Noncontrast Chest Computed Tomography Imaging Research Articles

An organ-effective modulation for non-contrast chest computed tomography imaging: effect on image quality and thyroid exposure reduction.

We investigate the efficacy of organ-effective modulation (OEM) technique for thyroid dose reduction among various body habitus and its impact on image quality in chest non-contrast computed tomography (CT). We prospectively enrolled 64 patients who underwent non-contrast chest CT from January to May 2022. The skin-absorbed radiation dose over the thyroid (Dthyroid) was obtained using a thermoluminescence dosemeter. Signal-to-noise ratio and image noise was also quantitatively assessed. In subjective analyses, two radiologists independently evaluated images based on a 5-point scale. The OEM group showed a markedly decrease in Dthyroid when compared with the non-OEM group (p<0.05). No significant difference was observed regarding the image noise (p<0.05), except for the ventral air space. The subjective scores of two radiologists showed no significant differences between the non-OEM and OEM groups. OEM can effectively reduce the radiation exposure of thyroid without compromising on image quality in non-contrast chest CT.

Unrecognized Patent Foramen Ovale in Patient With Sinus Venosus-Type Atrial Septal Defect With Partial Anomalous Pulmonary Venous Return.

Unrecognized Patent Foramen Ovale in Patient With Sinus Venosus-Type Atrial Septal Defect With Partial Anomalous Pulmonary Venous Return.

Abstract 10982: A Novel Index of Coronary Artery Calcium Derived From Preoperative Non-Gated Chest Computed Tomography Correlates With Traditional Estimates of Perioperative Risk

Introduction: Cardiovascular events are feared complications of non-cardiac surgery. Prior studies demonstrate associations between coronary artery calcium (CAC) from ECG-gated computed tomography (CT) imaging and perioperative major adverse cardiac events. We sought to evaluate correlations between an index of CAC severity from preoperative non-gated CT imaging and traditional estimates of perioperative risk. Methods We identified consecutive adults age ≥45 years who underwent in-hospital, major non-cardiac surgery across a large urban health system between 2016 and 2020 and had non-gated (contrast or non-contrast) chest CT imaging performed within 1 year prior to surgery. Patients with prior coronary revascularization or heart valve surgery were excluded. CAC severity in each coronary artery was assessed and graded from absent to severe using a 0-3 scale by physicians blinded to clinical data. An index of CAC was computed as the sum of the scores for each coronary artery. The Revised Cardiac Risk Index (RCRI) was determined for each patient. Correlations between the CAC index and RCRI were assessed using Spearman Correlation Coefficient (𝜌;). Results A total of 2,560 patients met inclusion criteria - mean age 68.1 ± 12.3 years, 49.8% female, 60% white (Figure 1A). The median pre-operative RCRI score was 1 [IQR 0-1, range 0-6] (Figure 1B). A majority of patients (64.3%) had non-contrast chest CT imaging. The median CAC index was 1 [IQR 0-3, range 0-9] (Figure 1C). A correlation between the CAC index and RCRI (Coefficient (𝜌;): 0.209, p&lt;0.001) was observed, with stepwise increases in the CAC index when stratified by pre-operative RCRI (Figure 1D). Conclusions A pragmatic, novel index of CAC derived from existing non-gated chest CT imaging correlates with pre-operative RCRI. As the RCRI is a validated preoperative risk stratification tool, this finding demonstrates the potential to leverage pre-existing, non-gated CT imaging to inform preoperative risk assessment.

How are Lung Volume and Respiratory Muscles Affected in Non-Severe Patients With Covid-19?

Aim: Coronavirus-2019 (Covid-19) primarily affects the respiratory system, and how it affects respiratory muscles and lung volume is still not fully understood. Our study aimed to assess the time-dependent changes that may occur in lungs and respiratory muscle sizes on chest computed tomography (CT) in adult coronavirus disease 2019 (COVID-19) patients.Material and Methods: The clinical and radiological records of 101 adult patients who had at least two non-contrast chest CT images in stage 1 (0-4 days) and stage 6 (&amp;gt;28 days) were collected retrospectively. ImFusion Suite program were used to calculate lung volumes, and the cross-sectional areas of the pectoral and intercostal muscles were also calculated using with “ImageJ” program.Results: One-hundred one patients (51 females, 50.49%) were included in the study. ANCOVA revealed a significant stage*side interaction effect regarding cross-sectional area (CSA) of intercostal muscles [(p=.010; η²p=.064)]. There was no significant difference between the two stages in terms of CSA of pectoralis major and minor muscles [(p=.314; η2p=.010), (p=.644; η²p=.002)] respectively and lung volume [(p=.340; η²p=.009)].Conclusion: Covid-19 pneumonia causes an atrophy in respiratory muscles. However, it seems to have a nonsignificant effect on auxiliary respiratory muscles and lung volume. Further investigation of respiratory muscles and respiratory muscle training to reduce the risk of serious complications during viral infections are required.

Chest Computed Tomography Severity Score to Predict Adverse Outcomes of Patients with COVID-19.

BackgroundThe novel coronavirus disease 2019 (COVID-19) continues to wreak havoc worldwide. This study assessed the ability of chest computed tomography (CT) severity score (CSS) to predict intensive care unit (ICU) admission and mortality in patients with COVID-19 pneumonia.Materials and MethodsA total of 192 consecutive patients with COVID-19 pneumonia aged more than 20 years and typical CT findings and reverse-transcription polymerase chain reaction positive admitted in a tertiary hospital were included. Clinical symptoms at admission and short-term outcome were obtained. A semi-quantitative scoring system was used to evaluate the parenchymal involvement. The association between CSS, disease severity, and outcomes were evaluated. Prediction of CSS was assessed with the area under the receiver-operating characteristic (ROC) curves.ResultsThe incidence of admission to ICU was 22.8% in men and 14.1% in women. CSS was related to ICU admission and mortality. Areas under the ROC curves were 0.764 for total CSS. Using a stepwise binary logistic regression model, gender, age, oxygen saturation, and CSS had a significant independent relationship with ICU admission and death. Patients with CSS ≥12.5 had about four-time risk of ICU admission and death (odds ratio 1.66, 95% confidence interval 1.66 – 9.25). The multivariate regression analysis showed the superiority of CSS over other clinical information and co-morbidities.ConclusionCSS was a strong predictor of progression to ICU admission and death and there was a substantial role of non-contrast chest CT imaging in the presence of typical features for COVID-19 pneumonia as a reliable predictor of clinical severity and patient’s outcome.

Epicardial adipose tissue is associated with extent of pneumonia and adverse outcomes in patients with COVID-19

AimWe sought to examine the association of epicardial adipose tissue (EAT) quantified on chest computed tomography (CT) with the extent of pneumonia and adverse outcomes in patients with coronavirus disease 2019 (COVID-19). MethodsWe performed a post-hoc analysis of a prospective international registry comprising 109 consecutive patients (age 64 ± 16 years; 62% male) with laboratory-confirmed COVID-19 and noncontrast chest CT imaging. Using semi-automated software, we quantified the burden (%) of lung abnormalities associated with COVID-19 pneumonia. EAT volume (mL) and attenuation (Hounsfield units) were measured using deep learning software. The primary outcome was clinical deterioration (intensive care unit admission, invasive mechanical ventilation, or vasopressor therapy) or in-hospital death. ResultsIn multivariable linear regression analysis adjusted for patient comorbidities, the total burden of COVID-19 pneumonia was associated with EAT volume (β = 10.6, p = 0.005) and EAT attenuation (β = 5.2, p = 0.004). EAT volume correlated with serum levels of lactate dehydrogenase (r = 0.361, p = 0.001) and C-reactive protein (r = 0.450, p < 0.001). Clinical deterioration or death occurred in 23 (21.1%) patients at a median of 3 days (IQR 1–13 days) following the chest CT. In multivariable logistic regression analysis, EAT volume (OR 5.1 [95% CI 1.8–14.1] per doubling p = 0.011) and EAT attenuation (OR 3.4 [95% CI 1.5–7.5] per 5 Hounsfield unit increase, p = 0.003) were independent predictors of clinical deterioration or death, as was total pneumonia burden (OR 2.5, 95% CI 1.4–4.6, p = 0.002), chronic lung disease (OR 1.3 [95% CI 1.1–1.7], p = 0.011), and history of heart failure (OR 3.5 [95% 1.1–8.2], p = 0.037). ConclusionsEAT measures quantified from chest CT are independently associated with extent of pneumonia and adverse outcomes in patients with COVID-19, lending support to their use in clinical risk stratification.

Automated Lung Segmentation on Chest Computed Tomography Images with Extensive Lung Parenchymal Abnormalities Using a Deep Neural Network.

ObjectiveWe aimed to develop a deep neural network for segmenting lung parenchyma with extensive pathological conditions on non-contrast chest computed tomography (CT) images.Materials and MethodsThin-section non-contrast chest CT images from 203 patients (115 males, 88 females; age range, 31–89 years) between January 2017 and May 2017 were included in the study, of which 150 cases had extensive lung parenchymal disease involving more than 40% of the parenchymal area. Parenchymal diseases included interstitial lung disease (ILD), emphysema, nontuberculous mycobacterial lung disease, tuberculous destroyed lung, pneumonia, lung cancer, and other diseases. Five experienced radiologists manually drew the margin of the lungs, slice by slice, on CT images. The dataset used to develop the network consisted of 157 cases for training, 20 cases for development, and 26 cases for internal validation. Two-dimensional (2D) U-Net and three-dimensional (3D) U-Net models were used for the task. The network was trained to segment the lung parenchyma as a whole and segment the right and left lung separately. The University Hospitals of Geneva ILD dataset, which contained high-resolution CT images of ILD, was used for external validation.ResultsThe Dice similarity coefficients for internal validation were 99.6 ± 0.3% (2D U-Net whole lung model), 99.5 ± 0.3% (2D U-Net separate lung model), 99.4 ± 0.5% (3D U-Net whole lung model), and 99.4 ± 0.5% (3D U-Net separate lung model). The Dice similarity coefficients for the external validation dataset were 98.4 ± 1.0% (2D U-Net whole lung model) and 98.4 ± 1.0% (2D U-Net separate lung model). In 31 cases, where the extent of ILD was larger than 75% of the lung parenchymal area, the Dice similarity coefficients were 97.9 ± 1.3% (2D U-Net whole lung model) and 98.0 ± 1.2% (2D U-Net separate lung model).ConclusionThe deep neural network achieved excellent performance in automatically delineating the boundaries of lung parenchyma with extensive pathological conditions on non-contrast chest CT images.

Coronary artery calcium scoring on non-gated, non-contrast chest computed tomography (CT) using wide-detector, high-pitch and fast gantry rotation: comparison with dedicated calcium scoring CT.

BackgroundOur study assessed the reliability of non-gated, non-contrast chest computed tomography (NCCT) (with high pitch, wide coverage, and fast gantry rotation time, reconstructed at various slice thicknesses), compared with the electrocardiography (ECG)-gated calcium scoring cardiac computed tomography (CaCT), for quantifying coronary artery calcification (CAC).MethodsPatients aged ≥50 years who required clinical NCCT were prospectively enrolled. All CT scans were performed with 256-detector rows; z-axis coverage, 8 cm; pitch, 1.5; and gantry rotation time, 280 ms (table feed, 42.86 cm/s). NCCT was followed by ECG-gated CaCT. The NCCT images were reconstructed at 0.625-, 1.25-, and 2.5-mm slice intervals. The CAC score was calculated on four sets of CT images with a commercially available software using the Agatston method. The CAC scores were divided into four standard Agatston scoring categories (Agatston scores: 0, 1–100, 101–400, and >400). The inter-observer and inter-technique agreements were evaluated for the CAC scores.ResultsTwenty-six patients (M:F, 14:12; mean age, 66.04±6.97 years) were evaluated. Agatston scores showed near-perfect correlation between CaCT and NCCT for each slice thickness. On Bland-Altman analysis, the mean differences of Agatston scores between CaCT and NCCT (slice thicknesses: 0.625, 1.25, and 2.5 mm) were 37.54, 6.67, and −41.04, respectively. Inter-technique concordance was high for the four Agatston scoring categories with linear-weighted kappa values of 0.599, 0.609, and 0.597 for NCCT (slice thicknesses: 0.625, 1.25, and 2.5 mm, respectively). NCCT with 1.25-mm slice thickness showed the strongest correlation with CaCT.ConclusionsCAC quantification with NCCT using a wide detector, high pitch, and high temporal resolution scanning modes correlates very highly with ECG-gated CaCT, and 1.25-mm slice thickness NCCT images are more reliable than other NCCT images.

A CT-based radiomics nomogram for prediction of lung adenocarcinomas and granulomatous lesions in patient with solitary sub-centimeter solid nodules

PurposeTo develop a radiomics nomogram based on computed tomography (CT) images that can help differentiate lung adenocarcinomas and granulomatous lesions appearing as sub-centimeter solid nodules (SCSNs).Materials and methodsThe records of 214 consecutive patients with SCSNs that were surgically resected and histologically confirmed as lung adenocarcinomas (n = 112) and granulomatous lesions (n = 102) from 2 medical institutions between October 2011 and June 2019 were retrospectively analyzed. Patients from center 1 ware enrolled as training cohort (n = 150) and patients from center 2 were included as external validation cohort (n = 64), respectively. Radiomics features were extracted from non-contrast chest CT images preoperatively. The least absolute shrinkage and selection operator (LASSO) regression model was used for radiomics feature extraction and radiomics signature construction. Clinical characteristics, subjective CT findings, and radiomics signature were used to develop a predictive radiomics nomogram. The performance was examined by assessment of the area under the receiver operating characteristic curve (AUC).ResultsLung adenocarcinoma was significantly associated with an irregular margin and lobulated shape in the training set (p = 0.001, < 0.001) and external validation set (p = 0.016, = 0.018), respectively. The radiomics signature consisting of 22 features was significantly associated with lung adenocarcinomas of SCSNs (p < 0.001). The radiomics nomogram incorporated the radiomics signature, gender and lobulated shape. The AUCs of combined model in the training and external validation dataset were 0.885 (95% confidence interval [CI]: 0.823–0.931), 0.808 (95% CI: 0.690–0.896), respectively. Decision curve analysis (DCA) demonstrated that the radiomics nomogram was clinically useful.ConclusionA radiomics signature based on non-enhanced CT has the potential to differentiate between lung adenocarcinomas and granulomatous lesions. The radiomics nomogram incorporating the radiomics signature and subjective findings may facilitate the individualized, preoperative treatment in patients with SCSNs.

Diagnostic errors of coronavirus infection: Role of fluorodeoxyglucose positron emission tomography/computed tomography scan

Background: In the era of coronavirus disease 2019 (COVID-19) pandemic, many cases may be misdiagnosed based on their semiology, laboratory tests or chest computed tomography (CT) images and further evaluation might be helpful in appropriately selected cases. Methods:We are discussing usefulness of fluorodeoxyglucose (FDG) positron emission tomography PET/CT scan in appropriately selected suspicious cases of COVID-19 infection. We are discussing two interesting cases on how18F-FDG PET/CT scan might be helpful to avoid COVID-19 infection diagnostic error. Both cases had clinical symptoms suggestive for COVID-19 infection with nonspecific chest CT scan findings including lung nodules, ground glass opacities (GGOs), consolidations, and mosaic perfusion patterns. This case series was approved by our institutional review board and informed consent obtained from both patients. Results: In one case,18F-FDG PET/CT images demonstrated hypermetabolic mostly peripheral GGOs and nodules in both lungs with subsequent evaluation confirming COVID-19 infection. The second case demonstrated right perihilar consolidation, not well appreciated on noncontrast chest CT images in addition to GGOs and further evaluation confirmed diagnosis of poorly differentiated squamous cell carcinoma of the lung. Conclusion: During COVID-19 pandemic, many cases might be misdiagnosed by either clinicians or radiologists. In appropriately selected cases, FDG PET/CT scan may be helpful during COVID-19 outbreak to avoid diagnostic errors.

Management of incidental anterior mediastinal lesions: summary of relevant studies.

With the increasing use of chest computed tomography (CT) imaging, the detection of asymptomatic incidental lesions in the anterior mediastinum has become more frequent. The prevalence of incidental nodular lesions in the anterior mediastinum is 0.49% to 0.89%. Most of these lesions manifest as soft tissue nodules measuring between 10 and 30 mm on non-contrast CT images. Thymic epithelial tumors are mainly responsible for larger lesions, while smaller lesions are primarily benign cysts. Most incidental thymic epithelial tumors are early-stage and have a favorable outcome. During follow-up, most lesions are stable, but some show indolent growth. Incidental lesions can be managed by a conservative patient-tailored approach with regular follow-up and the use of non-invasive imaging modalities such as magnetic resonance imaging.

Mosaic attenuation pattern in non-contrast computed tomography for the assessment of pulmonary perfusion in chronic thromboembolic pulmonary hypertension.

Chronic thromboembolic pulmonary hypertension (CTEPH) is difficult to diagnose as patients rarely present with specific symptoms. However, a mosaic attenuation pattern (MAP) in chest computed tomography (CT) suggests CTEPH. Areas of increased attenuation are not always visible using default CT settings for the lung. Thus, we examined the utility of non-contrast CT imaging with new settings focusing on MAP (CTMosaic) for the assessment of pulmonary perfusion in patients with CTEPH. The regional perfusion defects visualized using CTMosaic and single-photon-emission CT with fusion of CT images (SPECT/CT) were compared. Twenty-seven patients with CTEPH (20 women; aged 62.8±7.9 years) underwent imaging with non-contrast CT and SPECT/CT. We converted non-contrast mediastinal CT images into various CT window settings to identify the MAP, and the CT window setting that could most easily identify the MAP was defined as CTMosaic. We then scored and compared lung segments depending on the degree of perfusion on CTMosaic and SPECT/CT. CTMosaic was identified as the CT window setting in which the window level was -800 Hounsfield units (HU), and the window width was 200 HU. Using CTMosaic, MAP was detected in 366 of 486 segments (75.3%). The agreement between CTMosaic and perfusion defects on SPECT/CT was 84.9%. Weighted kappa statistics demonstrated a good agreement between the two examinations (κ=0.605, 95% confidence interval, 0.502-0.707). The CTMosaic setting can easily identify an MAP in CTEPH patients. Therefore, this may be useful as a simple and cost-effective evaluation method for blood distribution in patients with CTEPH.

Automatic reconstruction of the arterial and venous trees on volumetric chest CT

This paper introduces a novel approach to classify pulmonary arteries and veins from volumetric chest computed tomography (CT) images. Although there is known to be a relationship between the alteration of vessel distributions and the progress of various pulmonary diseases, there has been relatively little research on the quantification of pulmonary vessels in vivo due to morphological difficulties. In particular, there have been few efforts to quantify the morphology and distribution of only arteries or veins through automated algorithms despite the clinical importance of such work. In this study, the authors classify different types of vessels by constructing a tree structure from vascular points while minimizing the construction cost using the vascular geometries and features of CT images. First, a vascular point set is extracted from an input volume and the weights of the points are calculated using the intensity, distance from the boundaries, and the Laplacian of the distance field. The tree construction cost is then defined as the summation of edge connection costs depending on the vertex weights. As a solution, the authors can obtain a minimum spanning tree whose branches correspond to different vessels. By cutting the edges in the mediastinal region, branches can be separated. From the root points of each branch, the cut region is regrouped toward the entries of pulmonary vessels in the same framework of the initial tree construction. After merging branches with the same orientation as much as possible, it can be determined manually whether a given vessel is an artery or vein. Our approach can handle with noncontrast CT images as well as vascular contrast enhanced images. For the validation, mathematical virtual phantoms and ten chronic obstructive pulmonary disease (COPD) noncontrast volumetric chest CT scans with submillimeter thickness were used. Based on experimental findings, the suggested approach shows 9.18 ± 0.33 (mean ± SD) visual scores for ten datasets, 91% and 98% quantitative accuracies for two cases, a result which is clinically acceptable in terms of classification capability. This automatic classification approach with minimal user interactions may be useful in assessing many pulmonary disease, such as pulmonary hypertension, interstitial lung disease and COPD.