Objectives : Coronary CT angiography (CCTA)-derived plaque characteristics are associated with myocardial ischemia, but whether these relationships differ across coronary vessels remains unclear. We aimed to investigate vessel-specific associations between quantitative plaque characteristics and fractional flow reserve (FFR)-defined myocardial ischemia nd assess their incremental value beyond stenosis severity. Materials and Methods : This retrospective single-center study included 204 patients comprising 290 coronary vessels (161 LAD, 56 LCX, and 73 RCA) who underwent CCTA followed by invasive coronary angiography with FFR measurement. Quantitative plaque analysis was performed using semi-automated software. Vessel-specific feature selection was conducted using LASSO or Boruta combined with XGBoost within a five-fold cross-validation framework. Bootstrap resampling was used to assess model robustness. Incremental value was evaluated by comparing a baseline model incorporating clinical variables and severe stenosis (≥70%) with an extended model including selected plaque characteristics. Results : Ischemic vessels exhibited longer lesion length, smaller minimal lumen area, greater stenosis severity, and higher plaque burden than non-ischemic vessels. Bootstrap validation confirmed vessel-specific plaque signatures associated with ischemia. Plaque-based models achieved good discrimination for FFR-defined ischemia, with AUCs of 0.91 (95% CI: 0.87–0.97), 0.94 (0.86–1.00), and 0.97 (0.89–1.00) for LAD, LCX, and RCA, respectively. Addition of plaque characteristics significantly improved discrimination and reclassification in LAD (AUC: 0.613 vs. 0.755; NRI=0.736; IDI=0.163) and RCA vessels (AUC: 0.489 vs. 0.767; NRI=0.955; IDI=0.276), but not in LCX vessels. Conclusion : Quantitative CCTA plaque characteristics show vessel-specific associations with FFR-defined myocardial ischemia and provide incremental predictive value beyond stenosis severity, particularly in LAD and RCA vessels.

Background:The accurate identification of the left ventricular long axis is important for standardized cardiac orientation and quantitative analysis in coronary computed tomography angiography (CCTA). However, current workflows often require manual adjustment, leading to variability and increased workload. This study aimed to develop a computer-aided method for the automatic localization of the left ventricular long axis. Methods:Eighty-eight patients undergoing CCTA were retrospectively analyzed. We employed a specific range of tangential slopes within the outer contour in the mid-to-lower region and applied further correction to build spatial localization of the long axis, with physician annotations on 3D Slicer serving as the reference. The consistency was assessed via the Spearman correlation coefficient and Bland-Altman analysis. Results:In the coordinate system, the physician-labeled long axis (Axis a) and computer-measured long axis (Axis d) formed the following median angles with the coronal and transverse planes, respectively: α a, 104.10° [interquartile range (IQR), 98.95-111.92°]; α d, 105.85° (IQR, 98.44-111.84°); and β a, 126.03° (IQR, 120.89-133.78°); and β d, 125.57° (IQR, 121.38-133.66°). Spearman analysis showed highly significant correlations at α and β (r=0.88 and r=0.87, respectively; P<0.001). Bland-Altman analysis demonstrated good agreement, with SDs of bias of 8.8 for α [95% limits of agreement (LoA): -16.98 to 17.65] and 4.62 for β (95% LoA: -9.49 to 8.60). For the comparison between the Axis d and Axis a, the median distance error was 19.42 (IQR, 10.25-36.70), and the median angular error was 4.40° (IQR, 2.36-8.66). These errors were comparable to the inter-reader differences between Axis a and another physician-labeled long axis (Axis b), with no significant difference (P>0.05). The inter- and intra-correlation coefficients were good [all intraclass correlation coefficients (ICCs) >0.9; P<0.001]. Conclusions:Determining and validating the left ventricular long axis from distinct ventricular features can markedly enhance the efficiency of image processing and analysis.

Timely intervention of interstitial lung disease (ILD) was promising for attenuating the lung function decline and improving clinical outcomes. The prone position HRCT is essential for early diagnosis of ILD, but limited by its high radiation exposure. This study was aimed to explore whether deep learning reconstruction (DLR) could keep the image quality and reduce the radiation dose compared with hybrid iterative reconstruction (HIR) in prone position scanning for patients of early-stage ILD. This study prospectively enrolled 21 patients with early-stage ILD. All patients underwent high-resolution CT (HRCT) and low-dose CT (LDCT) scans. HRCT images were reconstructed with HIR using standard settings, and LDCT images were reconstructed with DLR (lung/bone kernel) in a mild, standard, or strong setting. Overall image quality, image noise, streak artifacts, and visualization of normal and abnormal ILD features were analysed. The effective dose of LDCT was 1.22 ± 0.09 mSv, 63.7

Background:The diagnostic accuracy of coronary computed tomography angiography (CTA) for obstructive coronary artery disease (CAD) is influenced by the extent of coronary artery calcium (CAC). Incidental CAC findings on non-gated chest computed tomography (CT) may inform the selection of patients for subsequent coronary CTA. The purpose of this study was to evaluate how incidental CAC findings on non-gated chest CT affect the diagnostic accuracy of coronary CTA for obstructive CAD. Methods:We retrospectively identified consecutive patients who underwent coronary CTA. Cases who underwent both chest CT and invasive coronary angiography (ICA) within 1 year of the coronary CTA were included. The severity of CAC on non-gated chest CT was assessed using a semi-quantitative scoring system and classified into four scales (absent, mild, moderate, severe). The area under the receiver operating characteristic (ROC) curve (AUC) was used to evaluate the diagnostic accuracy of coronary CTA compared to ICA. Subgroups were stratified based on the severity of CAC, and the accuracy of coronary CTA diagnosis in each subgroup was analyzed. Results:A total of 337 patients were included. The overall AUC of coronary CTA for diagnosing obstructive CAD was 0.609. In patients with absent or mild CAC, the AUC values were higher than the overall AUC (0.650 and 0.665, respectively). In contrast, among patients with moderate or severe CAC, the AUC decreased to 0.549 and 0.491, respectively. Notably, in the severe CAC subgroup, the AUC fell below 0.5, indicating no diagnostic value. The proportion of obstructive CAD was significantly higher in the severe CAC subgroup compared to the other groups (P=0.004), and the negative predictive value of coronary CTA dropped to 0. Conclusions:In patients with severe CAC on non-gated chest CT, coronary CTA is less effective in providing accurate stenosis assessment. However, the cut-off values for semi-quantitative CAC scoring remain to be clearly defined through future studies.

To compare the image quality and pulmonary nodule detectability and measurement accuracy between deep learning reconstruction (DLR) and hybrid iterative reconstruction (HIR) of chest ultra-low-dose CT (ULDCT). Participants who underwent chest standard-dose CT (SDCT) followed by ULDCT from October 2020 to January 2022 were prospectively included. ULDCT images reconstructed with HIR and DLR were compared with SDCT images to evaluate image quality, nodule detection rate, and measurement accuracy using a commercially available deep learning–based nodule evaluation system. Wilcoxon signed-rank test was used to evaluate the percentage errors of nodule size and nodule volume between HIR and DLR images. Eighty-four participants (54 ± 13 years; 26 men) were finally enrolled. The effective radiation doses of ULDCT and SDCT were 0.16 ± 0.02 mSv and 1.77 ± 0.67 mSv, respectively (P < 0.001). The mean ± standard deviation of the lung tissue noises was 61.4 ± 3.0 HU for SDCT, 61.5 ± 2.8 HU and 55.1 ± 3.4 HU for ULDCT reconstructed with HIR-Strong setting (HIR-Str) and DLR-Strong setting (DLR-Str), respectively (P < 0.001). A total of 535 nodules were detected. The nodule detection rates of ULDCT HIR-Str and ULDCT DLR-Str were 74.0

Objective: This study aimed to compare automated three-dimensional Intrapulmonary Vessel Volume (IPVV) differences between lung and mediastinal windows in healthy individuals using quantitative measurements obtained from chest Computed Tomography (CT) plain scans. Methods: A total of 258 participants (aged 21–83 years) with negative chest CT scans from routine physical examinations conducted between January to November 2023 were retrospectively enrolled. For each healthy participant, an algorithm was used to automatically extract total lung IPVVs as well as IPVVs for vessels of specific diameter. Differences in IPVVs were then compared between those extracted using the lung window and those extracted using the mediastinal window. Results: The IPVVs for the entire lung, intrapulmonary arteries, intrapulmonary veins, and small pulmonary vessels (categorized by different diameters) extracted from the lung window were significantly higher than those extracted from the mediastinal window (p<0.01). No significant sex-based differences in IPVV were observed for pulmonary arteries and veins with diameters between 0.8 and 1.6 mm, as well as pulmonary veins with diameters between 2.4 and 3.2 mm. However, in pulmonary arteries and veins with diameters between 1.6 and 2.4 mm, females had significantly higher IPVVs than males. In all other cases, IPVVs were larger in males than in females. Conclusion: This method of automatic IPVV extraction and quantitative assessment has been proven to be feasible. Automated IPVV expression effectively identified morphological characteristics of intrapulmonary vessels. The study has concluded IPVVs extracted from the lung window to be generally larger than those extracted from the mediastinal window.

To explore the performance of low-dose computed tomography (LDCT) with deep learning reconstruction (DLR) for the improvement of image quality and assessment of lung parenchyma. Sixty patients underwent chest regular-dose CT (RDCT) followed by LDCT during the same examination. RDCT images were reconstructed with hybrid iterative reconstruction (HIR) and LDCT images were reconstructed with HIR and DLR, both using lung algorithm. Radiation exposure was recorded. Image noise, signal-to-noise ratio, and subjective image quality of normal and abnormal CT features were evaluated and compared using the Kruskal–Wallis test with Bonferroni correction. The effective radiation dose of LDCT was significantly lower than that of RDCT (0.29 ± 0.03 vs 2.05 ± 0.65 mSv, p < 0.001). The mean image noise ± standard deviation was 33.9 ± 4.7, 39.6 ± 4.3, and 31.1 ± 3.2 HU in RDCT, LDCT HIR-Strong, and LDCT DLR-Strong, respectively (p < 0.001). The overall image quality of LDCT DLR-Strong was significantly better than that of LDCT HIR-Strong (p < 0.001) and comparable to that of RDCT (p > 0.05). LDCT DLR-Strong was comparable to RDCT in evaluating solid nodules, increased attenuation, linear opacity, and airway lesions (all p > 0.05). The visualization of subsolid nodules and decreased attenuation was better with DLR than with HIR in LDCT but inferior to RDCT (all p < 0.05). LDCT DLR can effectively reduce image noise and improve image quality. LDCT DLR provides good performance for evaluating pulmonary lesions, except for subsolid nodules and decreased lung attenuation, compared to RDCT-HIR. The study prospectively evaluated the contribution of DLR applied to chest low-dose CT for image quality improvement and lung parenchyma assessment. DLR can be used to reduce radiation dose and keep image quality for several indications. • DLR enables LDCT maintaining image quality even with very low radiation doses. • Chest LDCT with DLR can be used to evaluate lung parenchymal lesions except for subsolid nodules and decreased lung attenuation. • Diagnosis of pulmonary emphysema or subsolid nodules may require higher radiation doses.

Objective:To evaluate the image quality of low-dose temporal bone computed tomography (CT) in otitis media and mastoiditis patients by using deep learning reconstruction (DLR). Materials and methods:A total of ninety-seven temporal bones from 53 consecutive adult patients who had suspected otitis media and mastoiditis and underwent temporal bone CT were prospectively enrolled. All patients underwent high resolution CT protocol (group A) and an additional low-dose protocol (group B). In group A, high resolution data were reconstructed by filter back projection (FBP). In group B, low-dose data were reconstructed by DLR mild (B1), DLR standard (B2) and DLR strong (B3). The objective image quality was analyzed by measuring the CT value and image noise on the transverse image and calculating the signal-to-noise ratio (SNR) on incudomallear joint, retroauricular muscle, vestibule and subcutaneous fat. Subjective image quality was analyzed by using a five-point scale to evaluate nine anatomical structures of middle and inner ear. The number of temporal bone lesions which involved in five structures of middle ear were assessed in group A, B1, B2 and B3 images. Results:There were no significant differences in the CT values of the four reconstruction methods at four structures (all p > 0.05). The DLR group B1, B2 and B3 had significantly less image noise and a significantly higher SNR than group A at four structures (all p < 0.001). The group B1 had comparable subjective image quality as group A in nine structures (all p > 0.05), however, the group B3 had lower subjective image quality than group A in modiolus, spiral osseous lamina and stapes (all p < 0.001), the group B2 had lower subjective image quality than group A in modiolus and spiral osseous lamina (both p < 0.05). The number of temporal bone lesions which involved in five structures for group A, B1 and B2 images were no significant difference (all p > 0.05), however, the number of temporal bone lesions which involved in mastoid for group B3 images were significantly more than group A (p < 0.05). The radiation dose of high resolution CT protocol and low-dose protocol were 0.55 mSv and 0.11 mSv, respectively. Conclusion:Compared with high resolution CT protocol, in the low-dose protocol of temporal bone CT, DLR mild and standard could improve the objective image quality, maintain good subjective image quality and satisfy clinical diagnosis of otitis media and mastoiditis patients.

目的 探讨全模型迭代(Forward Projected Model-Based Iterative Reconstruction Solution,FIRST)算法对腹部CT血管造影的图像质量改善情况.方法 回顾性分析于北京医院放射科行腹部CT血管造影检查的患者30例,分别对其图像进行不同算法的重建:滤波反投影算法(Filtered Back Projection,FBP)、混合迭代重建算法(Adaptive Iterative Dose Reduction 3D,AIDR 3D)和FIRST算法,并对3组图像进行最大密度投影后处理.采用单因素方差分析法对腹部主要血管及肌肉的CT值、噪声(Standard Deviation,SD)、信噪比(Signal to Noise Ratio,SNR)和对比噪声比(Contrast to Noise Ratio,CNR)进行对比,主要包括:第12胸椎水平的腹主动脉(Abdominal Aorta,AA)、腹腔干(Celiac Trunk,CT)、肠系膜上动脉(Superior Mesenteric,SMA)、肾动脉(Renal Artery,RA)、肝右动脉(Right Hepatic Artery,RHA)水平;在主观评价方面,由两名工作经验分别为7年及10年以上的放射科技师,通过双盲法按照5分标准进行评估,通过Kappa分析观察者间一致性,并采用Kruskal-Wallis检验分析3组图像之间的差异.结果 与FBP组和AIDR 3D组相比,FIRST组中腹腔干、肠系膜上动脉、肾动脉、背景(肌肉)的SNR、CNR值显著升高(P<0.001);3组腹腔干、肠系膜上动脉、肾动脉、肝右动脉、背景(肌肉)的CT值无统计学意义(P>0.05).在主观评分方面,FIRST组及AIDR 3D组均可满足诊断需要,主观评分均优于FBP组(P<0.05);此外,FIRST组的主观评分显著优于AIDR 3D组(P<0.05).结论 在腹部CT血管造影的重建算法中,与FBP和AIDR 3D相比,FIRST重建算法可以显著改善图像质量.

Objective:To explore the effect of deep learning reconstruction (DLR) on radiation dosage reduction and image quality of CTPA compared with hybrid iterative reconstruction (HIR).Methods:A total of 100 patients with suspected pulmonary embolism (APE) or indications for CTPA due to other pulmonary artery diseases in Peking Union Medical College Hospital from December 2020 to April 2021 were prospectively enrolled and divided into HIR group and DLR group according to block randomization, with 50 cases in each group. The patient′s gender, age and body mass index (BMI) were recorded. HIR group and DLR group underwent standard deviation (SD)=8.8 and SD=15 CTPA protocols in combination with HIR and DLR algorithm respectively. Other scanning parameters and contrast medium injection plan were the same. The effective dose (ED) and size-specific dose estimate (SSDE) were calculated. Regions of interest (ROIs) were drawn in the lumen of Grade 1-3 pulmonary arteries and bilateral paravertebral muscles. The corresponding CT and SD values were recorded to acquire signal to noise ratio (SNR) and contrast noise ratio (CNR). Based on a double-blind method, two radiologists evaluated the subjective noise, visualization of pulmonary arteries, and diagnostic confidence of the two groups by 5-point Likert scales. The inconsistent results were judged comprehensively by the third radiologist. Independent samples t-test was used to compare the demographic data, radiation dosage and quantitative image quality of the two groups. Mann-Whitney U test was used to compare the subjective noise, visualization of pulmonary arteries and diagnostic confidence between the two groups. Linear weighted Kappa coefficient was calculated to analyze the consistency of the qualitative scores between the two radiologists. Results:There were no significant differences in gender, age and BMI between the two groups ( P>0.05). The CT values of Grade1-3 pulmonary arteries and paravertebral muscle had no significant differences ( P>0.05). Compared with HIR group, the ED and SSDE in DLR group decreased by about 35% to 1.3 mSv and 4.20 mGy respectively, while the SNR (30±5) and CNR (26±5) of CTPA images were higher in DLR group than those in HIR group (23±5 and 20±5, with t=-6.60 and -5.90, respectively, both P<0.001). The subjective noise score was higher in DLR group than that in HIR group ( Z=-7.34, P<0.001). In addition, two radiologists showed excellent interobserver agreement in DLR group (Kappa=0.847, 95%CI 0.553-1.000). No significant differences were found in visualization of pulmonary arteries and diagnostic confidence between the two groups ( P>0.05). Conclusion:DLR further reduced the radiation dosage and improved the image quality of CTPA, with no detriment to diagnostic confidence. Thus DLR is worthy of clinical promotion.

Objective:To evaluate the effectiveness of deep learning reconstruction (DLR) compared with hybrid iterative reconstruction (Hybrid IR) in improving the image quality in chest low-dose CT (LDCT).Methods:Seventy-seven patients who underwent LDCT scan for physical examination or regular follow-up in Peking Union Medical College Hospital from October 2020 to March 2021 were retrospectively included. The LDCT images were reconstructed with Hybrid IR at standard level (Hybrid IR Stand) and DLR at standard and strong level (DLR Stand and DLR Strong). Regions of interest were placed on pulmonary lobe, aorta, subscapularis muscle and axillary fat to measure the CT value and image noise. The signal to noise ratio (SNR) and contrast to noise ratio (CNR) were calculated. Subjective image quality was evaluated using Likert 5-score method by two experienced radiologists. The number and features of ground-glass nodule (GGN) were also assessed. If the scores of the two radiologists were inconsistent, the score was determined by the third radiologist. The objective and subjective image evaluation were compared using the Kruskal-Wallis test, and the Bonferroni test was used for multiple comparisons within the group.Results:Among Hybrid IR Stand, DLR Stand and DLR Strong images, the CT value of pulmonary lobe, aorta, subscapularis muscle and axillary fat had no significant differences (all P>0.05), but the image noise and SNR of pulmonary lobe, aorta, subscapularis muscle and axillary fat had significant differences(all P<0.05), and the CNR of images had significant difference( P<0.05), too. The CNR of Hybrid IR Stand images, DLR stand images and DLR strong images were 0.71 (0.49, 0.88), 1.06 (0.78, 1.32) and 1.14 (0.84, 1.48), respectively. Compared with Hybrid IR images, DLR images had lower objective and subjective image noise,higher SNR and CNR (all P<0.05). The scores of DLR images were superior to Hybrid IR images in identifying lung fissures, pulmonary vessels, trachea and bronchi, lymph nodes, pleura, pericardium and GGN (all P<0.05). Conclusions:DLR significantly reduced the image noise, and DLR images were superior to Hybrid IR images in identifying GGN in chest LDCT while maintaining superior image quality at relatively low radiation dose levels. Thus DLR images can improve the safety of lung cancer screening and pulmonary nodule follow-up by CT.

目的 探讨深度学习重建（Deep Learning Reconstruction,DLR）算法较混合迭代重建算法（Hybrid Iterative Reconstruction,HIR）在改善骶髂关节CT图像质量与降低辐射剂量中的能力。方法 前瞻性纳入2021年5月至2022年3月于我院接受骶髂关节CT检查的患者共76例，均行常规剂量CT（Standard-Dose CT,SDCT）与低剂量CT（Low-Dose CT,LDCT）。SDCT采用HIR重建，LDCT采用HIR与DLR（AiCE）重建，分别表示为SD-HIR、LD-HIR和LD-DLR。计算有效剂量（Effective Dose,ED）和体型特异性扫描剂量（Size Specific Dose Estimate,SSDE）。测量并计算背景噪声（Background Noise,BN）、第一骶椎与髂骨的信噪比（Signal to Noise Ratio,SNR）与对比噪声比（Contrast to Noise Ratio,CNR）等客观指标。以Likert5分制评分法对3组重建图像行主观评分，依据纽约骶髂关节炎分类标准对双侧骶髂关节进行分级诊断。所有患者依据体质量指数（Body Mass Index,BMI）分为正常体重、超重与肥胖三组，于各组内比较SDCT与LDCT辐射剂量以及3种重建图像间客观、主观评价间的差异。结果 全部患者LDCT的平均ED、SSDE均低于SDCT（P＜0.001）,ED和SSDE的降低率随BMI升高而提高。3组图像的BN、SNR、CNR和主观评分均存在统计学差异（P＜0.001）;LD-DLR图像BN值均低于SD-HIR和LD-HIR图像，SNR和CNR值均高于SD-HIR和LD-HIR图像（P＜0.05）,CNR值随着BMI增加降低。LD-DLR主观评分与SD-HIR无统计学差异（P=0.808），但高于LD-HIR（P＜0.001）。LD-DLR和SD-HIR图像对骶髂关节炎分级的诊断一致性优（Kappa值=0.888,P＜0.001）。结论 DLR算法能在降低骶髂关节CT检查辐射剂量的同时改善图像质量，满足诊断需求，有助于提高骶髂关节CT扫描的安全性。

Objective:To investigate the feasibility of chest ultra-low dose CT (ULDCT) using deep learning reconstruction (DLR) for lung cancer screening, and to compare its image quality and nodule detection rate with ULDCT iterative reconstruction (Hybrid IR) and conventional dose CT (RDCT) Hybrid IR.Methods:The patients who underwent chest CT examination for pulmonary nodules in Peking Union Medical College Hospital from October 2020 to March 2021 were prospectively included and underwent chest RDCT (120 kVp, automatic tube current), followed by ULDCT (100 kVp, 20 mA). The RDCT images were reconstructed with Hybrid IR (adaptive iterative dose reduction 3D,AIDR 3D), and ULDCT was reconstructed with AIDR3D and DLR. Radiation dose parameters and nodule numbers were recorded. Image quality was assessed using objective noise, signal-to-noise ratio (SNR) of the main trachea and left upper lobe, subjective image scores of the lung and nodules. Subjective scores were scored by 2 experienced radiologists on a Likert 5-point scale. The difference of radiation dose was compared with paired t-test between ULDCT and RDCT.The differences of quantitative indexes, objective image noise and subjective scores of the three reconstruction methods were compared with one-way analysis of variance or Friedman test. Results:Forty-five patients were enrolled, including 17 males and 28 females, aged from 32 to 74 (55±11) years. The radiation dose of ULDCT was (0.17±0.01) mSv, which was significantly lower than that of RDCT [(1.35±0.41) mSv, t=15.46, P<0.001]. There were significant differences in the image noise and SNR in the trachea and lung parenchyma and in the CT value of the trachea among ULDCT-AICE, ULDCT-AIDR 3D and RDCT-AIDR 3D images ( P<0.05). Image noise in the trachea and lung parenchyma and CT value in the trachea of ULDCT-AICE were significantly lower than those of ULDCT-AIDR 3D ( P<0.05) and comparable to RDCT-AIDR 3D ( P>0.05). There were significant differences in subjective image scores of the lung and nodules among ULDCT-AICE, ULDCT-AIDR 3D and RDCT-AIDR 3D images (χ2=50.57,117.20, P<0.001). Subjective image scores of the lung and nodules for ULDCT-AICE were significantly higher than those of ULDCT-AIDR 3D ( P<0.05), and non-inferior to RDCT-ADIR 3D ( P>0.05). All 72 clinically significant nodules detected on RDCT-ADIR 3D were also noted on ULDCT-AICE and ULDCT-AIDR 3D images. Conclusions:Chest ULDCT using DLR can significantly reduce the radiation dose, and compared with Hybrid IR, it can effectively reduce the image noise and improve SNR, and display the pulmonary nodules well. The image quality and nodule detection are not inferior to RDCT Hybrid IR routinely used in clinical practice.

目的:分析不同CT球管参数设定对腰椎骨折3 D打印模型的影响,探讨适合腰椎骨折术前3D打印模型的低剂量CT扫描参数.方法:利用东芝640层CT对L1-L2椎体棘突骨折腰椎模型进行扫描,A、B两组的扫描管电压分别为120、100 kV,管电流均依次设定为160、140、120、100、80、60、40、20 mA.由两位高年资影像诊断主治医师对骨折线的可发现、可诊断和整体图像质量进行主观评价,另由两位高年资骨科主治医师对3D打印模型清晰度、手术指导情况进行主观评价,均采取3分评价法,评分≥2分为可接受.结果:两位医师对骨折线及3D打印模型的主观评价一致性均较好,各组3D打印模型评价均为3分.管电流为20 mA时,A、B两组L2棘突骨折线评分均为1分,图像质量差,其余骨折线评分均为2分或3分.不同参数L1-L5双侧椎弓根左右径宽度均值范围为9.42～10.19 mm,不同参数腰椎3D打印模型L2-L5双侧椎弓根上下径长度均值范围为12.15～12.52 mm,差异均无统计学意义(P值均>0.05).A组管电流为20 mA,B组管电流为40 mA时,噪声最低,信噪比(SNR)、对比噪声比(CNR)最高,分别为11.7、15.31、95.72和8.62、30.97、165.10;随着辐射剂量降低,噪声降低,SNR、CNR升高.结论:采用低管电压、低管电流CT扫描能够满足3D打印模型的临床需求和腰椎骨折诊断需求.

自发性颅内出血是卒中中最严重但却最缺乏治疗方案的类型,其预测及预后均取决于血肿的体积及血肿扩张的范围,降压治疗对于限制血肿增长及改善临床结果是可信的.影响脑出血继续出血的因素有诸多方面,因此对于继续出血的预测很重要,临床通过观察各个临床指征及相关检查结果帮助诊断.随着医疗技术的飞速发展,影像检查的诊断作用日益提高,CT三维重建技术已显示其诊断的优越性.及早预防脑出血、控制继续出血极为必要,目前的治疗方案致力于解决继续出血以改善预后,而在治疗脑出血后应长期、规律地抗高血压治疗,防止再出血,提高生存率,降低致残率、病死率.本文从早期血肿扩大的相关因素、发生机制、相关分子信号、CT三维重建在颅内血肿的应用及预后治疗等方面进行综合阐述.

Objective It is to approach the clinical application effect and function of liver perfusion imaging in patients with cirrhosis by 320 row dynamic volume CT.Methods Fifty patients with liver cirrhosis and 44 normal cases were perfusion scanned by 320 row dynamic volume CT.The result was compared and analyzed.Results The time-density curve of liver cirrhosis has no significant difference compared with normal cases.There was no significant difference between the right and left lobe with liver perfusion.The changes of liver volume in cirrhotic patients had closed relations with HAP,HPP,HBF and other hepatic perfusion parameters.Conclusion Hepatic perfusion imaging can measure the whole liver perfusion parameters.Degree of cirrhosis can be classified by liver perfusion parameters.

Objective To explore the value of 640 slices volume CT 4D-DSA technique in evaluating the types of anatomical variations of hepatic arteries and the classification of arterial blood supply of hepatocellular carcinoma. Methods 4D-DSA hepatic angiography data of 31 patients with confirmed or suspected hepatocellular carcinoma underwent the 640-slices volume CT scanning were analysed. The anatomical variations of hepatic arteries and the supply artery of liver cancer were analyzed and classified, combining with the axial imaging. Results Except 2 patients the image quality does not meet the diagnostic requirements, 29 patients were enrolled. The anatomical variations of hepatic arteries includes Michels classification Ⅰtype 21 patients(79.31%),Ⅸ type 6 patients(20.67%), and Michels classification no mentioned 2 patients(22.22%). Regular blood supply of hepatic cancer was seen in 18 patients(75%, including 3 patients with parasitic blood supply), and variant blood supply was observed in 5 patients(25%). Portal vein and hepatic artery blood supply were displayed in 4 hepatocellular carcinoma. Conclusion 640 slices volume CT 4D-DSA technique can obviously display anatomical variations of hepatic arteries and the classification of arterial blood supply of hepatocellular carcinoma.

Objective To explore the value of 4D CT angiography and dynamic whole-brain CT perfusion imaging in evaluating collateral vessels and its influence of brain perfusion and prognosis in ischemic stroke.Methods 34 patients(M 24,F 10;age range 42~84 years,mean 68.22 years) with ischemic cerebrovascular disease underwent 4D dynamic whole-brain CTA-CTP imaging.22 patients had follow-up CT or MR imaging 20~40 days after initial scan.CTA-CTP results were compared with follow-up imaging results.Results Among 34 patients,4D dynamic CTA-CTP imaging was negative in 3 patients and positive in 31 patients,with sensitivity of 96.88% and specificity of 100%.29 cases were confirmed to be ischemia stroke.4D CTA of the stroke patients revealed internal carotid artery(ICA) and/or middle cerebral artery(MCA) occlusion or stenosis in 23 cases.12 of them with visible collateral vessel formation had small final infarct and better prognosis.Conclusion 4D CTA imaging is a reliable and effect method to determine hemodynamic changes and collateral vessel formation of the ischemic stroke.It provides helpful information for clinical individual treatment.