, (65.76±1.85)%, and (47.01±3.29)%, respectively; differences were all significant (all P<0.01) between each experiment group and control group, as well as between each experiment group. Compared with the control group, the apoptosis rate of cells in the experimental group was significantly higher (48.59% vs. 16.34%, P<0.01), the protein and mRNA expression levels of pro-apoptotic proteins Bax and caspase-3, and pro-inflammatory factors IL-1β, IL-6, and TNF-α were higher (all P<0.01), whereas the protein and mRNA expression levels of anti-apoptotic protein Bcl-2 and anti-inflammatory factor IL-10 were lower ( P<0.05, P<0.01). Conclusions:Amiodarone can cause HUVECs injury, which would be enhanced with the increase of concentration and action time of amiodarone. Amiodarone may cause HUVECs injury by inducing apoptosis, inflammatory response, and oxidative stress.

Objective:To explore the injury effect and its possible mechanism of amiodarone on human umbilical vein endothelial cells (HUVECs).Methods:After 3 generations of cultivation, the HUVECs were seeded in 96-well plates and incubated with amiodarone (0, 10, 20, 30, and 60 μmol/L) for 24 hours. The cell viability was detected using cell counting kit 8 (CCK-8) assay and the relative viability of cells incubated with different concentrations of amiodarone were calculated by taking the cell viability of the 0 μmol/L group as 100%. The concentration of amiodarone at which cell viability was reduced to 70% was selected for subsequent experiments. The effect of amiodarone of this concentration on the activity of HUVECs after action for different time (6, 12, 24, 36, and 48 hours) was detected using the CCK-8 assay. HUVECs cultured with amiodarone of this concentration were set as the experimental groups and those without amiodarone were set as the control group. Apoptosis rate of HUVECs was detected by Annexin V-FITC/P flow cytometry; the protein and mRNA expression levels of B-cell lymphoma 2 (Bcl-2), Bcl-2-associated X protein (Bax), Caspase-3, interleukin 10 (IL-10), IL-1β, IL-6, and tumor necrosis factor alpha (TNF-α) were detected using western blotting and real-time fluorescence quantitative polymerase chain reaction, respectively; the reactive oxygen species (ROS) was detected by DCFH-DA fluorescence probe assay; the superoxide dismutase (SOD) activity was detected by water-soluble tetrazolium-1 assay; the reduced glutathione (GSH) content was detected by microplate assay.Results:The viabilities of HUVECs incubated with amiodarone at concentration of 10, 20, 30, and 60 μmol/L for 24 hours were (88.82±2.64)%, (74.96±1.75)%, (64.95±2.10)%, and (18.57±0.65)%, respectively; differences were all significant (all P<0.01) between each experiment group and control group, as well as between each experiment group. Amiodarone at a concentration of 30 μmol/L was used for subsequent experiments. After incubating with 30 μmol/L amiodarone for 6, 12, 24, 36, and 48 hours, the viabilities of HUVECs were (90.19±1.88)%, (82.81±2.51)%, (75.33±1.37)%, (65.76±1.85)%, and (47.01±3.29)%, respectively; differences were all significant (all P<0.01) between each experiment group and control group, as well as between each experiment group. Compared with the control group, the apoptosis rate of cells in the experimental group was significantly higher (48.59% vs. 16.34%, P<0.01), the protein and mRNA expression levels of pro-apoptotic proteins Bax and caspase-3, and pro-inflammatory factors IL-1β, IL-6, and TNF-α were higher (all P<0.01), whereas the protein and mRNA expression levels of anti-apoptotic protein Bcl-2 and anti-inflammatory factor IL-10 were lower ( P<0.05, P<0.01). Conclusions:Amiodarone can cause HUVECs injury, which would be enhanced with the increase of concentration and action time of amiodarone. Amiodarone may cause HUVECs injury by inducing apoptosis, inflammatory response, and oxidative stress.

Objective: A predictive model of WHO/ISUP grading of renal clear cell carcinoma was constructed based on CT radiomics. Methods: The clinical data of 104 patients with ccRCC confirmed by operation or biopsy from March 2014 to December 2018 in the Affiliated Hospital of Shaanxi University of Traditional Chinese Medicine were retrospectively analyzed. There were 70 males and 34 females, and the age was 61.2±11.7 years. The patients were randomly divided into development cohort (73 cases) and validation cohort (31 cases) by stratified sampling according to 7∶3 ratio. According to the WHO/ISUP pathological grading criteria of renal cancer in 2016, Ⅰ and Ⅱ were defined as low-grade group, Ⅲ and Ⅳ were defined as high-grade group. The radiomics features of ccRCC were calculated in cortical phase images of CT enhanced scanning. LASSO regression was used to reduce the radiomics feature dimensionality in the training group, and to establish radiomics risk scores. The binary logistic regression was used to build the prediction model, which was used in the validation group. Bootstrap method was used to validate the model of training and validation group. AUC, sensitivity and specificity were calculated respectively. Hosmer-Lemeshow goodness-of-fit test was used to evaluate model calibration degree. Results: After dimensionality reduction, the radiomics risk score of ccRCC was established. The low and high-level risk scores of the training group were -2.49±1.73 and 1.23±2.17, with significant difference (t=-7.785, P < 0.01). The binary logistic regression multivariate analysis showed that the radiomics risk score was an independent risk factor in identifying low or high-grade ccRCC with odds ratio of (OR=3.576, 95%CI 1.964~6.513). The predictive model was Y=1/[1+ exp(-Z)], Z=1.274×radiomics risk score+ 0.072. The AUC of radiomics risk score in training group was 0.940 (95%CI 0.883-0.998) with 95.5% sensitivity and 88.2% specificity after internal verification by Bootstrap method, and good Hosmer-Lemeshow goodness-of-fit test (χ²=4.463, P>0.05). The low and high-level risk scores of the Validation group were -2.27±2.02 and 0.82±2.08, with significant difference (t=-3.832, P<0.01). The AUC in validation group was 0.859(95%CI 0.723-0.995) with 77.8% sensitivity and 81.8% specificity, and with good Hosmer-Lemeshow goodness-of-fit test (χ²=14.554, P=0.068) as well. Conclusions The prediction model based on CT radiomics has high accuracy in predicting high or low grade of ccRCC.

Objective To explore the clinical value of adaptive statistical iterative reconstruction(ASIR)combined with automatic tube current modulation in low dose scan of chest CT.Methods 80 patients who underwent chest CT scan were randomly divided into four groups,with a noise index(NI)of 14,16,18,and 20,respectively.Automatic tube current modulation technique for chest CT scan was adopted for all patients.The standard dose group,with a NI of 14,was reconstructed with FBP.While the low-dose groups,with a NI of 16,18 and 20,were reconstructed with four ASIR levels(20% ASIR,40% ASIR,60% ASIR and 80% ASIR) in each group.The volume CT dose index(CTDIvol),dose length product(DLP)and effective dose(ED)were recorded of the four groups.The signal-to-noise ratio(SNR)and contrast-to-noise ratio(CNR)were calculated of the different reconstruction groups. Two radiologists with more than five years of work experience blindly scored the subjective image quality.Results Effective dose of the four groups with different NI were(3.29 ± 1.21)mSv,(3.10 ± 1.07)mSv,(2.20 ± 0.82)mSv and(1.97 ± 1.21)mSv,respectively. In all the reconstruction groups,when NI was constant,the greater the ASIR,the lower the SD.When ASIR percentage was constant, the SD was increased and the SNR was reduced along with NI rising up.When the parameters were set as NI 18 and ASIR 60%,the SD would be smaller and SNR would be higher than that of a standard dose group.In aspect of the scores from subjective image quality evaluation,there was no significant difference(P> 0.05).The average ED was decreased by about 33.2% when compared to the standard dose group.Conclusion Combining automatic tube current modulation with ASIR has a certain clinical practicality.When the NI is set at 18 and ASIR is 60%,the image quality can be optimal for not only satisfying the requirements of clinical diagnosis, but also reducing radiation dose in chest CT scanning.

Objective To demonstrate the feasibility of high-resolution computed tomography(HRCT)reconstructed with a model-based iterative reconstruction (MBIR)for evaluating early peripheral lung cancer (≤3 cm),by comparing image quality obtained from MBIR,filtered back proj ection reconstruction(FBP)and state of the art adaptive statistical iterative reconstruction(ASIR)algorithm respectively.Methods A total of 30 patients confirmed with lung cancer by postoperative pathology were enrolled in the study.A chest phantom was also used to evaluate image noise,spatial resolution and density resolution.Both patients and chest phantom were received HRCT,and the images were reconstructed using FBP,ASIR(40% ASIR and FBP mix)and MBIR.The objective CT value, standard deviation(SD)and signal noise ratio(SNR)were measured.Two radiologists used a semi-quantitative to rate subjective image quality of lung nodules.Results There was no significant difference in CT value between the three reconstruction algorithms (P>0.05).But significant improvements in objective image noise were observed in MBIR compared with FBP and ASIR (P<0.05):including the SD value in back muscle [(12.63±1.70)with MBIR vs (31.58±5.21)with FBP and (24.55±4.14)with ASIR],and in subcutaneous fat [(12.77±2.53)vs (24.39±5.08)and (19.20±4.11)].Subjective image noise of the three group were also significantly difference:FBP with lowest subjective noise score;and MBIR with highest subjective noise score.The sharpness of small vessels and bronchi and diagnostic acceptability with MBIR were significantly better than with FBP and ASIR (P< 0.05).Conclusion Lung HRCT reconstructed with MBIR provides diagnostically more acceptable images for the detailed analyses of peripheral lung cancer compared with FBP and ASIR.