Chest Computed Tomography Images Research Articles

Can Opportunistic Use of Computed Tomography Help Reveal the Association Between Hepatic Steatosis and Disease Severity in Hospitalized COVID-19 Patients?

To measure hepatic steatosis (HS) in hospitalized COVID-19 patients using unenhanced chest computed tomography (CT) imaging and to evaluate the relationship between disease severity and prognosis in adult patients.This retrospective study included 152 consecutive hospitalized COVID-19 patients with a positive reverse transcriptase polymerase chain reaction (RT-PCR) test. The COVID-19 Reporting and Data System (CO-RADS) and the chest CT score were evaluated. HS measurements were performed based on CT images using a single region of interest placed on the right liver lobe (segments V-VII). HS was defined as a liver attenuation value <40 Hounsfield units. Data were collected and compared with the patients' prognostic parameters.Of the 152 inpatients, 137 patients (90.1%) had a CT score ≥3 and 109 patients (71.7%) had a CO-RADS score ≥4, 43 (28.2%) had HS. All patients with HS (100%) and 94/109 (86.2%) patients without HS had a CT score ≥3. There was a statistically significant difference between the two groups in terms of chest CT score (p=0.006). There was no statistically significant difference between the two groups in terms of CO-RADS score (p=0.291). The median CRP levels were significantly increased in patients with HS compared to patients without HS (p=0.023). There was no significant difference in ICU hospitalization and mortality due to the presence of HS (p>0.05).The current study revealed significantly higher chest CT scores in COVID-19 patients with HS measured on CT compared to those without HS. Opportunistic use of CT images for the detection of HS can be considered as an adjunctive tool in the risk analysis of COVID-19 patients hospitalized due to COVID-19 pneumonia.The severity of COVID-19 disease is increased in hospitalized patients with hepatosteatosis compared to patients with a normal liver. Density measurements for the evaluation of HS using opportunistic CT applications can be considered as an adjunctive tool in the prognostic evaluation of hospitalized patients with COVID-19 pneumonia. · Parlak AE, Erdem Toslak İ, Turkoglu Selcuk N. Can Opportunistic Use of Computed Tomography Help Reveal the Association Between Hepatic Steatosis and Disease Severity in Hospitalized COVID-19 Patients?. Fortschr Röntgenstr 2024; DOI 10.1055/a-2369-8377.

Feasibility of Epicardial Adipose Tissue Quantification Using Non-electrocardiogram-Gated Chest Computed Tomography Images.

Epicardial adipose tissue (EAT) is an important imaging indicator of cardiovascular risk. EAT volume is usually measured using electrocardiogram (ECG) gating. However, there are concerns regarding the influence of motion artifacts when measuring EAT volume on non-ECG-gated plain chest computed tomography (CT) images. Few studies have evaluated the EAT volume using non-ECG gating. This study aimed to validate the accuracy of EAT quantification using non-ECG-gated chest CT imaging. We included 100 patients (64 males, 36 females) who underwent simultaneous coronary artery calcification score imaging (ECG gated) and plain chest CT imaging (non-ECG gated). Images taken using non-ECG gating were reconstructed using the same field of view and slice thickness as those obtained with ECG gating. The EAT capacity of each image was measured and compared. An AZE Virtual Place (Canon) was used for the measurements. The Mann-Whitney U test and intraclass correlation coefficient were used for statistical analyses. P values <0.05 were considered statistically significant. Concordance was evaluated using Bland-Altman analysis. The mean EAT volume measured by ECG-gated imaging was 156.5 ± 66.9 mL and 155.4 ± 67.9 mL by non-ECG-gated imaging, with no significant difference between the two groups (P = 0.86). Furthermore, the EAT volumes measured using ECG-gated and non-ECG-gated imaging showed a strong correlation (r = 0.95, P < 0.05). Bland-Altman analysis revealed that the mean error of the EAT volume (non-ECG-gated imaging - ECG-gated imaging) was -1.02 ± 2.95 mL (95% confidence interval, -6.49 to 4.76). The EAT volume obtained using non-ECG-gated imaging was equivalent to that obtained using ECG-gated imaging.

Assessing the inter-observer and intra-observer reliability of radiographic measurements for size-specific dose estimates

BackgroundCalculating size-specific dose estimates (SSDEs) requires measurement of the patient’s anteroposterior (AP) and lateral thickness based on computed tomography (CT) images. However, these measurements can be subject to variation due to inter-observer and intra-observer differences. This study aimed to investigate the impact of these variations on the accuracy of the calculated SSDE.MethodsFour radiographers with 1–10 years of experience were invited to measure the AP and lateral thickness on 30 chest, abdomen, and pelvic CT images. The images were sourced from an internet-based database and anonymized for analysis. The observers were trained to perform the measurements using MicroDicom software and asked to repeat the measurements 1 week later. The study was approved by the institutional review board at Taibah University, and written informed consent was obtained from the observers. Statistical analyses were performed using Python libraries Pingouin (version 0.5.3), Seaborn (version 0.12.2), and Matplotlib (version 3.7.1).ResultsThe study revealed excellent inter-observer agreement for the calculated effective diameter and AP thickness measurements, with Intraclass correlation coefficients (ICC) values of 0.95 and 0.96, respectively. The agreement for lateral thickness measurements was lower, with an ICC value of 0.89. The second round of measurements yielded nearly the same levels of inter-observer agreement, with ICC values of 0.97 for the effective diameter, 1.0 for AP thickness, and 0.88 for lateral thickness. When the consistency of the observer was examined, excellent consistency was found for the calculated effective diameter, with ICC values ranging from 0.91 to 1.0 for all observers. This was observed despite the lower consistency in the lateral thickness measurements, which had ICC values ranging from 0.78 to 1.0.ConclusionsThe study’s findings suggest that the measurements required for calculating SSDEs are robust to inter-observer and intra-observer differences. This is important for the clinical use of SSDEs to set diagnostic reference levels for CT scans.

Computed tomography-based radiomics and clinical-genetic features for brain metastasis prediction in patients with stage III/IV epidermal growth factor receptor-mutant non-small-cell lung cancer.

To evaluate the value of computed tomography (CT)-based radiomics combined with clinical-genetic features in predicting brain metastasis in patients with stage III/IV epidermal growth factor receptor (EGFR)-mutant non-small-cell lung cancer (NSCLC). The study included 147 eligible patients treated at our institution between January 2018 and May 2021. Patients were randomly divided into two cohorts for model training (n = 102) and validation (n = 45). Radiomics features were extracted from the chest CT images before treatment, and a radiomics signature was constructed using the Least Absolute Shrinkage and Selection Operator regression. Kaplan-Meier survival analysis was used to describe the differences in brain metastasis-free survival (BM-FS) risk. A clinical-genetic model was developed using Cox regression analysis. Radiomics, genetic, and combined prediction models were constructed, and their predictive performances were evaluated by the concordance index (C-index). Patients with a low radiomics score had significantly longer BM-FS than those with a high radiomics score in both the training (p < 0.0001) and the validation (p = 0.0016) cohorts. The C-indices of the nomogram, which combined the radiomics signature and N stage, overall stage, third-generation tyrosine kinase inhibitor treatment, and EGFR mutation status, were 0.886 (95% confidence interval [CI] 0.823-0.949) and 0.811 (95% CI 0.719-0.903) in the training and validation cohorts, respectively. The combined model achieved a higher discrimination and clinical utility than the single prediction models. The combined radiomics-genetic model could be used to predict BM-FS in stage III/IV NSCLC patients with EGFR mutations.

Constrictive pericarditis in a patient with fiberglass lung disease: a case report

BackgroundFiberglass has a larger aerodynamic diameter and is less likely to be inhaled into the lungs. Further, it will be cleared even if it is mechanically broken into smaller pieces and inhaled into the lungs. Fiberglass lung disease has been well documented if long term exposure but was thought reversible and would not cause severe diseases. The diagnosis of fiberglass lung disease depends on exposure history and histopathological findings. However, the exact occupational exposure history is often difficult to identify because mixed substance exposure often occurs and fiberglass disease is not as well-known as asbestosis.Case presentationA 66-year-old man had unexplained transudative pericardial effusion requiring pleural pericardial window operation twice at another medical center where asbestosis was told because of his self-reported long-term asbestosis exposure and the histopathological finding of a ferruginous body in his lung. Constrictive pericarditis developed two years later and resulted in congestive heart failure. Radical pericardiectomy combined with lung biopsy was performed following chest computed tomography imaging and the transudative nature of pericardial effusion not compatible with asbestosis. However, the histopathologic findings of his lung and pericardium at our hospital only showed chronic fibrosis without any asbestosis body. The patient’s lung was found to be extremely fragile during a lung biopsy; histopathologic specimens were reviewed, and various fragments of fiberglass were found in the lung and pericardium. The patient’s occupational exposure was carefully reevaluated, and he restated that he was only exposed to asbestosis for 1–2 years but was heavily exposed to fiberglass for more than 40 years. This misleading exposure history was mainly because he was only familiar with the dangers of asbestos. Since most fiberglass lung diseases are reversible and the symptoms of heart failure resolve soon after surgery, only observation was needed. Ten months after radical pericardiectomy, his symptoms, pleural effusion, and impaired pulmonary function eventually resolved.ConclusionFiberglass could cause inflammation of the pericardium, resulting in pericardial effusion and constrictive pericarditis, which could be severe and require radical pericardiectomy. Exact exposure history and histopathological examinations are the key to diagnosis.

Association between thymus density loss and efficacy in non-small cell lung cancer patients treated with immune checkpoint inhibitors.

Although the thymus undergoes degeneration with the advancement of age, recent studies have continuously revealed that the thymus possesses the potential for regeneration and may reverse this aging trend. Furthermore, an increasing number of studies indicate an association between thymus function and immunotherapy. Considering that lung cancer patients typically undergo chest computed tomography (CT) scans during treatment, this provides convenient conditions for us to observe thymic remodeling through imaging data. Therefore, exploring the changes in the thymus on CT images is of great significance for understanding its relationship with the efficacy of immunotherapy in non-small cell lung cancer (NSCLC) patients. This study investigated the CT imaging characteristics of thymic density changes in patients with advanced NSCLC after immunotherapy. The primary objective was to determine whether changes in thymic density are predictors of response to immunotherapy in patients with NSCLC. A total of 412 patients with advanced NSCLC who underwent immunotherapy were included. Thymic density measurements were taken initially and after immunotherapy, with the annualized change calculated. Comprehensive analysis, including disease progression, survival, and subgroup assessments, was conducted. The primary outcome was overall survival (OS), and the secondary outcomes were progression-free survival (PFS), objective response rate (ORR) and disease control rate (DCR). The annual change in density of the thymic region ranged from -108 to 108 HU after the initiation of ICIs. Patients were categorized into "loss" or "non-loss" groups (210 vs. 202) based on thymic density changes. Analysis of short-term progression of solid tumors revealed no statistically significant differences in ORR (P=0.55) and DCR (P=0.67) between the two groups. Throughout the entire follow-up period, 41 patients (19.5%) in the "loss" group and 64 patients (31.7%) in the "non-loss" group died. Thymic density reduction was not associated with PFS (P=0.08), but it was positively associated with increased OS (P=0.003). The results were consistent across subgroups. Thymic density changes were observed in nearly all NSCLC patients undergoing immunotherapy, with decreased density associated with longer OS. These findings suggest a potential association between thymic density changes and immune efficacy in NSCLC immunotherapy.

Balancing accuracy and efficiency: A lightweight deep learning model for COVID 19 detection

The novel coronavirus disease in human was detected in Wuhan, China and spread rapidly across countries each time mutating itself as a new variant. It has impacted not only the patients but also medical infrastructure a lot. Several artificial intelligence-based systems for detection of covid-19 in chest images have been proposed. This study aims to develop a deep learning-based approach that efficiently detect Covid-19 using chest Computed Tomography (CT) images. In this work, we propose an efficient and lightweight deep-learning architecture based on the concept of a densely connected network for Covid-19 detection. The proposed architecture is designed by stacking dense modules in which each layer is directly connected to the other layers, with very fewer number of neurons at each layer. The dense connection is used to improve the learning capability of the proposed model and the fewer number of neurons make network computationally efficient. CT images for covid-19 detection are effectively used for the detection of Covid-19 patients. We train and test our model on SARS-CoV-2 CT-scan datasets. The experimental results are evaluated on accuracy, sensitivity and F1 score. The Adam optimizer with binary cross entropy loss function gives 97.2% accuracy with 0.98 sensitivity, 0.97 precision and 0.97 F1 score. The model achieves maximum accuracy of 88% at precision 0.97%, specificity 0.82 and F1 score 0.89 with Covid-CT dataset. The proposed model is compared with other state-of-the-art methods. The results of the proposed methods and its comparison shows acceptable level of performance on chest CT images.

SCALED SIAMESE CNN-BASED AUTOMATIC CLASSIFICATION ALGORITHM FOR DETECTING PULMONARY EMBOLISM

The majority of patients today have vascular illness that could cause pulmonary hypertension and pulmonary emboli which can lead to serious conditions. To diagnose changes in veins and arteries, manual, semi-automatic and automatic examination of a individuals chest computed tomography image or CT Pulmonary Angiogram (CTPA) is performed. Manual CT scan analysis is more tedious, takes more time and is not standardized. Thus the separation of vascular trees in CTPA scans is now semi-automatic and automatic which enables doctors to precisely identify aberrant conditions. Recent research has focused on applying machine learning along with deep learning approaches to find and categorize pulmonary vascular disorders like pulmonary embolism and pulmonary hypertension. Here, a brand-new technique is put out for the automatically classifying pulmonary veins in order to more accurately diagnose lung illnesses related to Pulmonary Embolism (PE). The proposed method uses Scaled Siamese-based Convoutional Neural Network (CNN) for classifying Pulmonary Embolism images with an accuracy more than 90 while using two publicly available datasets FUMPE and RSNA. Finally, comparison of the proposed method with other pretrained CNN-based algorithms for detecting PE is done in terms of Area Under Curve (AUC), recall, precision, accuracy, [Formula: see text]1 score, etc. and values are tabulated and our proposed method shows good results. Comparison was also done using deep learning models like PENET, Inception [Formula: see text]3 and [Formula: see text] fold cross validation and our proposed method values are outstanding compared to other methods.

Retraction Note: Lung nodule malignancy classification in chest computed tomography images using transfer learning and convolutional neural networks

Retraction Note: Lung nodule malignancy classification in chest computed tomography images using transfer learning and convolutional neural networks

Utility of block-matching and 3D filter for reproducibility of lung density and denoising in low-dose chest CT: A pilot study

PurposeThis study aimed to acquire an image quality consistent with that of full-dose chest computed tomography (CT) when obtaining low-dose chest CT images and to analyze the effects of block-matching and 3D (BM3D) filters on lung density measurements and noise reduction in lung parenchyma. MethodsUsing full-dose chest CT images, we evaluated lung density measurements and noise reduction in lung parenchyma images for low-dose chest CT. Three filters (median, Wiener, and the proposed BM3D) were applied to low-dose chest CT images for comparison and analysis with images from full-dose chest CT. To evaluate lung density measurements, we measured CT attenuation at the 15th percentile of the lung CT histogram. The coefficient of variation (COV) and contrast-to-noise ratio (CNR) were used to evaluate the noise level. ResultsThe 15th percentile of the lung CT histogram showed the smallest difference between full- and low-dose CT when applying the BM3D filter, and the highest difference between full- and low-dose CT without filters (full-dose = − 926.28 ± 0.32, BM3D = − 926.65 ± 0.32, and low-dose = − 959.43 ± 0.95) (p < 0.05). The COV was smallest when applying the BM3D filter, whereas the CNR was the highest (p < 0.05). ConclusionsThe results of the study prove that the BM3D filter can reduce image noise while increasing the reproducibility of the lung density, even for low-dose chest CT.

Extravascular protrusion of the Alterra adaptive prestent identified on surveillance computed tomography imaging.

The Alterra adaptive prestent is a novel self-expanding device designed to provide a landing zone for the 29 mm SAPIEN 3 valve to treat pulmonary regurgitation in patients with a right ventricular outflow tract that is too large for a balloon expandable valve alone. The mechanism of fixation for the Alterra prestent is radial force from the self-expanding stent frame, combined with a unique set of flared "tines" that protrude from both ends of the stent. In this report, we describe 6patients who underwent uncomplicated transcatheter pulmonary valve replacement with an Alterra adaptive prestent and SAPIEN 3 valve and had surveillance chest computed tomography (CT) scans performed 1 day to 21 months after implant. In each patient, the CT scan demonstrated extravascular extension of a portion of the Alterra prestent, without clinical sequelae, but with extension into the ascending aorta in 1 patient and contact with the ascending aorta, left pulmonary vein, or left atrial appendagein 3 others. Surveillance CT imaging shows that the Alterra prestent can perforate the pulmonary artery and/or right ventricle. Although no sequelae were seen in these patients, prestent perforation has the potential to be clinically important. Implanters should be aware of this finding and its potential implications. As experience with the Alterra prestent grows, it will be important to further define the risk factors, incidence, and implications of this phenomenon.

Chest Computed Tomography Severity Score to Predict Outcome of Patients with COVID 19

Abstract Background The coronavirus disease 2019 (COVID-19) is an acute infectious pneumonia caused by a severe acute respiratory syndrome-coronavirus-2. Some studies have suggested that C-reactive protein, lymphocyte count, and features from CT scoring as predictors of severity and prognosis of COVID-19. Non-contrast chest computed tomography (CT) scan was found to be a useful diagnostic instrument for screening COVID-19 pneumonia. This score uses lung opacification as a surrogate for extension of the disease in the lungs. Aim To assess the prognostic value of chest CT severity score (CSS) to predict the severity of symptoms and outcomes among patients with typical CT findings. Subject and Methods This Prospective Observational study was conducted at Radiology department, faculty of medicine, Ain Shams University on 94 patients. Results There is a significant difference between the groups regarding symptoms severity. there is no significant difference between the groups regarding CT findings. left lung severity scores and total severity score were significantly higher among poor prognosis patients compared to good prognosis patients. mortality rate was significantly higher among poor prognosis patients compared to good prognosis patients. severity score was significantly higher among severe symptoms patients compared to mild symptoms patients in good prognosis patients. Conclusion The current study showed that CT images and CT-severity scores can predict ICU admission and mortality in patients with COVID-19 pneumonia. Clinicians and radiologists can use the CT-severity score as a measure of disease severity and an indicator of poor prognosis, including the need for mechanical ventilation. Also, our findings showed the superiority of CT findings over other clinical information and co-morbidities in predicting the short-term outcome.

Refining Cardiovascular Calcification in the Appraisal of Ischemic Heart.

Even in asymptomatic patients, there is a high association of ischemia on myocardial perfusion scans in those with coronary artery calcification or valvular calcifications. Patients without coronary artery calcifications have exceeding-low rates of cardiovascular events. The absence of cardiovascular calcification, including coronary artery, valvular, and thoracic aorta is a strong negative predictor of myocardial ischemia. In individuals with suspected ischemia who had chest computed tomography imaging, evaluation for cardiovascular calcification (coronary artery, valves, and thoracic aorta) is an invaluable tool to guide management for further diagnostic imaging. We hypothesize that the complete absence of cardiovascular calcification has a high negative predictive value for defects in myocardial perfusion imaging such as single-photon emission computed tomography(SPECT) or positron emission tomography(PET). Non-contrast computed tomography performed for SPECT/PET CT attenuation correction from March 1, 2017, to September 30, 2017, were retrospectively reviewed for the absence of cardiovascular calcification by a cardiologist andradiologist who were blinded to patients' medical history. Medical records were reviewed to include patient demographics and medical history. A total of 132 patients were analyzed. Of the 132 patients without calcifications, seven patients had small myocardial perfusion defects suggestive of ischemia or infarct, but none were considered significant defects. Of these seven patients, six were managed medically and one was from an outside institution with no follow-up data. Two of the seven patients had follow-up invasive angiography or coronary CTA that did not show significant atherosclerotic coronary artery disease. A complete absence of cardiovascular calcification indicates a 100% negative predictive value for a significant perfusion defect on same-day confirmatory nuclear stress testing. Patients with suspected ischemia but absent cardiovascular calcifications can be safely managed medically without further testing for ischemia.

Subchronic particulate matter exposure underlying polyhexamethylene guanidine phosphate–induced lung injury: Quantitative and qualitative evaluation with chest computed tomography

Our study was to explore the effects of subchronic particulate matter (PM) exposure on lung injury induced by polyhexamethylene guanidine phosphate (PHMG-p) in a rat model. Specifically, we investigated pulmonary inflammation, fibrosis, and tumor formation using chest computed tomography (CT), and histopathologic examination. PHMG-p was administered intratracheally to 20 male rats. After an initial week of PHMG-p treatment, the experimental group (PM group) received intratracheal administration of PM suspension, while the control group received normal saline. This regimen was continued for 10 weeks to induce subchronic PM exposure. Chest CT scans were conducted on all rats, followed by the extraction of both lungs for histopathological analysis. All CT images underwent comprehensive quantitative and qualitative analyses. Pulmonary inflammation was markedly intensified in rats subjected to subchronic PM exposure in the PM group compared to those in the control. Similarly, lung fibrosis was more severe in the PM group as observed on both chest CT and histopathologic examination. Quantitative chest CT analysis revealed that the mean lesion volume was significantly greater in the PM group than in the control group. Although the incidence of bronchiolo-alveolar hyperplasia was higher in the PM group compared to the control group, this difference was not statistically significant. In summary, subchronic PM exposure exacerbated pulmonary inflammation and fibrosis underlying lung injury induced by PHMG-p.

Development and Validation of a Diagnosis System for Lung Infection Using Hybrid Deep-Learning Techniques

A fast and accurate test is necessary to detect COVID-19. A computed tomography (CT) scan has shown diagnostic accuracy. CT scan processing using a deep learning architecture may improve illness diagnosis and treatment. We proposed a deep learning system for COVID-19 detection using CT images, including using and comparing transfer-learning, fine-tuning, and the embedding process. This paper presents the development of a COVID-19 case identification model using deep learning techniques. The suggested model utilized a modified visual geometry group (VGG16) architecture as the deep learning framework. The model was trained and validated using a chest CT image dataset. The SARS-COV-2 dataset contains 2482 CT scans of 210 patients from publicly available sources. The modified model demonstrated encouraging outcomes by greatly enhancing the sensitivity measure (95.82±1.75)%, which is an essential criterion for accurately detecting instances of COVID-19 infection. In addition, the model achieved higher values for the accuracy metric (91.67±1.68)%, the specificity meter (88.08±3.72)%, the precision metric (87.51±3.27)%, the F1 score (91.43±1.55)%, and the area under the curve (91.98±1.55)%. Deep learning effectively detects COVID-19 in chest CT scan images. Clinical practitioners may employ the suggested approach to study, identify, and effectively mitigate a greater number of pandemics.

Artificial intelligence-based quantification of COVID-19 pneumonia burden using chest CT

AbstractDuring the coronavirus disease 2019 (COVID-19) pandemic, artificial intelligence (AI) based software on chest computed tomography (CT) imaging has proven to have a valuable role in accelerating diagnosis and screening. The proposed AI-based tools proved to be rapid and reproducible techniques to guide patient management and treatment protocols. Although no specific guidelines exist, CT-imaging and clinical features are used for patient staging. To shed light on the role of AI techniques that have been developed in fighting COVID-19, in this review, studies investigating the usage of commonly used AI models on chest CT imaging for disease quantification and prognostication are collected.

Effect of Model-Based Iterative Reconstruction on Image Quality of Chest Computed Tomography for COVID-19 Pneumonia.

This study aimed to compare the image quality of chest computed tomography (CT) scans for COVID-19 pneumonia using forward-projected model-based iterative reconstruction solution-LUNG (FIRST-LUNG) with filtered back projection (FBP) and hybrid iterative reconstruction (HIR). The CT images of 44 inpatients diagnosed with COVID-19 pneumonia between December 2022 and June 2023 were retrospectively analyzed. The CT images were reconstructed using FBP, HIR, and FIRST-LUNG-MILD/STANDARD/STRONG. The CT values and noise of the lumen of the main trachea and erector spine muscle were measured for each group. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated. Subjective evaluations included overall image quality, noise, streak artifact, visualization of normal lung structures, and abnormal CT features. One-way analysis of variance was used to compare the objective and subjective indicators among the five groups. The task-based transfer function was derived for three distinct contrasts representing anatomical structures, lower-contrast lesion, and higher-contrast lesion. The results of the study demonstrated significant differences in image noise, SNR, and CNR among the five groups (P < 0.001). The FBP images exhibited the highest levels of noise and the lowest SNR and CNR among the five groups (P < 0.001). When compared to the FBP and HIR groups, the noise was lower in the FIRST-LUNG-MILD/STANDARD/STRONG group, while the SNR and CNR were higher (P < 0.001). The subjective overall image quality score of FIRST-LUNG-MILD/STANDARD was significantly better than FBP and FIRST-LUNG-STRONG (P < 0.001). FIRST-LUNG-MILD was superior to FBP, HIR, FIRST-LUNG-STANDARD, and FIRST-LUNG-STRONG in visualizing proximal and peripheral bronchovascular and subpleural vessels (P < 0.05). Additionally, FIRST-LUNG-MILD achieved the best scores in evaluating abnormal lung structure (P < 0.001). The overall interobserver agreement was substantial (intraclass correlation coefficient = 0.891). The task-based transfer function 50% values of FIRST reconstructions are consistently higher compared to FBP and HIR. The FIRST-LUNG-MILD/STANDARD algorithm can enhance the image quality of chest CT in patients with COVID-19 pneumonia, while preserving important details of the lesions, better than the FBP and HIR algorithms. After evaluating various COVID-19 pneumonia lesions and considering the improvement in image quality, we recommend using the FIRST-LUNG-MILD reconstruction for diagnosing COVID-19 pneumonia.

Thymic Rosai-Dorfmann disease: a case report

BackgroundRosai-Dorfman disease (RDD), known as sinus histiocytosis with massive lymphadenopathy, commonly involves lymph nodes in the neck or mediastinum, although extranodal involvement is observed in approximately 40% of RDD patients. RDD involving only the thymus has rarely been reported. Here, we report a case of RDD originating in the thymus. The lesion was surgically removed, and a cure was finally achieved. There was no recurrence after telephone follow-up for 3 years.Case presentationA 52-year-old male was accidentally found to have a 7 × 6 cm anterior mediastinum lump by chest computed tomography (CT). The mediastinal lesion was resected by surgery, and postoperative pathology revealed RDD originating from the thymus. Regular telephone follow-up after surgery lasted 3 years and showed that the patient remained in good condition without any relevant symptoms.ConclusionsRDD originating in the thymus cannot be characterized from CT images and is easily misdiagnosed as a traditional mediastinal tumor. This is mainly because there is so little disease in this area that physicians are not aware of it. We report this case with the hope that clinicians will have a better understanding of this disease. According to our follow-up results, surgery is an effective means of treatment.

A novel soft computing based efficient feature selection approach for timely identification of COVID-19 infection using chest computed tomography images: a human centered intelligent clinical decision support system

A novel soft computing based efficient feature selection approach for timely identification of COVID-19 infection using chest computed tomography images: a human centered intelligent clinical decision support system

Endotracheal tube cuff position in relationship to the walls of the trachea: A retrospective computed tomography-based analysis.

The use of cuffed endotracheal tubes (ETTs) has become the standard of care in pediatric practice. The rationale for the use of a cuffed ETT is to minimize pressure around the cricoid while providing an effective airway seal. However, safe care requires that the cuff lie distal to the cricoid ring following endotracheal intubation. The current study demonstrates the capability of computed tomography (CT) imaging in identifying the position of the cuff of the ETT in intubated patients. The study included patients ranging in age from 1 month to 10 years who underwent neck and chest CT imaging that required general anesthesia and endotracheal intubation. The location of the ETT and of the cuff within the airway was determined from axial CT images at three levels (proximal, middle, and distal). Anatomical orientations were tabulated, and percent chances of each orientation were determined for the ETT and the cuff. The study cohort included 42 patients ranging in age from 1 to 114 months. An ETT with a polyvinylchloride cuff was used in 24 patients, and an ETT with a polyurethane cuff was used in 18 patients. The ETT was located near the posterior wall of the trachea in approximately 24-38% of patients, being most likely to be centrally located at the proximal end and at its mid-portion. The middle part of the cuff was most likely to be positioned in the mid-portion of the trachea but tended to skew anteriorly at both the proximal and distal ends. This is the first study using CT imaging to identify the uniformity of cuff inflation within the trachea in children. With commonly used cuffed ETTs, cuff inflation and the final position of ETT cuff within the tracheal lumen were not uniform. Future investigations are needed to determine the reasons for this asymmetry and its clinical implications.