Ultrahigh-Resolution Photon-Counting Detector CT of the Lungs: Association of Reconstruction Kernel and Slice Thickness With Image Quality.

BACKGROUND. Prior work has shown improved image quality for photon-counting detector (PCD) CT of the lungs compared with energy-integrating detector CT. A paucity of the literature has compared PCD CT of the lungs using different reconstruction parameters. OBJECTIVE. The purpose of this study is to the compare the image quality of ultra-high-resolution (UHR) PCD CT image sets of the lungs that were reconstructed using different kernels and slice thicknesses. METHODS. This retrospective study included 29 patients (17 women and 12 men; median age, 56 years) who underwent noncontrast chest CT from February 15, 2022, to March 15, 2022, by use of a commercially available PCD CT scanner. All acquisitions used UHR mode (1024 × 1024 matrix). Nine image sets were reconstructed for all combinations of three sharp kernels (BI56, BI60, and BI64) and three slice thicknesses (0.2, 0.4, and 1.0 mm). Three radiologists independently reviewed reconstructions for measures of visualization of pulmonary anatomic structures and pathologies; reader assessments were pooled. Reconstructions were compared with the clinical reference reconstruction (obtained using the BI64 kernel and a 1.0-mm slice thickness [BI641.0-mm]). RESULTS. The median difference in the number of bronchial divisions identified versus the clinical reference reconstruction was higher for reconstructions with BI640.4-mm (0.5), BI600.4-mm (0.3), BI640.2-mm (0.5), and BI600.2-mm (0.2) (all p < .05). The median bronchial wall sharpness versus the clinical reference reconstruction was higher for reconstructions with BI640.4-mm (0.3) and BI640.2-mm (0.3) and was lower for BI561.0-mm (-0.7) and BI560.4-mm (-0.3) (all p < .05). Median pulmonary fissure sharpness versus the clinical reference reconstruction was higher for reconstructions with BI640.4-mm (0.3), BI600.4-mm (0.3), BI560.4-mm (0.5), BI640.2-mm (0.5), BI600.2-mm (0.5), and BI560.2-mm (0.3) (all p < .05). Median pulmonary vessel sharpness versus the clinical reference reconstruction was lower for reconstructions with BI561.0-mm (-0.3), BI600.4-mm (-0.3), BI560.4-mm (-0.7), BI640.2-mm (-0.7), BI600.2-mm (-0.7), and BI560.2-mm (-0.7). Median lung nodule conspicuity versus the clinical reference reconstruction was lower for reconstructions with BI561.0-mm (-0.3) and BI560.4-mm (-0.3) (both p < .05). Median conspicuity of all other pathologies versus the clinical reference reconstruction was lower for reconstructions with BI561.0 mm (-0.3), BI560.4-mm (-0.3), BI640.2-mm (-0.3), BI600.2-mm (-0.3), and BI560.2-mm (-0.3). Other comparisons among reconstructions were not significant (all p > .05). CONCLUSION. Only the reconstruction using BI640.4-mm yielded improved bronchial division identification and bronchial wall and pulmonary fissure sharpness without a loss in pulmonary vessel sharpness or conspicuity of nodules or other pathologies. CLINICAL IMPACT. The findings of this study may guide protocol optimization for UHR PCD CT of the lungs.

The impact of the COVID-19 pandemic on nasopharyngeal carcinoma extent at FDG PET/MR staging: The NPCOVIPET study.

BACKGROUND: To evaluate the impact of coronavirus disease 2019 (COVID-19) pandemic on disease extent in patients with nasopharyngeal carcinoma (NPC) using 18 fuorodeoxyglucose (FDG) positron emission tomography (PET)/magnetic resonance imaging (MRI). METHODS: This retrospective cohort study included biopsy-proven, newly diagnosed NPC patients using whole-body FDG PET/MR staging in two selected intervals: 1 May 2017 to 31 January 2020 (Group A, the pre-COVID-19 period), and 1 February 2020 to 30 June 2021 (Group B, the COVID-19 period). RESULTS: Three-hundred and ninety patients were included. No significant difference was observed in terms of T classification, N classification, overall stage, N stations, and M stations between the two groups (p > 0.05). For the involved neck node levels, more patients had developed level Vc metastasis in the group B (p = 0.044). CONCLUSION: Although the overall stage was not affected, more patients with NPC had developed level Vc metastasis in the era of COVID-19.

Pediatric COVID-19: Correlations between Clinical and Imaging Perspectives.

Background: Although SARS-CoV-2 infection primarily affects adults, the increasing emergence of infected pediatric patients has been recently reported. However, there is a paucity of data regarding the value of imaging in relation to the clinical severity of this pandemic emergency. Objectives: To demonstrate the relationships between clinical and radiological COVID-19 findings and to determine the most effective standardized pediatric clinical and imaging strategies predicting the disease severity. Patients and Methods. This observational study enrolled eighty pediatric patients with confirmed COVID-19 infection. The studied patients were categorized according to the disease severity and the presence of comorbidities. Patients' clinical findings, chest X-ray, and CT imaging results were analyzed. Patients' evaluations using several clinical and radiological severity scores were recorded. The relations between clinical and radiological severities were examined. Results: Significant associations were found between severe-to-critical illness and abnormal radiological findings (p = 0.009). In addition, chest X-ray score, chest CT severity score, and rapid evaluation of anamnesis, PO2, imaging disease, and dyspnea-COVID (RAPID-COVID) score were significantly higher among patients with severe infection (p < 0.001, <0.001, and 0.001) and those with comorbidities (p = 0.005, 0.002, and <0.001). Conclusions: Chest imaging of pediatric patients with COVID-19 infection may be of value during the evaluation of severe cases of infected pediatric patients and in those with underlying comorbid conditions, especially during the early stage of infection. Moreover, the combined use of specific clinical and radiological COVID-19 scores are likely to be a successful measure of the extent of disease severity.

Diagnosing the acute abdomen during a global contrast shortage: a single centre experience.

BACKGROUND: The COVID-19 pandemic led to a global shortage of iodinated contrast media (ICM) in early 2022. ICM is used in more than half of the computed tomography of the abdomen and pelvis (CTAP) performed to diagnose an acute abdomen (AA). In response to the shortage, the RANZCR published contrast-conserving recommendations. This study aimed to compare AA diagnostic outcomes of non-contrast CTs performed before and during the shortage. METHODS: A single-centre retrospective observational cohort study of all adult patients presenting with an AA who underwent a CTAP was conducted during the contrast shortage period from May to July 2022. The pre-shortage control comparison group was from January to March 2022; key demographics, imaging modality indication and diagnostic outcomes were collected and analysed using SPSS v27. RESULTS: Nine hundred and sixty-two cases met the inclusion criteria, of which n = 502, 52.2% were in the shortage period group. There was a significant increase of 464% in the number of non-contrast CTAPs performed during the shortage period (P < 0.001). For the six AA pathologies, only n = 3, 1.8% of non-contrast CTAPs had equivocal findings requiring further imaging with a contrast CTAP. Of the total CTs performed, n = 464, 48.2% were negative. CONCLUSION: This study showed that when non-contrast CTs are selected appropriately, they appear to be non-inferior to contrast-enhanced CTAPs in diagnosing acute appendicitis, colitis, diverticulitis, hernia, collection, and obstruction. This study highlights the need for further research into utilizing non-contrast scans for assessing the AA to minimize contrast-associated complications.

A deep learning-based application for COVID-19 diagnosis on CT: The Imaging COVID-19 AI initiative.

BACKGROUND: Recently, artificial intelligence (AI)-based applications for chest imaging have emerged as potential tools to assist clinicians in the diagnosis and management of patients with coronavirus disease 2019 (COVID-19). OBJECTIVES: To develop a deep learning-based clinical decision support system for automatic diagnosis of COVID-19 on chest CT scans. Secondarily, to develop a complementary segmentation tool to assess the extent of lung involvement and measure disease severity. METHODS: The Imaging COVID-19 AI initiative was formed to conduct a retrospective multicentre cohort study including 20 institutions from seven different European countries. Patients with suspected or known COVID-19 who underwent a chest CT were included. The dataset was split on the institution-level to allow external evaluation. Data annotation was performed by 34 radiologists/radiology residents and included quality control measures. A multi-class classification model was created using a custom 3D convolutional neural network. For the segmentation task, a UNET-like architecture with a backbone Residual Network (ResNet-34) was selected. RESULTS: A total of 2,802 CT scans were included (2,667 unique patients, mean [standard deviation] age = 64.6 [16.2] years, male/female ratio 1.3:1). The distribution of classes (COVID-19/Other type of pulmonary infection/No imaging signs of infection) was 1,490 (53.2%), 402 (14.3%), and 910 (32.5%), respectively. On the external test dataset, the diagnostic multiclassification model yielded high micro-average and macro-average AUC values (0.93 and 0.91, respectively). The model provided the likelihood of COVID-19 vs other cases with a sensitivity of 87% and a specificity of 94%. The segmentation performance was moderate with Dice similarity coefficient (DSC) of 0.59. An imaging analysis pipeline was developed that returned a quantitative report to the user. CONCLUSION: We developed a deep learning-based clinical decision support system that could become an efficient concurrent reading tool to assist clinicians, utilising a newly created European dataset including more than 2,800 CT scans.

COVID-19 Chest CT Quantification: Triage and Prognostic Value in Different Ages.

Objective: We evaluated the triage and prognostic performance of seven proposed computed tomography (CT)-severity score (CTSS) systems in two different age groups.Design: Retrospective study.Setting: COVID-19 pandemic.Participants: Admitted COVID-19, PCR-positive patients were included, excluding patients with heart failure and significant pre-existing pulmonary disease.Methods: Patients were divided into two age groups: ≥65 years and ≤64 years. Clinical data indicating disease severity at presentation and at peak disease severity were recorded. Initial CT images were scored by two radiologists according to seven CTSSs (CTSS1-CTSS7). Receiver operating characteristic (ROC) analysis for the performance of each CTSS in diagnosing severe/critical disease on admission (triage performance) and at peak disease severity (prognostic performance) was done for the whole cohort and each age group separately.Results: Included were 96 patients. Intraclass correlation coefficient (ICC) between the two radiologists scoring the CT scan images were good for all the CTSSs (ICC=0.764-0.837). In the whole cohort, all CTSSs showed an unsatisfactory area under the curve (AUC) in the ROC curve for triage, excluding CTSS2 (AUC=0.700), and all CTSSs showed acceptable AUCs for prognostic usage (0.759-0.781). In the older group (≥65 years; n=55), all CTSSs excluding CTSS6 showed excellent AUCs for triage (0.804-0.830), and CTSS6 was acceptable (AUC=0.796); all CTSSs showed excellent or outstanding AUCs for prognostication (0.859-0.919). In the younger group (≤64 years; n=41), all CTSSs showed unsatisfactory AUCs for triage (AUC=0.487-0.565) and prognostic usage (AUC=0.668-0.694), excluding CTSS6, showing marginally acceptable AUC for prognostic performance (0.700).Conclusion: Those CTSSs requiring more numerous segmentations, namely CTSS2, CTSS7, and CTSS5 showed the best ICCs; therefore, they are the best when comparison between two separate scores is needed. Irrespective of patients' age, CTSSs show minimal value in triage and acceptable prognostic value in COVID-19 patients. CTSS performance is highly variable in different age groups. It is excellent in those aged ≥65 years, but has little if any value in younger patients. Multicenter studies with larger sample size to evaluate results of this study should be conducted.

AI support for accurate and fast radiological diagnosis of COVID-19: an international multicenter, multivendor CT study.

OBJECTIVES: Differentiation between COVID-19 and community-acquired pneumonia (CAP) in computed tomography (CT) is a task that can be performed by human radiologists and artificial intelligence (AI). The present study aims to (1) develop an AI algorithm for differentiating COVID-19 from CAP and (2) evaluate its performance. (3) Evaluate the benefit of using the AI result as assistance for radiological diagnosis and the impact on relevant parameters such as accuracy of the diagnosis, diagnostic time, and confidence. METHODS: We included n = 1591 multicenter, multivendor chest CT scans and divided them into AI training and validation datasets to develop an AI algorithm (n = 991 CT scans; n = 462 COVID-19, and n = 529 CAP) from three centers in China. An independent Chinese and German test dataset of n = 600 CT scans from six centers (COVID-19 / CAP; n = 300 each) was used to test the performance of eight blinded radiologists and the AI algorithm. A subtest dataset (180 CT scans; n = 90 each) was used to evaluate the radiologists' performance without and with AI assistance to quantify changes in diagnostic accuracy, reporting time, and diagnostic confidence. RESULTS: The diagnostic accuracy of the AI algorithm in the Chinese-German test dataset was 76.5%. Without AI assistance, the eight radiologists' diagnostic accuracy was 79.1% and increased with AI assistance to 81.5%, going along with significantly shorter decision times and higher confidence scores. CONCLUSION: This large multicenter study demonstrates that AI assistance in CT-based differentiation of COVID-19 and CAP increases radiological performance with higher accuracy and specificity, faster diagnostic time, and improved diagnostic confidence. KEY POINTS: • AI can help radiologists to get higher diagnostic accuracy, make faster decisions, and improve diagnostic confidence. • The China-German multicenter study demonstrates the advantages of a human-machine interaction using AI in clinical radiology for diagnostic differentiation between COVID-19 and CAP in CT scans.

Computed tomography and nuclear medicine for the assessment of coronary inflammation: clinical applications and perspectives.

There is increasing evidence that in patients with atherosclerotic cardiovascular disease (ASCVD) under optimal medical therapy, a persisting dysregulation of the lipid and glucose metabolism, associated with adipose tissue dysfunction and inflammation, predicts a substantial residual risk of disease progression and cardiovascular events. Despite the inflammatory nature of ASCVD, circulating biomarkers such as high-sensitivity C-reactive protein and interleukins may lack specificity for vascular inflammation. As known, dysfunctional epicardial adipose tissue (EAT) and pericoronary adipose tissue (PCAT) produce pro-inflammatory mediators and promote cellular tissue infiltration triggering further pro-inflammatory mechanisms. The consequent tissue modifications determine the attenuation of PCAT as assessed and measured by coronary computed tomography angiography (CCTA). Recently, relevant studies have demonstrated a correlation between EAT and PCAT and obstructive coronary artery disease, inflammatory plaque status and coronary flow reserve (CFR). In parallel, CFR is well recognized as a marker of coronary vasomotor function that incorporates the haemodynamic effects of epicardial, diffuse and small-vessel disease on myocardial tissue perfusion. An inverse relationship between EAT volume and coronary vascular function and the association of PCAT attenuation and impaired CFR have already been reported. Moreover, many studies demonstrated that 18F-FDG PET is able to detect PCAT inflammation in patients with coronary atherosclerosis. Importantly, the perivascular FAI (fat attenuation index) showed incremental value for the prediction of adverse clinical events beyond traditional risk factors and CCTA indices by providing a quantitative measure of coronary inflammation. As an indicator of increased cardiac mortality, it could guide early targeted primary prevention in a wide spectrum of patients. In this review, we summarize the current evidence regarding the clinical applications and perspectives of EAT and PCAT assessment performed by CCTA and the prognostic information derived by nuclear medicine.

AIGAN: Attention-encoding Integrated Generative Adversarial Network for the reconstruction of low-dose CT and low-dose PET images.

X-ray computed tomography (CT) and positron emission tomography (PET) are two of the most commonly used medical imaging technologies for the evaluation of many diseases. Full-dose imaging for CT and PET ensures the image quality but usually raises concerns about the potential health risks of radiation exposure. The contradiction between reducing the radiation exposure and remaining diagnostic performance can be addressed effectively by reconstructing the low-dose CT (L-CT) and low-dose PET (L-PET) images to the same high-quality ones as full-dose (F-CT and F-PET). In this paper, we propose an Attention-encoding Integrated Generative Adversarial Network (AIGAN) to achieve efficient and universal full-dose reconstruction for L-CT and L-PET images. AIGAN consists of three modules: the cascade generator, the dual-scale discriminator and the multi-scale spatial fusion module (MSFM). A sequence of consecutive L-CT (L-PET) slices is first fed into the cascade generator that integrates with a generation-encoding-generation pipeline. The generator plays the zero-sum game with the dual-scale discriminator for two stages: the coarse and fine stages. In both stages, the generator generates the estimated F-CT (F-PET) images as like the original F-CT (F-PET) images as possible. After the fine stage, the estimated fine full-dose images are then fed into the MSFM, which fully explores the inter- and intra-slice structural information, to output the final generated full-dose images. Experimental results show that the proposed AIGAN achieves the state-of-the-art performances on commonly used metrics and satisfies the reconstruction needs for clinical standards.

Use of Contrast Media During CT-guided Thermal Ablation of Colorectal Liver Metastasis for Procedure Planning is Associated with Improved Immediate Outcomes.

PURPOSE: The aim of this study was to analyze the impact of using intra-procedural pre-ablation contrast-enhanced CT prior to percutaneous thermal ablation (pre-ablation CECT) of colorectal liver metastases (CLM) on local outcomes. MATERIALS AND METHODS: This retrospective analysis of a prospectively collected liver ablation registry included 144 consecutive patients (median age 57 years IQR [49, 65], 60% men) who underwent 173 CT-guided ablation sessions for 250 CLM between October 2015 and March 2020. In addition to oncologic outcomes, technical success was retrospectively evaluated using a biomechanical deformable image registration software for 3D-minimal ablative margin (3D-MAM) quantification. Bayesian regression was used to estimate effects of pre-ablation CECT on residual unablated tumor, 3D-MAM, and local tumor progression-free survival (LTPFS). RESULTS: Pre-ablation CECT was acquired in 71/173 (41%) sessions. Residual unablated tumor was present in one (0.9%) versus nine tumors (6.6%) ablated with versus without using pre-ablation CECT, respectively (p = 0.024). Pre-ablation CECT use decreased the odds of residual disease on first follow-up by 78% (CI95% [5, 86]) and incomplete ablation (3D-MAM ≤ 0 mm) by 58% (CI95% [13, 122]). The odds ratio for residual unablated tumor for larger CLM was lower when pre-ablation CECT was used (odds ratio 1.0 with pre-ablation CECT vs. 2.52 without). Pre-ablation CECT use was not associated with improvements on LTPFS. CONCLUSIONS: Pre-ablation CECT is associated with improved immediate outcomes by significantly reducing the incidence of residual unablated tumor and by mitigating the risk of incomplete ablation for larger CLM. We recommend performing baseline intra-procedural pre-ablation CECT as a standard imaging protocol. LEVEL OF EVIDENCE: Level 3 (retrospective cohort study).

Myocardial extracellular volume assessment at CT in hospitalized COVID-19 patients with regards to pulmonary embolism.

PURPOSE: To evaluate myocardial status through the assessment of extracellular volume (ECV) calculated at computed tomography (CT) in patients hospitalized for novel coronavirus disease (COVID-19), with regards to the presence of pulmonary embolism (PE) as a risk factor for cardiac dysfunction. METHOD: Hospitalized patients with COVID-19 who underwent contrast-enhanced CT at our institution were retrospectively included in this study and grouped with regards to the presence of PE. Unenhanced and portal venous phase scans were used to calculate ECV by placing regions of interest in the myocardial septum and left ventricular blood pool. ECV values were compared between patients with and without PE, and correlations between ECV values and clinical or technical variables were subsequently appraised. RESULTS: Ninety-four patients were included, 63/94 of whom males (67%), with a median age of 70 (IQR 56-76 years); 28/94 (30%) patients presented with PE. Patients with PE had a higher myocardial ECV than those without (33.5%, IQR 29.4-37.5% versus 29.8%, IQR 25.1-34.0%; p = 0.010). There were no correlations between ECV and patients' age (p = 0.870) or sex (p = 0.122), unenhanced scan voltage (p = 0.822), portal phase scan voltage (p = 0.631), overall radiation dose (p = 0.569), portal phase scan timing (p = 0.460), and contrast agent dose (p = 0.563). CONCLUSIONS: CT-derived ECV could help identify COVID-19 patients at higher risk of cardiac dysfunction, especially when related to PE, to potentially plan a dedicated, patient-tailored clinical approach.

Pulmonary circulation abnormalities in post-acute COVID-19 syndrome: dual-energy CT angiographic findings in 79 patients.

OBJECTIVES: To evaluate the frequency and pattern of pulmonary vascular abnormalities in the year following COVID-19. METHODS: The study population included 79 patients remaining symptomatic more than 6 months after hospitalization for SARS-CoV-2 pneumonia who had been evaluated with dual-energy CT angiography. RESULTS: Morphologic images showed CT features of (a) acute (2/79; 2.5%) and focal chronic (4/79; 5%) PE; and (b) residual post COVID-19 lung infiltration (67/79; 85%). Lung perfusion was abnormal in 69 patients (87.4%). Perfusion abnormalities included (a) perfusion defects of 3 types: patchy defects (n = 60; 76%); areas of non-systematized hypoperfusion (n = 27; 34.2%); and/or PE-type defects (n = 14; 17.7%) seen with (2/14) and without (12/14) endoluminal filling defects; and (b) areas of increased perfusion in 59 patients (74.9%), superimposed on ground-glass opacities (58/59) and vascular tree-in-bud (5/59). PFTs were available in 10 patients with normal perfusion and in 55 patients with abnormal perfusion. The mean values of functional variables did not differ between the two subgroups with a trend toward lower DLCO in patients with abnormal perfusion (74.8 ± 16.7% vs 85.0 ± 8.1). CONCLUSION: Delayed follow-up showed CT features of acute and chronic PE but also two types of perfusion abnormalities suggestive of persistent hypercoagulability as well as unresolved/sequelae of microangiopathy. CLINICAL RELEVANCE STATEMENT: Despite dramatic resolution of lung abnormalities seen during the acute phase of the disease, acute pulmonary embolism and alterations at the level of lung microcirculation can be identified in patients remaining symptomatic in the year following COVID-19. KEY POINTS: • This study demonstrates newly developed proximal acute PE/thrombosis in the year following SARS-CoV-2 pneumonia. • Dual-energy CT lung perfusion identified perfusion defects and areas of increased iodine uptake abnormalities, suggestive of unresolved damage to lung microcirculation. • This study suggests a complementarity between HRCT and spectral imaging for proper understanding of post COVID-19 lung sequelae.

Association of lung recruitment and change in recruitment-to-inflation ratio from supine to prone position in acute respiratory distress syndrome.

Prone positioning is an evidence-based treatment for patients with moderate-to-severe acute respiratory distress syndrome. Lung recruitment has been proposed as one of the mechanisms by which prone positioning reduces mortality in this group of patients. Recruitment-to-inflation ratio (R/I) is a method to measure potential for lung recruitment induced by a change in positive end-expiratory pressure (PEEP) on the ventilator. The association between R/I and potential for lung recruitment in supine and prone position has not been studied with computed tomography (CT) scan imaging. In this secondary analysis, we sought to investigate the correlation between R/I measured in supine and prone position with CT and the potential for lung recruitment as measured by CT scan. Among 23 patients, the median R/I did not significantly change from supine (1.9 IQR 1.6-2.6) to prone position (1.7 IQR 1.3-2.8) (paired t test p = 0.051) but the individual changes correlated with the different response to PEEP. In supine and in prone position, R/I significantly correlated with the proportion of lung tissue recruitment induced by the change of PEEP. Lung tissue recruitment induced by a change of PEEP from 5 to 15 cmH2O was 16% (IQR 11-24%) in supine and 14.3% (IQR 8.4-22.6%) in prone position, as measured by CT scan analysis (paired t test p = 0.56). In this analysis, PEEP-induced recruitability as measured by R/I correlated with PEEP-induced lung recruitment as measured by CT scan, and could help to readjust PEEP in prone position.

A novel CT image de-noising and fusion based deep learning network to screen for disease (COVID-19).

A COVID-19, caused by SARS-CoV-2, has been declared a global pandemic by WHO. It first appeared in China at the end of 2019 and quickly spread throughout the world. During the third layer, it became more critical. COVID-19 spread is extremely difficult to control, and a huge number of suspected cases must be screened for a cure as soon as possible. COVID-19 laboratory testing takes time and can result in significant false negatives. To combat COVID-19, reliable, accurate and fast methods are urgently needed. The commonly used Reverse Transcription Polymerase Chain Reaction has a low sensitivity of approximately 60% to 70%, and sometimes even produces negative results. Computer Tomography (CT) has been observed to be a subtle approach to detecting COVID-19, and it may be the best screening method. The scanned image's quality, which is impacted by motion-induced Poisson or Impulse noise, is vital. In order to improve the quality of the acquired image for post segmentation, a novel Impulse and Poisson noise reduction method employing boundary division max/min intensities elimination along with an adaptive window size mechanism is proposed. In the second phase, a number of CNN techniques are explored for detecting COVID-19 from CT images and an Assessment Fusion Based model is proposed to predict the result. The AFM combines the results for cutting-edge CNN architectures and generates a final prediction based on choices. The empirical results demonstrate that our proposed method performs extensively and is extremely useful in actual diagnostic situations.

Lung Recruitment Assessed by Electrical Impedance Tomography (RECRUIT): A Multicenter Study of COVID-19 Acute Respiratory Distress Syndrome.

Rationale: Defining lung recruitability is needed for safe positive end-expiratory pressure (PEEP) selection in mechanically ventilated patients. However, there is no simple bedside method including both assessment of recruitability and risks of overdistension as well as personalized PEEP titration. Objectives: To describe the range of recruitability using electrical impedance tomography (EIT), effects of PEEP on recruitability, respiratory mechanics and gas exchange, and a method to select optimal EIT-based PEEP. Methods: This is the analysis of patients with coronavirus disease (COVID-19) from an ongoing multicenter prospective physiological study including patients with moderate-severe acute respiratory distress syndrome of different causes. EIT, ventilator data, hemodynamics, and arterial blood gases were obtained during PEEP titration maneuvers. EIT-based optimal PEEP was defined as the crossing point of the overdistension and collapse curves during a decremental PEEP trial. Recruitability was defined as the amount of modifiable collapse when increasing PEEP from 6 to 24 cm H2O (ΔCollapse24-6). Patients were classified as low, medium, or high recruiters on the basis of tertiles of ΔCollapse24-6. Measurements and Main Results: In 108 patients with COVID-19, recruitability varied from 0.3% to 66.9% and was unrelated to acute respiratory distress syndrome severity. Median EIT-based PEEP differed between groups: 10 versus 13.5 versus 15.5 cm H2O for low versus medium versus high recruitability (P < 0.05). This approach assigned a different PEEP level from the highest compliance approach in 81% of patients. The protocol was well tolerated; in four patients, the PEEP level did not reach 24 cm H2O because of hemodynamic instability. Conclusions: Recruitability varies widely among patients with COVID-19. EIT allows personalizing PEEP setting as a compromise between recruitability and overdistension. Clinical trial registered with www.clinicaltrials.gov (NCT04460859).

Pulmonary computed tomographic manifestations of COVID-19 in vaccinated and non-vaccinated patients.

This study aimed to analyze computed tomographic (CT) imaging features of vaccinated and non-vaccinated COVID-19 patients. The study population of this retrospective single-center cohort study consisted of hospitalized COVID-19 patients who received a chest CT at the study site between July 2021 and February 2022. Qualitative scoring systems (RSNA, CO-RADS, COV-RADS), imaging pattern analysis and semi-quantitative scoring of lung changes were assessed. 105 patients (70,47% male, 62.1 ± 16.79 years, 53.3% fully vaccinated) were included in the data analysis. A significant association between vaccination status and the presence of the crazy-paving pattern was observed in univariate analysis and persisted after step-wise adjustment for possible confounders in multivariate analysis (RR: 2.19, 95% CI: [1.23, 2.62], P = 0.024). Scoring systems for probability assessment of the presence of COVID-19 infection showed a significant correlation with the vaccination status in univariate analysis; however, the associations were attenuated after adjustment for virus variant and stage of infection. Semi-quantitative assessment of lung changes due to COVID-19 infection revealed no association with vaccination status. Non-vaccinated patients showed a two-fold higher probability of the crazy-paving pattern compared to vaccinated patients. COVID-19 variants could have a significant impact on the CT-graphic appearance of COVID-19.

COVID-19 with rheumatic diseases: a report of 5 cases.

The coronavirus disease 2019 (COVID-19), the result of an infection with the new virus, SARS-CoV-2, is rapidly spreading worldwide. It is largely unknown whether the occurrence of COVID-19 in patients with rheumatic immune diseases has some specific manifestations, or makes them more prone to rapidly progress into severe COVID-19. In this case report, we describe the clinical features of 5 rheumatic immune disease patients with the concomitant presence of COVID-19. Amongst these patients, 4 had rheumatoid arthritis (RA) and 1 had systemic sclerosis (SSc). Two patients had a history of close contact with a COVID-19 patient. The age of the patients ranged between 51 and 79 years. Fever (80%), cough (80%), dyspnea (40%), and fatigue (20%) were the most common presenting symptoms. Laboratory investigations revealed leukopenia and lymphopenia in 2 patients. In all the patients, chest computerized tomography (CT) revealed patchy ground glass opacities in the lungs. During the hospital stay, the condition of two patients remained the same (i.e., mild COVID-19), two patients progressed to the severe COVID-19, and one patient worsened to the critically ill COVID-19. These patients were treated with antiviral agents for COVID-19, antibiotics for secondary bacterial infections, and immunomodulatory agents for rheumatic immune diseases. All the patients responded well, were cured of COVID-19, and subsequently discharged.

Computed tomography-based COVID-19 triage through a deep neural network using mask-weighted global average pooling.

Background: There is an urgent need to find an effective and accurate method for triaging coronavirus disease 2019 (COVID-19) patients from millions or billions of people. Therefore, this study aimed to develop a novel deep-learning approach for COVID-19 triage based on chest computed tomography (CT) images, including normal, pneumonia, and COVID-19 cases. Methods: A total of 2,809 chest CT scans (1,105 COVID-19, 854 normal, and 850 non-3COVID-19 pneumonia cases) were acquired for this study and classified into the training set (n = 2,329) and test set (n = 480). A U-net-based convolutional neural network was used for lung segmentation, and a mask-weighted global average pooling (GAP) method was proposed for the deep neural network to improve the performance of COVID-19 classification between COVID-19 and normal or common pneumonia cases. Results: The results for lung segmentation reached a dice value of 96.5% on 30 independent CT scans. The performance of the mask-weighted GAP method achieved the COVID-19 triage with a sensitivity of 96.5% and specificity of 87.8% using the testing dataset. The mask-weighted GAP method demonstrated 0.9% and 2% improvements in sensitivity and specificity, respectively, compared with the normal GAP. In addition, fusion images between the CT images and the highlighted area from the deep learning model using the Grad-CAM method, indicating the lesion region detected using the deep learning method, were drawn and could also be confirmed by radiologists. Conclusions: This study proposed a mask-weighted GAP-based deep learning method and obtained promising results for COVID-19 triage based on chest CT images. Furthermore, it can be considered a convenient tool to assist doctors in diagnosing COVID-19.

A case of pulmonary infection with Prevotella melanogenica having the paving stone symptom.

Prevotella melanogenica is a typical organism present in the human oral cavity and female reproductive tract, which is responsible for causing periodontal disease and the inflammation of the female reproductive tract. The present report discusses the case of a young female patient who presented with cough and fever as the main clinical symptoms. Computed Tomography (CT) revealed multiple clusters of ground glass density shadows in both lungs, with network-like and paving stone-like changes. The alveolar lavage fluid was collected for next-generation sequencing, which revealed the presence of Prevotella melanogenica. The patient received treatments, CT revealed that the density of multiple flakes of ground glass density in both lungs was lower than the previously observed density. Prevotella melanogenica pneumonia is rare, and the paving stone symptom observed in CT is not specific. Therefore, the case reported here provides a novel perspective regarding the diagnosis of pneumonia.