Objective:To compare contrast-induced hardening artifacts of the axillary vein during the arterial phase between dual-energy CT (DECT) and single-energy CT (SECT) with different contrast agent injection rates in thoracoabdominal enhanced CT examinations, and to assess their clinical value. Methods: A retrospective study was conducted on 77 cases of chest and abdomen enhanced CT scans that were performed from January to November 2024 at our hospital. These scans were performed on the same subjects using both dual-energy (experimental group) and single-energy (control group) CT techniques. The differences between the two groups were compared in terms of axillary vein contrast agent artifact size, arterial phase enhancement, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), subjective evaluations of artifact severity, and overall image quality. The subjective evaluations of artifact severity and image quality were independently assessed by two radiologists. Results: The objective scores for axillary vein contrast agent artifacts in the experimental group were significantly lower than those in the control group. The arterial CT values in the experimental group were higher than those in the control group. The SNR and CNR in non-artifact-affected regions, such as bone, muscle, and adipose tissue, in the experimental group were not lower than those in the control group, whereas the radiation dose was significantly lower in the experimental group. Subjective scores for hardening artifacts were significantly better in the experimental group, and image quality scores in regions outside the axillary vein hardening artifacts were non-inferior to those of the control group. Interobserver agreement between the two radiologists was high. Conclusion: Dual-energy CT blended images combined with a contrast injection rate of 3 mL/s can effectively reduce contrast-induced hardening artifacts, improve image quality, and lower radiation dose in thoracoabdominal enhanced CT examinations, offering superior clinical applicability.

To explore the effectiveness of radiographic biomarkers on transition area (TA)—the grayscale gradient zone from carious lesion to normal dentine on radiographs—for identifying deep caries/reversible pulpitis and chronic pulpitis via diagnostic model analysis. This retrospective study included 392 caries cases. Canny edge detection was used to define the TA region. Texture parameters were extracted from the carious lesions (S1) and TA region (S2) by MaZda software on radiographs. Least absolute shrinkage and selection operator (LASSO) regression analysis was used to select biomarkers. Diagnostic models were fitted and model performance was furtherly evaluated by internal and external validation, decision curve analysis was applied to evaluate clinical benefits. TA-based biomarkers (e.g., TA thickness, TA ratio, S2-S(5,-5) contrast and S2-WavEnLL-s-4) were significantly associated with the diagnosis of deep caries/reversible pulpitis versus chronic pulpitis, model performance significantly improved when adding the above biomarkers (likelihood-ratio test; p < 0.05, with an increase of AUC from 0.67 (reference model) to 0.89), and these results were maintained in a small external validation cohort. Clinical benefit was greater with the application of TA-based biomarkers. TA-based biomarkers are proven to be an effective tool in differentiating deep caries/reversible pulpitis and chronic pulpitis, preoperative diagnosis was improved with the above biomarkers compared to the reference model.

Treacher Collins syndrome (TCS) is a rare congenital craniofacial disorder, typically inherited as an autosomal dominant condition. Here, we report on a family in which germline mosaicism for TCS was likely present. The proband was diagnosed with TCS based on the typical clinical features and a pathogenic variant TCOF1 (c.4369_4373delAAGAA, p.K1457Efs*12). The mutation was not detected in his parents' peripheral blood DNA samples, suggesting a de novo mutation had occurred in the proband. However, a year later, the proband's mother became pregnant, and the amniotic fluid puncture revealed that the fetus carried the same mutation as the proband. Prenatal ultrasound also indicated a maxillofacial dysplasia with unilateral microtia. The mother then disclosed a previous birth history in which a baby had died of respiratory distress shortly after birth, displaying a TCS-like phenotype. Around the same time, the proband's father was diagnosed with mild bilateral conductive hearing loss. Based on array data, we concluded that the father may have had germline mosaicism for TCOF1 mutation. Our findings highlight the importance of considering germline mosaicism in sporadic de novo TCOF1 mutations when providing genetic consulting, and prenatal diagnosis is important when the proband's parents become pregnant again.

The aim of this study was to compare the outcomes of different mapping procedures based on anatomic or default frequency distribution in postlingual deafness adults who underwent cochlear implantation (CI). Forty-eight adults with postlingual deafness who underwent CI (MED-EL) from January 2021 to May 2022 in our hospital were prospectively recruited. The participants were randomly assigned to two groups (the anatomic group and the default group). Postoperative computerized tomography (CT) scans were evaluated with Otoplan® to determine the angular insertion depth (AID) and the specific locations of the intracochlear electrodes. Anatomic maps were imported into MAESTRO 9.0 software (MED-EL) for anatomy-based fitting for anatomic group, while default mapping program was set up for the default group. Hearing thresholds, Speech Recognition Scores (SRS), and subjects’ auditory and musical abilities were evaluated 1 year after using the CI. Differences were determined in two groups using Stata statistical software, with significance defined as p < 0.05. SRS under noisy conditions was significantly greater for anatomic group than the default group (p = 0.02). Under quiet conditions, however, mean hearing thresholds (0.5, 1, 2, and 4 kHz) and SRS did not differ significantly between the two groups (p = 0.07). Modified questionnaires showed that auditory (p = 0.02) and musical (p = 0.01) quality were significantly better following the anatomic mapping than the default procedure. CI program based on the anatomic distribution may bring better SRS under noise conditions as well as better auditory and musical qualities than based on the default frequency distribution.

RATIONALE AND OBJECTIVE:To investigate the impact of the deep learning reconstruction (DLR) technique on the image quality of CT angiography (CTA) derived from 80-kVp cerebral CT perfusion (CTP) data and compare it with hybrid-iterative reconstruction (HIR). MATERIALS AND METHODS:Thirty-three patients underwent CTP at 80 kVp were prospectively enrolled. CTP data were reconstructed with HIR and DLR. Four image datasets were reconstructed: HIRpeak and DLRpeak were single arterial phase images derived from the time point showing the peak value, HIRtMIP and HIRtAve were time-resolved maximum intensity projection image and time-resolved average image derived from three time points with the greatest enhancement of HIR. The mean CT values, standard deviation, signal-to-noise ratio, and contrast-to-noise ratio of the internal carotid artery and basilar artery were compared among the four image dataset. Image quality was performed using a five-point rating scale. Arterial stenosis was evaluated. RESULTS:DLRpeak had the highest CT value and contrast-to-noise ratio in the internal carotid artery and basilar artery (all p < 0.001). DLRpeak showed the best subjective image quality and had the highest score (4.93 ± 0.4) compared to the other three HIR CTA images (all p < 0.001). The degree of vascular stenosis was consistent among the four evaluated sequences (HIRtAve, HIRpeak, and HIRtMIP DLRpeak). CONCLUSION:For CTA derived from 80-kVp cerebral CTP data, images reconstructed with deep learning showed better image quality and improved intracranial artery visualization than those processed with HIR and other currently used techniques.

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 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.

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.

OBJECTIVES:To investigate whether deep learning reconstruction (DLR) could keep image quality and reduce radiation dose in interstitial lung disease (ILD) patients compared with HRCT reconstructed with hybrid iterative reconstruction (hybrid-IR).METHODS:Seventy ILD patients were prospectively enrolled and underwent HRCT (120 kVp, automatic tube current) and LDCT (120 kVp, 30 mAs) scans. HRCT images were reconstructed with hybrid-IR (Adaptive Iterative Dose Reduction 3-Dimensional [AIDR3D], standard-setting); LDCT images were reconstructed with DLR (Advanced Intelligence Clear-IQ Engine [AiCE], lung/bone, mild/standard/strong setting). Image noise, streak artifact, overall image quality, and visualization of normal and abnormal features of ILD were evaluated.RESULTS:The mean radiation dose of LDCT was 38% of HRCT. Objective image noise of reconstructed LDCT images was 33.6 to 111.3% of HRCT, and signal-to-noise ratio (SNR) was 0.9 to 3.1 times of the latter (p < 0.001). LDCT-AiCE was not significantly different from or even better than HRCT in overall image quality and visualization of normal lung structures. LDCT-AiCE (lung, mild/standard/strong) showed progressively better recognition of ground glass opacity than HRCT-AIDR3D (p < 0.05, p < 0.01, p < 0.001), and LDCT-AiCE (lung, mild/standard/strong; bone, mild) was superior to HRCT-AIDR3D in visualization of architectural distortion (p < 0.01, p < 0.01, p < 0.01; p < 0.05). LDCT-AiCE (bone, strong) was better than HRCT-AIDR3D in the assessment of bronchiectasis and/or bronchiolectasis (p < 0.05). LDCT-AiCE (bone, mild/standard/strong) was significantly better at the visualization of honeycombing than HRCT-AIDR3D (p < 0.05, p < 0.05, p < 0.01).CONCLUSION:Deep learning reconstruction could effectively reduce radiation dose and keep image quality in ILD patients compared to HRCT with hybrid-IR.KEY POINTS:• Deep learning reconstruction was a novel image reconstruction algorithm based on deep convolutional neural networks. It was applied in chest CT studies and received auspicious results. • HRCT plays an essential role in the whole process of diagnosis, treatment efficacy evaluation, and follow-ups for interstitial lung disease patients. However, cumulative radiation exposure could increase the risks of cancer. • Deep learning reconstruction method could effectively reduce the radiation dose and keep the image quality compared with HRCT reconstructed with hybrid iterative reconstruction in patients with interstitial lung disease.

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.

目的探讨100 kVp条件下全模型迭代技术(Forward Projected Model-Based Iterative Reconstruction Solution,FIRST对头颈CT血管造影（CT Angiography,CTA）图像质量的影响。方法分析33例行头颈CTA检查的患者并重建出滤波反投影（Filtered Back Projection,FBP）、混合迭代重建算法（Adaptive Iterative Dose Reduction 3D,AIDR-3D）和FIRST三组图像,对三组图像进行最大密度投影处理。从客观测量和主观评分两方面来评价不同重建方式对头颈CTA图像质量的影响。结果客观指标:三组图像间的CT值在主动脉弓处相比具有统计学差异（P＜0.001）,其余部位结果比较,差异无统计学差异（P＞0.05）;三组间的SD值、信噪比和噪声比值,在各血管均具有统计学差异（P＜0.05）。主观评分:三组不同重建图像均能满足诊断要求,FBP组与AIDR-3D组图像间评分无统计学差异（P＞0.05）,FIRST组评分与FBP组、AIDR-3D组相比均具有统计学差异（P＜0.05）。结论在头颈CTA检查图像质量上,与FBP和AIDR-3D重建算法相比,FIRST重建技术能够降低图像噪声,提高图像信噪比,在颅内细小血管的显示能力方面表现更佳,可以在临床检查中加以应用。

OBJECTIVE:The objective of this study was to compare the diagnostic performance of magnetic resonance imaging (MRI) and computed tomography (CT) in differentiating pleomorphic adenomas from Warthin tumors using radiomics. STUDY DESIGN:We retrospectively reviewed 626 patients who underwent preoperative MRI or CT for parotid tumor diagnosis. Patient groups were balanced by propensity score matching (PSM) and 123 radiomic features were extracted from tumor images. Radiomic signatures (rad-scores) were generated using a least absolute shrinkage and selection operator logistic regression model. The Canny edge detector was used to define tumor borders (border index). The diagnostic performance of rad-score and border index before and after PSM was evaluated with area under the receiver operating characteristic curve analysis. RESULTS:For differentiation of pleomorphic adenomas and Warthin tumors, rad-score and border index areas under the curve for MRI after PSM were 0.911 (95% confidence interval [CI], 0.871-0.951) and 0.716 (95% CI, 0.646-0.787), respectively; those for CT were 0.876 (95% CI, 0.829-0.923) and 0.608 (95% CI, 0.527-0.690), respectively. Tumor border index on MRI, but not CT, had superior diagnostic performance (P < .05); MRI- and CT-based rad-scores showed similar performance (P >.05). CONCLUSIONS:MRI is superior to CT for tumor margin examination; however, the radiomics features of both modalities showed no difference.

目的探讨双层探测器CT行胸部低剂量扫描采用不同重建技术对图像质量的影响。方法应用飞利浦双层探测器CT,纳入50例行胸部低剂量CT平扫的患者。分别采用不同重建算法进行重建,对图像质量进行定量测量及主观图像质量评估,对比分析不同重建算法图像的客观及主观结果。结果 FBP、iDose~4-L3、iDose~4-L6、IMR-RL1及IMR-SP1图像噪声值分别为55.69±10.07、64.00±8.07、31.42±6.11、17.30±2.77、13.76±2.59。与FBP组相比,iDose~4-L3组、iDos4-L6组、IMR-RL1组及IMR-SP1组4组的噪声分别降低了21.36%,43.66%,74.81%,68.33%（P值均＜0.05）。IMR组（IMR-RL1、IMR-SP1）的SNR及CNR均明显高于FBP组、iDose~4组（iDose~4-L3、iDose~4-L6）（P值均＜0.05）,但IMR-RL1及IMR-SP1两组间SNR、CNR差异不具有统计学意义（P＞0.05）。iDose~4-L3组、iDose~4-L6组的SNR及CNR值略高于FBP组,但差异无统计学意义（P值分别为0.080、0.091）。IMR重建图像评分高于FBP、iDose~4-L3及iDose~4-L6组,差异有统计学意义（P值均＜0.05）。IMR-SP1组肺窗图像质量最优,IMR-RL1组纵隔图像质量最优,IMR-SP1组与IMR-RL1组两组间的纵隔及肺窗质量无明显差异（P＞0.05）。结论在低剂量胸部CT中,IMR重建算法降噪及提高图像质量效果优于FBP、iDose~4重建算法。

目的探究70 keV虚拟单能量图像（VMI）提升低剂量CT（LDCT）图像质量的可行性。方法回顾性分析50例患者胸部低剂量CT扫描影像资料,低剂量扫描条件为120 kVp,自动管电流（预设值30 mA）。所得原始数据采用肺组织算法及软组织算法进行基础能谱数据重建,重建等级FBP、iDose~4-L5（iDose~4level 5）,重建层厚、层间距均为1.0 mm。分别测量并记录FBP组、iDose~4-L5组70 keV VMI和常规120 kVp双肺上野、中野、下野、主气管管腔、皮下脂肪、降主动脉管腔、肌肉的CT值、SD值,并计算图像的SNR、CNR。由两名具有5年以上工作经验的放射诊断医师对各组图像、磨玻璃结节进行主观评估。对比分析70 keV VMI及常规120 kV图像的定性及定量参数。结果 FBP组、iDose~4-L5组,70 keV VMI各肺野、空气、磨玻璃结节、降主动脉管腔、竖脊肌的CT值与120 kVp图像无明显差异（P值均＞0.05）。70 keV VMI双肺上野、中野、下野肺组织的噪声[FBP组:（9.51±3.82） HU、（5.57±2.38） HU、（5.80±3.51） HU;iDose~4-L5组:（7.42±2.84） HU、（4.67±1.68） HU、（4.49±5.19） HU]均低于常规120 kVp[FBP组:（88.74±15.08） HU、（54.67±8.95） HU、（62.35±11.18） HU;iDose~4-L5组:（59.13±11.35） HU、（35.38±7.32） HU、（39.56±8.49） HU],差异具有统计学意义（P值均＜0.05）;70 keV VMI肺窗、磨玻璃结节的主观评分均高于120 kVp图像。纵隔窗,70 keV VMI背景噪声低于常规120 kVp, SNR高于120 kVp图像,差异具有统计学意义（P值均＜0.05）,但CNR与120 kVp无明显差异（P值均＞0.05）,两者的主观评分无明显差异。结论在低剂量条件下,70 keV VMI在不影响肺组织、纵隔软组织CT值准确的前提下,可降低图像噪声,提高图像质量,其中对肺窗质量提高较为显著。

Objectives To explore the effectiveness of magnetic resonance image (MRI)-based biomarkers for identifying benign and malignant parotid tumors via diagnostic model analysis. Methods This retrospective study included 109 patients (development cohort and validation cohort) who underwent MRI preoperatively, including T1- and T2-weighted images. Parameters based on 2D or 3D texture analysis were extracted from tumor lesions by MaZda software, fisher discriminant and bootstrap method were used to perform parameter reduction, diagnostic models with the selected biomarkers were established along with clinical data, model performance (discrimination and calibration) was furtherly evaluated by internal and external validation, decision curve analysis was applied to measure the improvement of clinical benefits. Results S(5,5) Entrop, S(0,1) ASM, WavEnHH (s-4), S(1,1,0) Entropy and Perc.10% were significantly associated with the pathological diagnosis of parotid tumor (benign versus malignancy), when adding these biomarkers to the regression analysis, model performance significantly improved in the development cohort (likelihood-ratio-test; p < 0.05, with an increase of AUC from 0.72 (reference model) to 0.85), and these results were maintained in a small external validation cohort. Decision curve analysis indicated that clinical benefit was greater with the application of MRI-based biomarkers. Conclusions MRI-based texture analysis is proven to be an effective tool in differentiating benign and malignant parotid tumors, preoperative diagnosis was improved with the selected biomarkers compared to the reference model.

Objective To summarize the clinical characteristics and chest CT findings of coronavirus disease 2019(COVID-19)patients in Peking Union Medical College Hospital(PUMCH). Methods A total of 13 patients with COVID-19 confirmed at PUMCH from January 20 to February 6,2020 were selected as the research subjects.Their epidemiological histories,clinical characteristics,laboratory tests,and chest CT findings were analyzed retrospectively.The location,distribution,density,and other accompanying signs of abnormal lung CT lesions were recorded,and the clinical types of these patients were assessed. Results The clinical type was "common type" in all these 13 patients aged(46.8±14.7)years(range:27-68 years).Ten patients had a travel history to Wuhan or direct contact with patients from Wuhan,2 cases had recent travel histories,and 1 case had a travel history to Beijing suburb.The white blood cell(WBC)count was normal or decreased in 92.3% of the patients and the lymphocyte count decreased in 15.4% of the patients.Twelve patients(92.3%)had a fever,among whom 11 patients were admitted due to fever and 2 patients(15.4%)had low fever.Eight patients(61.5%)had dry cough.The CT findings in these 13 patients were all abnormal.The lesions were mainly distributed along the bronchi and under the pleura.The lesions were relatively limited in 8 patients(affecting 1-3 lobes,predominantly in the right or left lower lobe),and diffuse multiple lesions of bilateral lungs were seen in 5 patients.The CT findings mainly included ground glass opacities(GGOs)(n=10,76.9%),focal consolidation within GGOs(n=7,53.8%),thickened vascular bundle passing through the lesions(n=10,76.9%),bronchial wall thickening(n=12,92.3%),air bronchogram(n=10,76.9%),vacuole signs in the lesions(n=7,53.8%),fine reticulation and interlobular septal thickening(n=3,23.1%),reversed halo-sign(n=2,15.4%),crazy-paving pattern(n=2,15.4%),and pleural effusion(n=2,15.4%).Conclusions Most of our patients diagnosed with COVID-19 at PUMCH had a travel history to Wuhan or direct contact with patients from Wuhan.The first symptoms of COVID-19 mainly include fever and dry cough,along with normal or reduced counts of WBC and lymphocytes.CT may reveal that the lesions distribute along the bronchi and under the pleura;they are typically localized GGOs in the early stage but can become multiple GGOs and infiltrative consolidation in both lungs in the advanced stage.Scattered vacuole signs may be visible inside the lesions in some patients.