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Institute of Geophysics, China Earthquake Administration
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Editorial Office of Computerized Tomography Theory and Application
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- Best Articles of CT Theory and Application in 2021
- Outstanding Editors and Outstanding Reviewers of CT Theory and Application in 2022
- Editor ZHAO Xing and His Team Won the First Place in AAPM 2022 "Deep Learning Spectral CT" Contest
- Outstanding Editors of CT Theory and Application 2020-2021
- Editor WANG Zhenchang, Editor ZHANG Li and Editor XING Yuxiang Won the Second Prize of "National Science and Technology Progress Award"
Removal of Gridline Artifacts in Cone-beam CT Based on Wavelet Transform
In cone-beam computed tomography with flat-panel imagers, scattered X-ray photons lead to reduced image quality. Insertion of an anti-scatter grid between the patient or object and the flat-panel imagers is one of the most used techniques for reducing scattered radiation. However, while the scatter is reduced, gridline artifacts can be visible. Suppressing gridline artifacts in cone-beam computed tomography is significant. In this paper, the existing methods of removing gridline artifacts are summarized. Moreover, the wavelet transform method based on spectrum coalescence is innovatively proposed for removing gridline artifacts. Wavelet transform can remove gridline artifacts effectively and without introducing wavelet transform harmonics thus reducing the loss of object details. The effectiveness of this method is verified by experiments.
Research on the Edge Feature Enhancement of Fluvial Reservoirs Based on Image Processing
The identification of channel edge is the key aspect of fine description of fluvial reservoirs. Affected by the factors such as channel overlaying and crossing, thin sand body thickness, low seismic signal-to-noise ratio, and low resolution, the traditional slice interpretation and coherence technology can barely meet the requirements of fine exploration, and the newly developed edge detection based on operator processing still has application misconceptions. In this study, the geometric characteristics of river edges are analyzed as the entry point, and the physical meanings of first derivatives, module values, and second derivatives are clarified. Three-dimensional channel models with different velocity characteristics are established by extracting the coherence attributes of the model and real data; the existing problems of this technique in the fine description of channels are identified. To solve this problem, considering the Sobel operator as an example, the symbolic characteristics of the channel edge after using this technology are illustrated. The coherent enhancement technique for channel edge identification using histogram equalization and the fuzzy set theory is proposed, and good application results are obtained. This method can be used as reference to deepen the understanding of channel edge characteristics and improve the ability to identify fluvial reservoirs.
In Vivo Study of the Influence of CT Acquisition and Reconstruction Parameters on Chest CT Number
Objective: To explore the influence of different CT acquisition and reconstruction parameters on the CT number of the chest in vivo. Methods: The CT number of the trachea, blood vessels, lungs, vertebral bodies, and muscles of the human chest were measured under different CT scanning parameters. Six groups of different scanning parameters and reconstruction algorithms were set respectively: slice thickness 5 mm, 50% multi-model adaptive statistical iterative reconstruction Veo (ASIR-V) and low-dose for S1; slice thickness 5 mm, filtered back projection (FBP) and standard-dose for S2; slice thickness 1.25 mm, 50% ASIR-V and low-dose for S3; slice thickness 1.25 mm, 50% ASIR-V and standard-dose for S4; slice thickness 1.25 mm, FBP, low-dose for S5; slice thickness 1.25 mm, FBP, standard-dose for S6. The radiation dose of the scan was controlled using two noise indexes (NI), including low-dose (NI=40) and standard-dose (NI=10). Differences in CT number between two groups were compared using t-test or rank-sum test. Results: Significant differences of CT number of the trachea were detected between low-dose and standard-dose, but no significant differences of CT number of other tissues were detected between low-dose and standard-dose. No significant differences of CT number of chest tissues were detected between either 5 mm thickness and 1.25 mm thickness or 50% ASIR-V and FBP. Conclusion: The CT number of human chest tissues showed well stability which was scarcely influenced by slices thickness, reconstruction algorithm and scan dose.
Effect of Computed Tomography Window Technique on the Results of Artificial Intelligence Classification of Lung Lesions
Objective: To use three different 3D CNN algorithms and five different computed tomography (CT) window settings to study the effect on the results of artificial intelligence classification of lung lesions in different CT window techniques. Method: A total of 172 cases of peripheral lung cancer and 185 of focal pneumonia who underwent chest CT were analyzed. Three different 3D CNN algorithms were selected (ResNet, ResNext, and DenseNet) to divide the lesions into two groups. Five different CT window settings, including lung window (1500, 600), mediastinal window (350, 40), custom window 1 (SW1) (1000, 40), and custom window 2 (SW2) (1000, 100), were used retrospectively. We calculated classification accuracy, receiver operating characteristic (ROC) curve, and area under the curve (AUC). The ROC curve was compared in pairs. Results: The average classification accuracy of ResNet was the lowest in the mediastinal window (85.732%; AUC value: 0.871) and the highest in the full window (91.596%; AUC value: 0.946). The average classification accuracy of ResNext was the lowest in the mediastinal window (81.528%; AUC value: 0.814) and the highest in the full window (86.568%; AUC value: 0.882). The average classification accuracy of DenseNet was the lowest in the mediastinal window (87.954%; AUC value: 0.906) and the highest in the SW2 window (93.274%; AUC value: 0.951). Medcalc was used to compare ROC curves under five windows of three 3D CNN. The AUC values between mediastinal window and lung window, mediastinal window and SW1, and mediastinal window and SW2 were statistically significant. Conclusion: There is little difference in the diagnostic efficacy of the three 3D CNN. Different CT window settings have an influence on the results of CNN classification of the lung lesions, and the diagnostic efficiency of the three 3D CNN is the worst under the mediastinal window.
Application of Real Size Film Printing Technology in Costal Cartilage CT Image
Objective: To explore the film printing technology for displaying the true size of costal cartilage on medical film. Methods: The CT images of costal cartilage of 31 patients were selected and processed 14×17 inches (35×43 cm), calculate the main image display area and 14×17 the scale between the actual image display areas on the film shall be made into physical scales of 10 cm and 5 cm in length. Print the film in 2×2. Four images are arranged in the grid, and the transverse or sagittal images of single rib cartilage are printed on a 5 cm physical scale; with 1×1 The grid is a single image layout, and the 3D images of all the front rib arches (including costal cartilage) are printed on a 10 cm physical scale. The film measurement values of six indicators, including the length of the ascending and transverse parts of the right sixth costal cartilage, the width and thickness of the transverse junction of the ascending part, the thickness of the sternal end of the costal cartilage and the rib end of the costal cartilage, were compared with the solid measurement values during the operation, and statistical analysis was made. Results: (1) The printed film was measured, and the 10 cm and 5 cm scales shown on the film were equal to the actual size of the ruler. (2) There was no significant difference between the measured values of six groups of costal cartilage images on film and the measured values of costal cartilage entities during surgery. Conclusion: The printing technology based on DICOM protocol can realize the real size printing of costal cartilage CT image on film. The morphological data of the target tissue obtained by the operator from the film are reliable.
Articles Online First have been peer-reviewed and accepted, which are not yet assigned to volumes /issues, but are citable by Digital Object Identifier (DOI).
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doi: 10.15953/j.ctta.2023.080
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2023, 32(5): 579-585.
doi: 10.15953/j.ctta.2023.033
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Objective: To retrospectively investigate the clinical manifestations and imaging features of heavy novel coronavirus pneumonia (NCP). Materials and methods: Fifty-two patients with positive chest computed tomography (CT) manifestations and a confirmed diagnosis of COVID-19 in the Infection Unit from November 2022 to January 2023 were included in our study. All patients underwent chest thin-section CT examination and had more complete clinical data from 1 to 14 days after onset. Patients were divided into two groups based on the time interval between onset and CT examination (<7 and ≥7 days), and the differences in CT performance characteristics between the two groups were compared. Results: Among the 52 patients with severe COVID-19, the age range was approximately 53 to 97 years, with a median age of 80 years in 4 men and 18 women. Thirty-three patients (63.5%) with underlying diseases were combined, including 2 (3.8%) with pulmonary disease, 6 (11.5%) with diabetes mellitus, 18 (34.6%) with hypertension, 16 (30.8%) with coronary heart disease, and 7 (13.5%) with cerebrovascular disease. The main symptoms of patients included fever in 44 (84.6%), cough in 43 (82.7%), myalgia in 2 (3.8%), sore throat in 19 (36.5%), chest tightness in 9 (17.3%), diarrhea in 2 (3.8%), and poor appetite in 3 (5.8%). The differences in lesion involvement and lesion size were statistically significant between patients grouped at the time interval between onset and CT examination. Conclusions: Severe COVID-19 is more common in older adult males, mostly combined with underlying disease. CT features include more airway involvement in the short term, and bloodway involvement is more common in those with longer onset.
Objective: To retrospectively investigate the clinical manifestations and imaging features of heavy novel coronavirus pneumonia (NCP). Materials and methods: Fifty-two patients with positive chest computed tomography (CT) manifestations and a confirmed diagnosis of COVID-19 in the Infection Unit from November 2022 to January 2023 were included in our study. All patients underwent chest thin-section CT examination and had more complete clinical data from 1 to 14 days after onset. Patients were divided into two groups based on the time interval between onset and CT examination (<7 and ≥7 days), and the differences in CT performance characteristics between the two groups were compared. Results: Among the 52 patients with severe COVID-19, the age range was approximately 53 to 97 years, with a median age of 80 years in 4 men and 18 women. Thirty-three patients (63.5%) with underlying diseases were combined, including 2 (3.8%) with pulmonary disease, 6 (11.5%) with diabetes mellitus, 18 (34.6%) with hypertension, 16 (30.8%) with coronary heart disease, and 7 (13.5%) with cerebrovascular disease. The main symptoms of patients included fever in 44 (84.6%), cough in 43 (82.7%), myalgia in 2 (3.8%), sore throat in 19 (36.5%), chest tightness in 9 (17.3%), diarrhea in 2 (3.8%), and poor appetite in 3 (5.8%). The differences in lesion involvement and lesion size were statistically significant between patients grouped at the time interval between onset and CT examination. Conclusions: Severe COVID-19 is more common in older adult males, mostly combined with underlying disease. CT features include more airway involvement in the short term, and bloodway involvement is more common in those with longer onset.
2023, 32(5): 587-594.
doi: 10.15953/j.ctta.2023.044
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Objective: To analyze the differences of chest computed tomography (CT) inflammatory lesions in patients with coronavirus disease 2019 (COVID-19), which were quantitatively measured based on deep learning, to warn the occurrence of severe cases and improve the understanding of the prognosis of COVID-19. Methods: The chest CT scans of 477 local patients with COVID-19 diagnosed for the first time at Inner Mongolia Autonomous Region People's Hospital were retrospectively analyzed. A total of 276 men and 201 women were divided into group A (not serious) and group B (serious) based on whether their diseases turned serious (severe/critical). Comparison was made between the two groups on the basic CT signs, such as lesion distribution, involved lobe side, number, differences in lesion volume, volume proportion, and density based on deep learning. Results: All 477 patients with COVID-19 had epidemiological history, and no statistical difference was noted in age and gender between the two groups. The volume and proportion of the lesions in the whole lung and each lobe of the lung in group B were higher than those in group A. The lesions in group A were mainly in the lower lobe of the right lung, accounting for 3.32% more than that in other lobes. The lower lobe of the left lung was the next, accounting for 2.08%. The volume of lesions in the upper lobe of the left lung was lower than that in other lobes, accounting for only 0.25%. No lesions were noted in the upper lobe of the right lung, middle lobe of the right lung, and upper lobe of the left lung in part of group A. In group B, the lesions were distributed in both lungs and in each lung lobe. The lower lobes of the right lung and left lung were predominant, accounting for 57.86% and 54.76%, respectively. The volume of the middle lobe of the right lung was 34.73% compared with the other lobes. The main lesions in each group were ground-glass density shadows, and the main lesions in group A were −570 ~ −470 HU density, accounting for 13.89%, followed by −470 ~ −370 HU, accounting for 11.07%. Only 3.22% and 4.75% of solid lesions with densities of 30 ~ 70 HU and −70 ~ 30 HU were found. Most of the lesions in group B were ground-glass density shadows, and the focal densities were mainly −570 ~ −470 HU, −470 ~ −370 HU, and −370 ~ −270 HU, accounting for 13.18%, 12.58%, and 12.52%, respectively. No statistical difference was noted between the proportion of lesions with a density of −570 ~ −470 HU and that of group A; however, the volume and proportion of other lesions with different densities were higher than those of group A, showing a trend that the higher the density of the lesions, the higher the proportion of group B was compared with group A. Conclusion: Larger infection volume, more lesion solid components, and multiple CT signs often indicate more severe lung inflammation, which easily progresses to severe disease. Quantitative measurement of chest CT based on deep learning is helpful for the prognosis assessment of COVID-19 and the early warning of severe outcome.
Objective: To analyze the differences of chest computed tomography (CT) inflammatory lesions in patients with coronavirus disease 2019 (COVID-19), which were quantitatively measured based on deep learning, to warn the occurrence of severe cases and improve the understanding of the prognosis of COVID-19. Methods: The chest CT scans of 477 local patients with COVID-19 diagnosed for the first time at Inner Mongolia Autonomous Region People's Hospital were retrospectively analyzed. A total of 276 men and 201 women were divided into group A (not serious) and group B (serious) based on whether their diseases turned serious (severe/critical). Comparison was made between the two groups on the basic CT signs, such as lesion distribution, involved lobe side, number, differences in lesion volume, volume proportion, and density based on deep learning. Results: All 477 patients with COVID-19 had epidemiological history, and no statistical difference was noted in age and gender between the two groups. The volume and proportion of the lesions in the whole lung and each lobe of the lung in group B were higher than those in group A. The lesions in group A were mainly in the lower lobe of the right lung, accounting for 3.32% more than that in other lobes. The lower lobe of the left lung was the next, accounting for 2.08%. The volume of lesions in the upper lobe of the left lung was lower than that in other lobes, accounting for only 0.25%. No lesions were noted in the upper lobe of the right lung, middle lobe of the right lung, and upper lobe of the left lung in part of group A. In group B, the lesions were distributed in both lungs and in each lung lobe. The lower lobes of the right lung and left lung were predominant, accounting for 57.86% and 54.76%, respectively. The volume of the middle lobe of the right lung was 34.73% compared with the other lobes. The main lesions in each group were ground-glass density shadows, and the main lesions in group A were −570 ~ −470 HU density, accounting for 13.89%, followed by −470 ~ −370 HU, accounting for 11.07%. Only 3.22% and 4.75% of solid lesions with densities of 30 ~ 70 HU and −70 ~ 30 HU were found. Most of the lesions in group B were ground-glass density shadows, and the focal densities were mainly −570 ~ −470 HU, −470 ~ −370 HU, and −370 ~ −270 HU, accounting for 13.18%, 12.58%, and 12.52%, respectively. No statistical difference was noted between the proportion of lesions with a density of −570 ~ −470 HU and that of group A; however, the volume and proportion of other lesions with different densities were higher than those of group A, showing a trend that the higher the density of the lesions, the higher the proportion of group B was compared with group A. Conclusion: Larger infection volume, more lesion solid components, and multiple CT signs often indicate more severe lung inflammation, which easily progresses to severe disease. Quantitative measurement of chest CT based on deep learning is helpful for the prognosis assessment of COVID-19 and the early warning of severe outcome.
2023, 32(5): 595-602.
doi: 10.15953/j.ctta.2023.043
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Objective: To quantitatively analyze and compare the chest computed tomography (CT) imaging features of patients infected with delta and omicron variants of COVID-19 using artificial intelligence (AI). Method: The clinical data of 294 patients infected with the novel coronavirus delta variant diagnosed at the Fourth Hospital of Inner Mongolia Autonomous Region from February 20, 2022 to April 19, 2022 and 222 patients infected with the omicron variant diagnosed at the People's Hospital of Inner Mongolia Autonomous Region from December 1, 2022 to December 30, 2022 were retrospectively analyzed. CT imaging data were analyzed and divided into delta and omicron groups. Quantitative calculation was performed using deductive predictive pulmonary infection auxiliary diagnostic software, and CT imaging signs and quantitative CT data between groups were compared and analyzed. Results: No statistical significance was noted between the delta and omicron groups in imaging signs, such as ground-glass opacity, ground-glass nodule, cord-like lesion, consolidation, paving stone sign, thickened interlobular septum, and thickened vessels in the lesion. The distribution of lesions along the bronchial vascular bundle was more likely in the omicron than in the delta group. The total lung lesion volume, volume proportion, right middle lobe lesion volume, volume proportion, right inferior lobe lesion volume, and volume proportion in the delta group were higher than those in the omicron group. The proportions of lesions in the delta group in −570 ~ −470 HU, −470 ~ −370 HU, −370 ~ −270 HU, and −270 ~ −170 HU volumes were higher than those in the omicron group. Conclusion: In the early stage of COVID-19, the volume of CT lesions in the patients infected with the delta variant was higher than that in the omicron group, and the distribution of lesions in the omicron group was more likely to have atypical distribution along the bronchial vascular bundle than that in the delta group. The volume and volume proportion of the pneumonia-infected area in patients with COVID-19 were quantitatively evaluated using the AI-assisted diagnosis system for COVID-19 to provide objective reference data for patients' condition assessment.
Objective: To quantitatively analyze and compare the chest computed tomography (CT) imaging features of patients infected with delta and omicron variants of COVID-19 using artificial intelligence (AI). Method: The clinical data of 294 patients infected with the novel coronavirus delta variant diagnosed at the Fourth Hospital of Inner Mongolia Autonomous Region from February 20, 2022 to April 19, 2022 and 222 patients infected with the omicron variant diagnosed at the People's Hospital of Inner Mongolia Autonomous Region from December 1, 2022 to December 30, 2022 were retrospectively analyzed. CT imaging data were analyzed and divided into delta and omicron groups. Quantitative calculation was performed using deductive predictive pulmonary infection auxiliary diagnostic software, and CT imaging signs and quantitative CT data between groups were compared and analyzed. Results: No statistical significance was noted between the delta and omicron groups in imaging signs, such as ground-glass opacity, ground-glass nodule, cord-like lesion, consolidation, paving stone sign, thickened interlobular septum, and thickened vessels in the lesion. The distribution of lesions along the bronchial vascular bundle was more likely in the omicron than in the delta group. The total lung lesion volume, volume proportion, right middle lobe lesion volume, volume proportion, right inferior lobe lesion volume, and volume proportion in the delta group were higher than those in the omicron group. The proportions of lesions in the delta group in −570 ~ −470 HU, −470 ~ −370 HU, −370 ~ −270 HU, and −270 ~ −170 HU volumes were higher than those in the omicron group. Conclusion: In the early stage of COVID-19, the volume of CT lesions in the patients infected with the delta variant was higher than that in the omicron group, and the distribution of lesions in the omicron group was more likely to have atypical distribution along the bronchial vascular bundle than that in the delta group. The volume and volume proportion of the pneumonia-infected area in patients with COVID-19 were quantitatively evaluated using the AI-assisted diagnosis system for COVID-19 to provide objective reference data for patients' condition assessment.
2023, 32(5): 603-611.
doi: 10.15953/j.ctta.2023.045
Abstract:
Objective: This study aimed to analyze the chest computed tomography (CT) imaging features of coronavirus disease 2019 (COVID-19) in people of different ages and improve the understanding of the imaging manifestations of COVID-19. Methods: Chest CT data of 476 cases with COVID-19 were retrospectively analyzed, including 275 males and 201 females. The patients were divided into four groups according to different age groups: groups A (0~45 years old) 33, B (45~60 years old) 72, C (60~75 years old) 203, and D (over 75 years old) 168. A comparison was made between the four groups of patients with chest CT lesions involving lobe side, number and density, distribution, and other basic CT signs, as well as differences in lesion volume, volume proportion, and density based on deep learning. Results: All the 476 patients with COVID-19 had an epidemiological history, and there was no statistically significant difference in sex between the groups. The lesions in the lower lobes of both lungs were the most common in the four groups. The lesions in group A were mostly located in the unilateral lung, while those in groups C and D were mostly distributed in both lungs. The volume and proportion of lesions increased with age in each group, and the distribution was mainly in the lower lobe of both lungs. In groups A, C, and D, the right lower lobe was the most common and had the largest volume and proportion, while in group B, the left lower lobe had the largest volume and proportion. Compared with group A, all indexes of group C increased, and the difference was statistically significant; the lesion volume of the right inferior lobe of the lung was statistically significant compared with group B. The volume of lesions in the left upper lobe of the lung in group D was significantly increased compared with that in groups A and B, and the volume and proportion of lesions in the whole lung, upper, middle, and lower lobes of the right lung, and the lower lobe of the left lung in group D were significantly increased compared with that in groups A, B and C, and the difference was statistically significant. In group A, the density of pure ground glass was the most common, followed by the density of mixed ground glass, and the density of solid change was rare. The solid density of lesions in group D was more common, most of which showed mixed ground glass density. The incidence of pure ground glass, mixed ground glass, and solid density lesions was higher in groups B and C than that in groups A and D. The lesion density in each group was mainly ground glass density, and the CT value ranged from −570 to −470 HU and −470 to −370 HU. The lesion volume in each CT value range of group D was higher than that in groups A, B, and C, and the volume proportion was higher than that in group A, and the difference was statistically significant. Conclusion: All patients with COVID-19 in this group have an epidemiological history. Being familiar with chest CT features of people of different ages can make clinical diagnosis and treatment more targeted and provide a reference for COVID-19 disease monitoring and individualized prevention and treatment measures.
Objective: This study aimed to analyze the chest computed tomography (CT) imaging features of coronavirus disease 2019 (COVID-19) in people of different ages and improve the understanding of the imaging manifestations of COVID-19. Methods: Chest CT data of 476 cases with COVID-19 were retrospectively analyzed, including 275 males and 201 females. The patients were divided into four groups according to different age groups: groups A (0~45 years old) 33, B (45~60 years old) 72, C (60~75 years old) 203, and D (over 75 years old) 168. A comparison was made between the four groups of patients with chest CT lesions involving lobe side, number and density, distribution, and other basic CT signs, as well as differences in lesion volume, volume proportion, and density based on deep learning. Results: All the 476 patients with COVID-19 had an epidemiological history, and there was no statistically significant difference in sex between the groups. The lesions in the lower lobes of both lungs were the most common in the four groups. The lesions in group A were mostly located in the unilateral lung, while those in groups C and D were mostly distributed in both lungs. The volume and proportion of lesions increased with age in each group, and the distribution was mainly in the lower lobe of both lungs. In groups A, C, and D, the right lower lobe was the most common and had the largest volume and proportion, while in group B, the left lower lobe had the largest volume and proportion. Compared with group A, all indexes of group C increased, and the difference was statistically significant; the lesion volume of the right inferior lobe of the lung was statistically significant compared with group B. The volume of lesions in the left upper lobe of the lung in group D was significantly increased compared with that in groups A and B, and the volume and proportion of lesions in the whole lung, upper, middle, and lower lobes of the right lung, and the lower lobe of the left lung in group D were significantly increased compared with that in groups A, B and C, and the difference was statistically significant. In group A, the density of pure ground glass was the most common, followed by the density of mixed ground glass, and the density of solid change was rare. The solid density of lesions in group D was more common, most of which showed mixed ground glass density. The incidence of pure ground glass, mixed ground glass, and solid density lesions was higher in groups B and C than that in groups A and D. The lesion density in each group was mainly ground glass density, and the CT value ranged from −570 to −470 HU and −470 to −370 HU. The lesion volume in each CT value range of group D was higher than that in groups A, B, and C, and the volume proportion was higher than that in group A, and the difference was statistically significant. Conclusion: All patients with COVID-19 in this group have an epidemiological history. Being familiar with chest CT features of people of different ages can make clinical diagnosis and treatment more targeted and provide a reference for COVID-19 disease monitoring and individualized prevention and treatment measures.
Chest Computed Tomography Findings of Patients with Severe COVID-19 Complicated with Other Pathogens
2023, 32(5): 613-620.
doi: 10.15953/j.ctta.2023.054
Abstract:
Objective: To describe the characteristics of chest computed tomography (CT) findings of patients with severe COVID-19 complicated with other pathogens. Method: Chest CT data and outcomes of patients with severe COVID-19 complicated with other pathogens were retrospectively analyzed. Results: Twenty-seven patients were included in the study. Etiological examination showed that bacteria were isolated in 13 patients, fungi in 2 patients, and bacteria and fungi in the remaining 12 patients. Multiple lung lesions were found in the chest CT images of all 27 patients. Excluding the chest CT images of 6 patients with typical novel coronavirus pneumonia features, the remaining 21 cases mostly showed scattered or diffuse ground-glass, mixed density, patchy, and patchy-solid shadows distributed in the lung segments or lobes. Some of them were scattered in nodules or central lobular nodules, with the thickened interlobular septum showing "paving stone sign" and "vascular thickening sign" in the ground glass shadow, and air bronchial air images were visible in the solid shadows. Pleural effusion was found in most cases, with pulmonary air sacs in few cases, and mild lymph node enlargement in scattered cases. According to the outcomes, the patients were grouped into 6 patients who survived and 21 patients who died. The proportion of ground glass shadow and ground glass with solid shadow in the lung of the patients who died was higher than that of the patients who survived, and the other imaging findings were not statistically different. Conclusion: Secondary infections in patients with severe COVID-19 were mainly bacterial and fungal infections, with most infections were mixed pathogens. Chest CT images mainly showed ground glass, mixed density shadow, consolidation shadow, and nodular shadow without specific location distribution, and most cases were accompanied with pleural effusion, a few with lung sacs, and scattered cases with mild enlargement of chest lymph nodes, pavement stone sign, and vascular thickening sign. It showed the diverse imaging features of COVID-19 cases complicated with bacterial and fungal infections.
Objective: To describe the characteristics of chest computed tomography (CT) findings of patients with severe COVID-19 complicated with other pathogens. Method: Chest CT data and outcomes of patients with severe COVID-19 complicated with other pathogens were retrospectively analyzed. Results: Twenty-seven patients were included in the study. Etiological examination showed that bacteria were isolated in 13 patients, fungi in 2 patients, and bacteria and fungi in the remaining 12 patients. Multiple lung lesions were found in the chest CT images of all 27 patients. Excluding the chest CT images of 6 patients with typical novel coronavirus pneumonia features, the remaining 21 cases mostly showed scattered or diffuse ground-glass, mixed density, patchy, and patchy-solid shadows distributed in the lung segments or lobes. Some of them were scattered in nodules or central lobular nodules, with the thickened interlobular septum showing "paving stone sign" and "vascular thickening sign" in the ground glass shadow, and air bronchial air images were visible in the solid shadows. Pleural effusion was found in most cases, with pulmonary air sacs in few cases, and mild lymph node enlargement in scattered cases. According to the outcomes, the patients were grouped into 6 patients who survived and 21 patients who died. The proportion of ground glass shadow and ground glass with solid shadow in the lung of the patients who died was higher than that of the patients who survived, and the other imaging findings were not statistically different. Conclusion: Secondary infections in patients with severe COVID-19 were mainly bacterial and fungal infections, with most infections were mixed pathogens. Chest CT images mainly showed ground glass, mixed density shadow, consolidation shadow, and nodular shadow without specific location distribution, and most cases were accompanied with pleural effusion, a few with lung sacs, and scattered cases with mild enlargement of chest lymph nodes, pavement stone sign, and vascular thickening sign. It showed the diverse imaging features of COVID-19 cases complicated with bacterial and fungal infections.
2023, 32(5): 621-626.
doi: 10.15953/j.ctta.2023.048
Abstract:
Objective: To analyze the differences in chest computed tomography (CT) findings in patients infected with Omicron strain BF.7 of coronavirus disease 2019 (COVID-19) with different clinical outcomes, and to improve the understanding of COVID-19 imaging. Methods: The features of chest CT images from 126 patients infected with Omicron BF.7 strain at the People's Hospital of Inner Mongolia Autonomous Region were retrospectively analyzed, and divided into ‘group A’ (not serious) and ‘group B’ (serious) according to whether they progressed to critically ill patients. There were 103 cases in group A, including 65 males and 38 females, with an average age of (73.98±11.53) years. There were 23 patients in group B, including 16 males and 7 females, with an average age of (73.43±12.53) years old. The differences in age, gender, and chest CT lesion distribution, density, and lung lobe involvement were compared between the two groups. Results: All 126 COVID-19 patients had an epidemiological history, and there was no statistical significance in age and sexes between the two groups. The volume proportion of lesions in the upper and lower lobes of the left lung, the upper, middle, and lower lobes of the right lung, and both lungs in group B was higher than that in group A. The lesions were primarily ground glass shadow and consolidation, and the range was larger than group A. Conclusion: The age and chest CT findings of patients who developed severe COVID-19 are different from those who do not. The analysis of imaging characteristics can provide reference for clinical diagnosis, treatment, and prognostic assessment.
Objective: To analyze the differences in chest computed tomography (CT) findings in patients infected with Omicron strain BF.7 of coronavirus disease 2019 (COVID-19) with different clinical outcomes, and to improve the understanding of COVID-19 imaging. Methods: The features of chest CT images from 126 patients infected with Omicron BF.7 strain at the People's Hospital of Inner Mongolia Autonomous Region were retrospectively analyzed, and divided into ‘group A’ (not serious) and ‘group B’ (serious) according to whether they progressed to critically ill patients. There were 103 cases in group A, including 65 males and 38 females, with an average age of (73.98±11.53) years. There were 23 patients in group B, including 16 males and 7 females, with an average age of (73.43±12.53) years old. The differences in age, gender, and chest CT lesion distribution, density, and lung lobe involvement were compared between the two groups. Results: All 126 COVID-19 patients had an epidemiological history, and there was no statistical significance in age and sexes between the two groups. The volume proportion of lesions in the upper and lower lobes of the left lung, the upper, middle, and lower lobes of the right lung, and both lungs in group B was higher than that in group A. The lesions were primarily ground glass shadow and consolidation, and the range was larger than group A. Conclusion: The age and chest CT findings of patients who developed severe COVID-19 are different from those who do not. The analysis of imaging characteristics can provide reference for clinical diagnosis, treatment, and prognostic assessment.
2023, 32(5): 627-635.
doi: 10.15953/j.ctta.2023.059
Abstract:
Objective: To analyze and compare chest computed tomography (CT) findings and evolutionary characteristics of different strains of novel corona virus pneumonia and to explore the correlation of CT findings and strain characteristics with clinical outcomes. Methods: Chest CT images of 75 cases of the original strain, 130 cases of the Delta variant, and 562 cases of the Omicron variant from the Inner Mongolia Autonomous Region, were collected and sorted. The CT manifestations and their changes for different strains were analyzed and compared. Results: The proportion of patients with mild disease in the Omicron variant group (499 cases, 88.79%) was significantly higher than that in the original strain (9 cases, 12.00%) and Delta variant groups (47 cases, 36.15%). Compared to the original strain group, the Delta variant group showed higher incidences of mild cases% (47 cases, 36.15% vs. 9 cases, 12.00%) and lower incidences of severe cases (14 cases, 16.87% vs. 19 cases, 28.79%). A total of 96.97% (64 cases) of the original strain group, 93.98% (78 cases) of the Delta variant group, and 98.41% (62 cases) of the Omicron variant group showed ground-glass opacities, which were the main manifestations on the first CT scan. There was no statistically significant difference among the three groups. In terms of morphology and distribution of ground-glass opacity, 12 cases (19.05%) of the Omicron group showed acinular nodule ground-glass opacity, which was significantly higher than that shown by the original strain group (2 cases, 3.03%) and the Delta variant group (3 cases, 3.61%). The lesions in the three groups were mainly distributed along the subpleural lung regions. However, the Omicron variant group had a higher distribution ratio along the bronchial vascular bundle than the original strain and Delta variant groups. In terms of concomitant signs, concomitant consolidation and cable proportion were significantly lower on the first CT image. The proportions of concurrent consolidation in the original strain, Delta variant, and Omicron variant groups were 3.03% (2 cases), 6.02% (5 cases), and 5.00% (1 case), respectively. The proportions of accompanying cables in the original strain, Delta variant, and Omicron variant groups were 12.12% (8 cases), 15.66% (13 cases), and 20.00% (4 cases), respectively. The imaging findings of the lesions in the original strain and Delta variant groups changed over the course of the disease. In the original strain group, 39.39% (26 cases) had realistic changes based on the original ground-glass opacity and 53.03% (35 cases) had a cord based on the original ground-glass opacity. This proportion was significantly higher than the proportion of consolidation and cord based on the first CT. In the Delta variant group, 44.58% (37 cases) of patients showed inflammatory consolidation based on the original ground-glass opacity and 61.45% (51 cases) of patients showed a cord based on the original ground-glass opacity. In the Omicron variant group, 34.38% (11 cases) had inflammatory consolidation based on the original ground-glass opacity and 71.88% (23 cases) had cord based on the original ground-glass opacity, both of which were significantly higher than the proportions of primary inflammatory consolidation and cord. The median number of days from apparent absorption to onset in the original strain, Delta variant and Omicron variant groups were 16 days, 16 days, and 9 days, respectively. Conclusions: The dynamic changes in chest CT findings of cases infected with different strains of COVID-19 can reflect the evolution of lesions with the clinical course of the disease. This prediction has clinical application value in determining the course of COVID-19 and disease management.
Objective: To analyze and compare chest computed tomography (CT) findings and evolutionary characteristics of different strains of novel corona virus pneumonia and to explore the correlation of CT findings and strain characteristics with clinical outcomes. Methods: Chest CT images of 75 cases of the original strain, 130 cases of the Delta variant, and 562 cases of the Omicron variant from the Inner Mongolia Autonomous Region, were collected and sorted. The CT manifestations and their changes for different strains were analyzed and compared. Results: The proportion of patients with mild disease in the Omicron variant group (499 cases, 88.79%) was significantly higher than that in the original strain (9 cases, 12.00%) and Delta variant groups (47 cases, 36.15%). Compared to the original strain group, the Delta variant group showed higher incidences of mild cases% (47 cases, 36.15% vs. 9 cases, 12.00%) and lower incidences of severe cases (14 cases, 16.87% vs. 19 cases, 28.79%). A total of 96.97% (64 cases) of the original strain group, 93.98% (78 cases) of the Delta variant group, and 98.41% (62 cases) of the Omicron variant group showed ground-glass opacities, which were the main manifestations on the first CT scan. There was no statistically significant difference among the three groups. In terms of morphology and distribution of ground-glass opacity, 12 cases (19.05%) of the Omicron group showed acinular nodule ground-glass opacity, which was significantly higher than that shown by the original strain group (2 cases, 3.03%) and the Delta variant group (3 cases, 3.61%). The lesions in the three groups were mainly distributed along the subpleural lung regions. However, the Omicron variant group had a higher distribution ratio along the bronchial vascular bundle than the original strain and Delta variant groups. In terms of concomitant signs, concomitant consolidation and cable proportion were significantly lower on the first CT image. The proportions of concurrent consolidation in the original strain, Delta variant, and Omicron variant groups were 3.03% (2 cases), 6.02% (5 cases), and 5.00% (1 case), respectively. The proportions of accompanying cables in the original strain, Delta variant, and Omicron variant groups were 12.12% (8 cases), 15.66% (13 cases), and 20.00% (4 cases), respectively. The imaging findings of the lesions in the original strain and Delta variant groups changed over the course of the disease. In the original strain group, 39.39% (26 cases) had realistic changes based on the original ground-glass opacity and 53.03% (35 cases) had a cord based on the original ground-glass opacity. This proportion was significantly higher than the proportion of consolidation and cord based on the first CT. In the Delta variant group, 44.58% (37 cases) of patients showed inflammatory consolidation based on the original ground-glass opacity and 61.45% (51 cases) of patients showed a cord based on the original ground-glass opacity. In the Omicron variant group, 34.38% (11 cases) had inflammatory consolidation based on the original ground-glass opacity and 71.88% (23 cases) had cord based on the original ground-glass opacity, both of which were significantly higher than the proportions of primary inflammatory consolidation and cord. The median number of days from apparent absorption to onset in the original strain, Delta variant and Omicron variant groups were 16 days, 16 days, and 9 days, respectively. Conclusions: The dynamic changes in chest CT findings of cases infected with different strains of COVID-19 can reflect the evolution of lesions with the clinical course of the disease. This prediction has clinical application value in determining the course of COVID-19 and disease management.
2023, 32(5): 636-644.
doi: 10.15953/j.ctta.2023.149
Abstract:
Objective: Objective: To explore and analyze the clinical features and chest thin-slice non-enhanced computed tomography (CT) imaging features of patients with coronavirus disease 2019 (COVID-19) at initial diagnosis in a fever clinic. Methods: A retrospective analysis was performed on 140 patients with COVID-19 at initial diagnosis in a fever clinic, including 101 and 39 cases in the moderate and severe and critical groups , respective-ly. Baseline, clinical characteristics, complete blood count + C-reactive protein (CBC+CRP), and chest thin-slice non-enhanced CT imaging characteristics of the patients were analyzed. Results: (1) The comparison between the moderate and severe and criti-cal groups showed that there were statistically significant differences in age (66.05±14.38 vs. 77.90±13.12,), course of initial diagnosis (5.40±3.81 vs. 3.97±3.12), SPO2 (97.88±1.73 vs. 92.92±4.01), and Tmax (38.32±0.66 vs. 38.68±0.63).(2) CT features between the two groups showed statistically significant differences in semi-quantitative volume (18.85±13.51 vs. 34.41±19.34). (3) The comparison between the moderate and severe and critical groups showed that there were statistically significant differ-ences in CRP (29.42±26.93 vs. 80.67±48.01), LYM (1.64±0.68 vs. 0.95±0.64), and NLR (3.48±2.46 vs. 9.36±10.42). (4) Six indicators, namely age, the course of initial diagnosis, SPO2, semi-quantitative volume, CRP, and LYM, were screened for multivariate logistic regression analysis. The result show that age (OR=1.090, 95%CI 1.006 ~ 1.181), semi-quantitative (OR=1.086, 95%CI 1.086 ~ 1.019), and SPO2 (OR=0.261, 95%CI 0.089 ~ 0.762), are related to the occurrence of severe and critical COVID-19 infection, and the difference is statistically significant; CRP (OR=1.054, 95%CI 1.023 ~ 1.087) and LYM (OR=0.039, 95%CI 0.04 ~ 0.391) are related to the occurrence of severe and critical COVID-19 infection, and the difference is significant statistically significant. Conclusion: Age, lower SPO2 and LYM, a shorter course; a higher Tmax, semi-quantitative volume, CRP, and NLR are associ-ated with severe and critical cases and required early identification.
Objective: Objective: To explore and analyze the clinical features and chest thin-slice non-enhanced computed tomography (CT) imaging features of patients with coronavirus disease 2019 (COVID-19) at initial diagnosis in a fever clinic. Methods: A retrospective analysis was performed on 140 patients with COVID-19 at initial diagnosis in a fever clinic, including 101 and 39 cases in the moderate and severe and critical groups , respective-ly. Baseline, clinical characteristics, complete blood count + C-reactive protein (CBC+CRP), and chest thin-slice non-enhanced CT imaging characteristics of the patients were analyzed. Results: (1) The comparison between the moderate and severe and criti-cal groups showed that there were statistically significant differences in age (66.05±14.38 vs. 77.90±13.12,), course of initial diagnosis (5.40±3.81 vs. 3.97±3.12), SPO2 (97.88±1.73 vs. 92.92±4.01), and Tmax (38.32±0.66 vs. 38.68±0.63).(2) CT features between the two groups showed statistically significant differences in semi-quantitative volume (18.85±13.51 vs. 34.41±19.34). (3) The comparison between the moderate and severe and critical groups showed that there were statistically significant differ-ences in CRP (29.42±26.93 vs. 80.67±48.01), LYM (1.64±0.68 vs. 0.95±0.64), and NLR (3.48±2.46 vs. 9.36±10.42). (4) Six indicators, namely age, the course of initial diagnosis, SPO2, semi-quantitative volume, CRP, and LYM, were screened for multivariate logistic regression analysis. The result show that age (OR=1.090, 95%CI 1.006 ~ 1.181), semi-quantitative (OR=1.086, 95%CI 1.086 ~ 1.019), and SPO2 (OR=0.261, 95%CI 0.089 ~ 0.762), are related to the occurrence of severe and critical COVID-19 infection, and the difference is statistically significant; CRP (OR=1.054, 95%CI 1.023 ~ 1.087) and LYM (OR=0.039, 95%CI 0.04 ~ 0.391) are related to the occurrence of severe and critical COVID-19 infection, and the difference is significant statistically significant. Conclusion: Age, lower SPO2 and LYM, a shorter course; a higher Tmax, semi-quantitative volume, CRP, and NLR are associ-ated with severe and critical cases and required early identification.
2023, 32(5): 645-651.
doi: 10.15953/j.ctta.2023.034
Abstract:
Objective: To retrospectively analyze the clinical value of chest plain computed tomography (CT) for the initial diagnosis and dynamic changes of early novel coronavirus pneumonia (2019 novel Coronavirus, 2019-nCoV, referred to as new coronavirus pneumonia). Materials and methods: Fifty-two patients with confirmed new coronavirus pneumonia diagnoses and positive chest CT manifestations from November 12, 2022, to January 6, 2023, in the infection department of our hospital were collected. All patients had two chest thin-section CT examinations within 1 month from the onset of the disease and had complete clinical data. Patients were divided into two groups according to their age (60 years and >60 years), and the differences in CT performance characteristics between the two groups were compared. The CT review of all patients was also observed. Results: Among the 52 patients, 52 involved the lungs (100%), 24 involved the airways (46.2%), and 21 involved the bloodways (40.4%). Comparison between age groups showed statistically significant differences in lesion location (single/both lungs, airways), tree-in-bud pattern, large morphology, fibrous striae, interstitial changes, and pleural thickening. Among the 52 patients, review CT showed lesion progression in 18 cases (34.6%), which showed an increase in extent in 18 cases (100%), aggravation of solid changes in 7 (38.9), aggravation of GGO in 14 (77.8%), and increase in pleural effusion in 6 (33.3%); review CT showed lesion remission in 18 cases (34.6%), which showed a decrease in extent in 31 (91.2%), 6 (17.6%) with reduced density, 12 (35.3%) with fibrosis, 2 (5.9%) with complete resorption, and 4 (11.8) with reduced pleural effusion. Conclusion: The CT scan of the chest in neocoronary pneumonia has certain characteristics, and for the first time, it mostly showed multiple patchy signs or large patchy ground glass opacity (GGO) with mainly subpleural distribution in the periphery of both lungs, mostly accompanied by "halo sign," "anti-halo sign," and "paving stone sign." The lung is more susceptible to change following treatment. After treatment, the lung lesions change rapidly, with most patients showing absorption and shrinkage, density fading, or fibrosis and a few patients showing increased extent, solidity or aggravation of GGO, and pleural effusion. Chest plain CT helps clinicians in the early diagnosis and dynamic evaluation of neocoronary pneumonia.
Objective: To retrospectively analyze the clinical value of chest plain computed tomography (CT) for the initial diagnosis and dynamic changes of early novel coronavirus pneumonia (2019 novel Coronavirus, 2019-nCoV, referred to as new coronavirus pneumonia). Materials and methods: Fifty-two patients with confirmed new coronavirus pneumonia diagnoses and positive chest CT manifestations from November 12, 2022, to January 6, 2023, in the infection department of our hospital were collected. All patients had two chest thin-section CT examinations within 1 month from the onset of the disease and had complete clinical data. Patients were divided into two groups according to their age (60 years and >60 years), and the differences in CT performance characteristics between the two groups were compared. The CT review of all patients was also observed. Results: Among the 52 patients, 52 involved the lungs (100%), 24 involved the airways (46.2%), and 21 involved the bloodways (40.4%). Comparison between age groups showed statistically significant differences in lesion location (single/both lungs, airways), tree-in-bud pattern, large morphology, fibrous striae, interstitial changes, and pleural thickening. Among the 52 patients, review CT showed lesion progression in 18 cases (34.6%), which showed an increase in extent in 18 cases (100%), aggravation of solid changes in 7 (38.9), aggravation of GGO in 14 (77.8%), and increase in pleural effusion in 6 (33.3%); review CT showed lesion remission in 18 cases (34.6%), which showed a decrease in extent in 31 (91.2%), 6 (17.6%) with reduced density, 12 (35.3%) with fibrosis, 2 (5.9%) with complete resorption, and 4 (11.8) with reduced pleural effusion. Conclusion: The CT scan of the chest in neocoronary pneumonia has certain characteristics, and for the first time, it mostly showed multiple patchy signs or large patchy ground glass opacity (GGO) with mainly subpleural distribution in the periphery of both lungs, mostly accompanied by "halo sign," "anti-halo sign," and "paving stone sign." The lung is more susceptible to change following treatment. After treatment, the lung lesions change rapidly, with most patients showing absorption and shrinkage, density fading, or fibrosis and a few patients showing increased extent, solidity or aggravation of GGO, and pleural effusion. Chest plain CT helps clinicians in the early diagnosis and dynamic evaluation of neocoronary pneumonia.
2023, 32(5): 652-658.
doi: 10.15953/j.ctta.2023.030
Abstract:
Objective: To explore the imaging characteristics of patients with novel coronavirus pneumonia (COVID-19) combined with different underlying diseases. Materials and methods: COVID-19 was diagnosed in 153 patients at Beijing Shijitan Hospital, Capital Medical University, from November 16, 2022 to December 16, 2022, and data were retrospectively collected. All patients underwent chest CT scan from 1 to 14 days after onset and were divided into two groups based on the presence or absence of underlying diseases. Forty-three patients had underlying diseases, and 110 patients had none. We compared the differences between the two groups. Result: The comparison between the two groups showed statistically significant differences in age, cough, bilateral lung distribution, diffuse distribution, honeycomb-like changes in the lungs, patchy distribution, large patchy distribution, band distribution, crazy-paving sign, air bronchogram sign, traction bronchiectasis, and pleural effusion. Conclusion: Fever and cough are the most common clinical symptoms in patients with COVID-19. Chest CT showed multiple lesions in both lungs. The most common types of lesions were thickening of bronchovascular bundle and GGO. Patients with underlying diseases had more honeycomb-like changes, crazy-paving sign, air bronchogram sign, traction bronchiectasis, and pleural effusion than those without underlying diseases. Chest thin-slice CT scan provides a key reference for the early detection and diagnosis of the disease.
Objective: To explore the imaging characteristics of patients with novel coronavirus pneumonia (COVID-19) combined with different underlying diseases. Materials and methods: COVID-19 was diagnosed in 153 patients at Beijing Shijitan Hospital, Capital Medical University, from November 16, 2022 to December 16, 2022, and data were retrospectively collected. All patients underwent chest CT scan from 1 to 14 days after onset and were divided into two groups based on the presence or absence of underlying diseases. Forty-three patients had underlying diseases, and 110 patients had none. We compared the differences between the two groups. Result: The comparison between the two groups showed statistically significant differences in age, cough, bilateral lung distribution, diffuse distribution, honeycomb-like changes in the lungs, patchy distribution, large patchy distribution, band distribution, crazy-paving sign, air bronchogram sign, traction bronchiectasis, and pleural effusion. Conclusion: Fever and cough are the most common clinical symptoms in patients with COVID-19. Chest CT showed multiple lesions in both lungs. The most common types of lesions were thickening of bronchovascular bundle and GGO. Patients with underlying diseases had more honeycomb-like changes, crazy-paving sign, air bronchogram sign, traction bronchiectasis, and pleural effusion than those without underlying diseases. Chest thin-slice CT scan provides a key reference for the early detection and diagnosis of the disease.
2023, 32(5): 659-665.
doi: 10.15953/j.ctta.2023.031
Abstract:
Objective: To explore the characteristics of high-resolution computed tomography (HRCT) in diabetes complicated with coronavirus disease 2019 (COVID-19)-associated pneumonia. Materials and Methods: This study included 584 patients (359 males and 225 females), aged between 60~99 years old (mean, (76±9) years), with positive chest computed tomography (CT) findings and diagnosed with COVID-19 in our hospital from December 14, 2022, to January 10, 2023. Of these, 225 patients were diabetic and 359 were non-diabetic. The features of the chest HRCT from patients with diabetes mellitus complicated with COVID-19 and those without diabetes mellitus complicated with COVID-19 were compared. Moreover, 363 patients in the acute stage of COVID-19 (defined as the time interval between onset and CT examination <7 days) were selected for subgroup analysis, and the HRCT characteristics of COVID-19 between the diabetes group and the non-diabetic group in the acute stage. Results: The location, distribution, morphology, and concomitant signs of pulmonary lesions between the two groups of patients with COVID-19 did not differ significantly. Conversely, statistically significant differences in density (fine mesh, uneven density) and lesion margin (fuzzy lesion margin) were detected. In particular, the grid, uneven, and fuzzy signs on lung imaging were significantly higher in the non-diabetic group than that in the diabetic group. Additionally, 54 patients (24%) in the diabetic group and 127 patients (35.38%) in the non-diabetic group demonstrated fine mesh shadows. There were 181 patients (80.44%) in the diabetic group and 313 patients (87.19%) in the non-diabetic group with uneven density. Furthermore, 205 patients (91.11%) in the diabetic group and 344 patients (95.82%) in the non-diabetic group had blurred edges. There was significantly less pulmonary grid shadowing in the acute subgroup with diabetes (35, 24.65%) than in the acute subgroup without diabetes (82, 37.10%). Conclusion: The features of chest HRCT in patients with diabetes mellitus and COVID-19 are mainly exudation, uniform density, and a clear edge, while the interstitial changes are not obvious compared with patients in the non-diabetic group.
Objective: To explore the characteristics of high-resolution computed tomography (HRCT) in diabetes complicated with coronavirus disease 2019 (COVID-19)-associated pneumonia. Materials and Methods: This study included 584 patients (359 males and 225 females), aged between 60~99 years old (mean, (76±9) years), with positive chest computed tomography (CT) findings and diagnosed with COVID-19 in our hospital from December 14, 2022, to January 10, 2023. Of these, 225 patients were diabetic and 359 were non-diabetic. The features of the chest HRCT from patients with diabetes mellitus complicated with COVID-19 and those without diabetes mellitus complicated with COVID-19 were compared. Moreover, 363 patients in the acute stage of COVID-19 (defined as the time interval between onset and CT examination <7 days) were selected for subgroup analysis, and the HRCT characteristics of COVID-19 between the diabetes group and the non-diabetic group in the acute stage. Results: The location, distribution, morphology, and concomitant signs of pulmonary lesions between the two groups of patients with COVID-19 did not differ significantly. Conversely, statistically significant differences in density (fine mesh, uneven density) and lesion margin (fuzzy lesion margin) were detected. In particular, the grid, uneven, and fuzzy signs on lung imaging were significantly higher in the non-diabetic group than that in the diabetic group. Additionally, 54 patients (24%) in the diabetic group and 127 patients (35.38%) in the non-diabetic group demonstrated fine mesh shadows. There were 181 patients (80.44%) in the diabetic group and 313 patients (87.19%) in the non-diabetic group with uneven density. Furthermore, 205 patients (91.11%) in the diabetic group and 344 patients (95.82%) in the non-diabetic group had blurred edges. There was significantly less pulmonary grid shadowing in the acute subgroup with diabetes (35, 24.65%) than in the acute subgroup without diabetes (82, 37.10%). Conclusion: The features of chest HRCT in patients with diabetes mellitus and COVID-19 are mainly exudation, uniform density, and a clear edge, while the interstitial changes are not obvious compared with patients in the non-diabetic group.
2023, 32(5): 667-674.
doi: 10.15953/j.ctta.2023.026
Abstract:
Objective: This study aimed to explore the clinical value of thin slice computed tomography (CT) plain scan in the analysis of CT features of vascular abnormalities associated with coronavirus disease 2019 (COVID-19). Materials and methods: A total of 73 patients with COVID-19 confirmed by the Department of Infection of Beijing Shijitan Hospital from December 5, 2022 to December 17, 2022, were included in the study. Chest thin CT plain scan images showed that the lesions involved blood vessels were retrospectively collected. All patients had complete chest thin CT plain scan and relatively complete clinical data. According to age (>60 and ≤60 years), the patients were divided into the young and elderly groups. The chest imaging manifestations of all patients were observed and statistically analyzed between different age groups. Results: Among the 73 patients with COVID-19, the imaging indexes with statistical significance between the young and elderly groups were as follows: the distribution of the lesion around the central blood vessel, size of the lesion (10~30 mm), size of the lesion (>30 mm), percentage of the lesion to the volume of the lung lobe (≤30), percentage of the lesion to the volume of the lung lobe (>50) (white lung), shape of the lesion was large, the dominant type of the lesion was acinar, vascular distortion, vascular margin fuzzy, and tree-bud sign thick fiber rope. Conclusion: (1) Chest thin-slice CT plain scan can identify the number, location, involved location, scope, vascular abnormality, and pathological type of COVID-19-related vascular abnormality, which has certain significance for the qualitative and differential diagnosis of COVID-19 vascular abnormality. (2) The chest thin CT plain scan is of great significance for finding elderly patients with COVID-19 involving blood vessels. (3) COVID-19-related "blood vessel thickening" can be caused by either the diameter of the blood vessel itself or the inflammatory edema of the perivascular interstitium.
Objective: This study aimed to explore the clinical value of thin slice computed tomography (CT) plain scan in the analysis of CT features of vascular abnormalities associated with coronavirus disease 2019 (COVID-19). Materials and methods: A total of 73 patients with COVID-19 confirmed by the Department of Infection of Beijing Shijitan Hospital from December 5, 2022 to December 17, 2022, were included in the study. Chest thin CT plain scan images showed that the lesions involved blood vessels were retrospectively collected. All patients had complete chest thin CT plain scan and relatively complete clinical data. According to age (>60 and ≤60 years), the patients were divided into the young and elderly groups. The chest imaging manifestations of all patients were observed and statistically analyzed between different age groups. Results: Among the 73 patients with COVID-19, the imaging indexes with statistical significance between the young and elderly groups were as follows: the distribution of the lesion around the central blood vessel, size of the lesion (10~30 mm), size of the lesion (>30 mm), percentage of the lesion to the volume of the lung lobe (≤30), percentage of the lesion to the volume of the lung lobe (>50) (white lung), shape of the lesion was large, the dominant type of the lesion was acinar, vascular distortion, vascular margin fuzzy, and tree-bud sign thick fiber rope. Conclusion: (1) Chest thin-slice CT plain scan can identify the number, location, involved location, scope, vascular abnormality, and pathological type of COVID-19-related vascular abnormality, which has certain significance for the qualitative and differential diagnosis of COVID-19 vascular abnormality. (2) The chest thin CT plain scan is of great significance for finding elderly patients with COVID-19 involving blood vessels. (3) COVID-19-related "blood vessel thickening" can be caused by either the diameter of the blood vessel itself or the inflammatory edema of the perivascular interstitium.
2023, 32(5): 675-683.
doi: 10.15953/j.ctta.2023.041
Abstract:
Objective: To investigate the clinical value of thin-section chest computed tomography (CT) in the typing of coronavirus disease 2019 (COVID-19). Methods: A retrospective analysis was performed on 134 patients diagnosed with COVID-19 in our hospital’s Department of Infectious Diseases from December 20, 2022, to December 31, 2022. All patients underwent thin-section chest CT scan with complete clinical data. According to clinical classification, patients were divided into the non-severe and severe groups. Clinical data and imaging features of the two groups were compared and analyzed, and statistical analysis was conducted. Results: There was a statistically significant difference with respect to diabetes mellitus between the two groups, and the incidence of diabetes mellitus in the severe group (45.8%) was higher than that in the non-severe group (25.5%); There were no significant differences in sex, age, average course of disease, and clinical symptoms between the two groups; There were significant differences in the number of lesions, symmetrical distribution, predominant peripheral distribution, diffuse distribution, blurred edge, morphology of large flake and band, vascular bundle thickening, paving stone sign, arcade sign, and fried egg sign between the two groups, the number of lesions >10, diffuse distribution, morphology of large flake and band, vascular bundle thickening, paving stone sign, and arcade sign were more common in the severe group than in the non-severe group, while predominant peripheral distribution, blurred edge, and fried egg sign were more common in the non-severe group than in the severe group. Conclusions: Thin-section chest CT scan can identify the abnormal imaging manifestations of the lung in patients with COVID-19 and evaluate the number, distribution range, and morphological characteristics of the lesions. Combined background diseases, number, distribution characteristics, blurred edge, large flake and band morphology, vascular bundle thickening, paving stone sign, arcade sign, and fried egg sign can effectively indicate the classification of patients with COVID-19. This can provide imaging evidence for the diagnosis and treatment of COVID-19.
Objective: To investigate the clinical value of thin-section chest computed tomography (CT) in the typing of coronavirus disease 2019 (COVID-19). Methods: A retrospective analysis was performed on 134 patients diagnosed with COVID-19 in our hospital’s Department of Infectious Diseases from December 20, 2022, to December 31, 2022. All patients underwent thin-section chest CT scan with complete clinical data. According to clinical classification, patients were divided into the non-severe and severe groups. Clinical data and imaging features of the two groups were compared and analyzed, and statistical analysis was conducted. Results: There was a statistically significant difference with respect to diabetes mellitus between the two groups, and the incidence of diabetes mellitus in the severe group (45.8%) was higher than that in the non-severe group (25.5%); There were no significant differences in sex, age, average course of disease, and clinical symptoms between the two groups; There were significant differences in the number of lesions, symmetrical distribution, predominant peripheral distribution, diffuse distribution, blurred edge, morphology of large flake and band, vascular bundle thickening, paving stone sign, arcade sign, and fried egg sign between the two groups, the number of lesions >10, diffuse distribution, morphology of large flake and band, vascular bundle thickening, paving stone sign, and arcade sign were more common in the severe group than in the non-severe group, while predominant peripheral distribution, blurred edge, and fried egg sign were more common in the non-severe group than in the severe group. Conclusions: Thin-section chest CT scan can identify the abnormal imaging manifestations of the lung in patients with COVID-19 and evaluate the number, distribution range, and morphological characteristics of the lesions. Combined background diseases, number, distribution characteristics, blurred edge, large flake and band morphology, vascular bundle thickening, paving stone sign, arcade sign, and fried egg sign can effectively indicate the classification of patients with COVID-19. This can provide imaging evidence for the diagnosis and treatment of COVID-19.
2023, 32(5): 685-694.
doi: 10.15953/j.ctta.2023.079
Abstract:
Purpose: Utilizing deep learning techniques, this study aimed to develop an artificial intelligence model that automatically annotates lesion computed tomography (CT) data, accurately and rapidly distinguishing novel coronavirus pneumonia (COVID-19) from other community-acquired pneumonia cases. Methods: A retrospective analysis was conducted on data from 248 patients with COVID-19 and 347 patients with other types of pneumonia. The COVID-19 cases were differentiated from other pneumonia cases during classification. After performing artificial intelligence-based lung segmentation, the extracted abnormal CT image features were dimensionally reduced and inputted into various classical machine learning models, Three-dimensional convolutional neural network (3D CNN), and attention-Multiple-instance learning (MIL) deep neural network architectures. The diagnostic performance of the models was evaluated using metrics such as receiver operating characteristic (ROC) curves, Precision Recall (PR) curves, Area Under Curve (AUC), sensitivity, specificity, and accuracy. Results: Among the classical machine learning models, K-Nearest Neighbor (KNN)demonstrated good performance, with an AUC of 0.793, Average Precision (AP) of 0.886, Balanced F Score (F1-score) of 0.7608, accuracy of 0.7512, sensitivity of 0.7754, and precision of 0.7691 on the external test set. The classical 3D CNN model exhibited satisfactory performance on the external test set with an AUC of 0.635, AP of 0.816, F1-score of 0.7144, accuracy of 0.7783, sensitivity of 0.6603, and precision of 0.6200. The attention-MIL model showed better robustness on the external test set, achieving an AUC of 0.851, AP of 0.935, F1-score of 0.8193, accuracy of 0.9155, sensitivity of 0.7414, and precision of 0.7646. Conclusion: Compared to the radiomics-enhanced and 3D CNN models, the deep learning attention-MIL model exhibited better performance in the differential diagnosis of COVID-19 and other community-acquired pneumonia.
Purpose: Utilizing deep learning techniques, this study aimed to develop an artificial intelligence model that automatically annotates lesion computed tomography (CT) data, accurately and rapidly distinguishing novel coronavirus pneumonia (COVID-19) from other community-acquired pneumonia cases. Methods: A retrospective analysis was conducted on data from 248 patients with COVID-19 and 347 patients with other types of pneumonia. The COVID-19 cases were differentiated from other pneumonia cases during classification. After performing artificial intelligence-based lung segmentation, the extracted abnormal CT image features were dimensionally reduced and inputted into various classical machine learning models, Three-dimensional convolutional neural network (3D CNN), and attention-Multiple-instance learning (MIL) deep neural network architectures. The diagnostic performance of the models was evaluated using metrics such as receiver operating characteristic (ROC) curves, Precision Recall (PR) curves, Area Under Curve (AUC), sensitivity, specificity, and accuracy. Results: Among the classical machine learning models, K-Nearest Neighbor (KNN)demonstrated good performance, with an AUC of 0.793, Average Precision (AP) of 0.886, Balanced F Score (F1-score) of 0.7608, accuracy of 0.7512, sensitivity of 0.7754, and precision of 0.7691 on the external test set. The classical 3D CNN model exhibited satisfactory performance on the external test set with an AUC of 0.635, AP of 0.816, F1-score of 0.7144, accuracy of 0.7783, sensitivity of 0.6603, and precision of 0.6200. The attention-MIL model showed better robustness on the external test set, achieving an AUC of 0.851, AP of 0.935, F1-score of 0.8193, accuracy of 0.9155, sensitivity of 0.7414, and precision of 0.7646. Conclusion: Compared to the radiomics-enhanced and 3D CNN models, the deep learning attention-MIL model exhibited better performance in the differential diagnosis of COVID-19 and other community-acquired pneumonia.
2023, 32(5): 695-701.
doi: 10.15953/j.ctta.2023.047
Abstract:
Objective: To analyze the computed tomography (CT) imaging features and evolution of different stages of coronavirus disease 2019 (COVID-19). Methods: A retrospective analysis was conducted on the CT images of 113 patients diagnosed with COVID-19 at Hebei Provincial People's Hospital between December 2022 and January 2023 to observe the trends of imaging changes. Results: All 113 patients were clinically diagnosed with COVID-19. Among these, 32, 41, 20, 15, and five patients underwent CT examination once, twice, three times, four times, and five times, respectively. A total of 259 CT examinations were performed in this group of 113 patients. Among them, 32 were early-stage (within 7 days of COVID-19 infection) examinations, 87 were progression-stage CT examinations, and 140 were recovery-stage CT examinations. Analysis of the imaging features of each CT examination of the patients was performed to identify the imaging features and evolution rules of COVID-19. Among the 32 examinations performed in 32patients with early-stage disease (within 7 days of COVID-19 infection), 26 cases showed ground-glass density shadows and six cases additionally showed solid nodules. In the progression stage (8 ~ 30 days after COVID-19 infection), among the 87 CT examinations in 74 patients, 62, 11, and one patient underwent examinations one, two, and three times, respectively. The 87 examinations revealed there 32 cases with ground-glass density shadows and 55 cases with additional solid nodules. In the recovery stage (12 ~ 57 days after COVID-19 infection), 89 patients underwent 140 CT examinations. Among these, 48, 32, eight, and one patient underwent CT examinations once, twice, three times, and four times, respectively. Among the 140 CT examinations, 48 cases showed ground-glass density shadows, while 90 cases additionally showed solid nodules. Moreover, 112 patients had multiple lesions in multiple lobes, with only one case having multiple lesions in a single lobe. Regarding the distributions, in the early stage, 13 cases had subpleural distributions and 19 cases had peribronchovascular and subpleural distributions. In the progression stage, 24 cases had subpleural distribution, and 63 cases had peribronchovascular and subpleural distributions. In the recovery stage, 48 cases had subpleural distribution, two cases showed complete absorption and improvement, and 90 cases had peribronchovascular and subpleural distribution, with 48 cases accompanied by reticular shadows. Regarding thickening of the interlobular septa and vessels within the lesions, in the early stage, 32 cases showed thickening of the interlobular septa and vessels within the lesions. In the progression stage, 85 cases showed thickening of the interlobular septa and 87 cases showed thickening of vessels within the lesions. In the recovery stage, five cases showed thickening of the interlobular septa, one case showed thickening of vessels within the lesions, and 48 cases were accompanied by linear shadows. Finally, in the early stage, one case showed bronchial gas inflation. In the progression stage, six cases showed pleural effusion, six cases showed bronchial inflation, and three cases showed pulmonary emphysema. In the recovery stage, two cases showed bronchial inflation and one case showed pleural effusion. Conclusion: The characteristics of CT images differed in patients with new coronary pneumonia at different times. Understanding this evolution is important to guide clinical treatment.
Objective: To analyze the computed tomography (CT) imaging features and evolution of different stages of coronavirus disease 2019 (COVID-19). Methods: A retrospective analysis was conducted on the CT images of 113 patients diagnosed with COVID-19 at Hebei Provincial People's Hospital between December 2022 and January 2023 to observe the trends of imaging changes. Results: All 113 patients were clinically diagnosed with COVID-19. Among these, 32, 41, 20, 15, and five patients underwent CT examination once, twice, three times, four times, and five times, respectively. A total of 259 CT examinations were performed in this group of 113 patients. Among them, 32 were early-stage (within 7 days of COVID-19 infection) examinations, 87 were progression-stage CT examinations, and 140 were recovery-stage CT examinations. Analysis of the imaging features of each CT examination of the patients was performed to identify the imaging features and evolution rules of COVID-19. Among the 32 examinations performed in 32patients with early-stage disease (within 7 days of COVID-19 infection), 26 cases showed ground-glass density shadows and six cases additionally showed solid nodules. In the progression stage (8 ~ 30 days after COVID-19 infection), among the 87 CT examinations in 74 patients, 62, 11, and one patient underwent examinations one, two, and three times, respectively. The 87 examinations revealed there 32 cases with ground-glass density shadows and 55 cases with additional solid nodules. In the recovery stage (12 ~ 57 days after COVID-19 infection), 89 patients underwent 140 CT examinations. Among these, 48, 32, eight, and one patient underwent CT examinations once, twice, three times, and four times, respectively. Among the 140 CT examinations, 48 cases showed ground-glass density shadows, while 90 cases additionally showed solid nodules. Moreover, 112 patients had multiple lesions in multiple lobes, with only one case having multiple lesions in a single lobe. Regarding the distributions, in the early stage, 13 cases had subpleural distributions and 19 cases had peribronchovascular and subpleural distributions. In the progression stage, 24 cases had subpleural distribution, and 63 cases had peribronchovascular and subpleural distributions. In the recovery stage, 48 cases had subpleural distribution, two cases showed complete absorption and improvement, and 90 cases had peribronchovascular and subpleural distribution, with 48 cases accompanied by reticular shadows. Regarding thickening of the interlobular septa and vessels within the lesions, in the early stage, 32 cases showed thickening of the interlobular septa and vessels within the lesions. In the progression stage, 85 cases showed thickening of the interlobular septa and 87 cases showed thickening of vessels within the lesions. In the recovery stage, five cases showed thickening of the interlobular septa, one case showed thickening of vessels within the lesions, and 48 cases were accompanied by linear shadows. Finally, in the early stage, one case showed bronchial gas inflation. In the progression stage, six cases showed pleural effusion, six cases showed bronchial inflation, and three cases showed pulmonary emphysema. In the recovery stage, two cases showed bronchial inflation and one case showed pleural effusion. Conclusion: The characteristics of CT images differed in patients with new coronary pneumonia at different times. Understanding this evolution is important to guide clinical treatment.
