Galectin-10 in serum extracellular vesicles reflects asthma pathophysiology.

BACKGROUND: Novel biomarkers (BMs) are urgently needed for bronchial asthma (BA) with various phenotypes and endotypes. OBJECTIVE: We sought to identify novel BMs reflecting tissue pathology from serum extracellular vesicles (EVs). METHODS: We performed data-independent acquisition of serum EVs from 4 healthy controls, 4 noneosinophilic asthma (NEA) patients, and 4 eosinophilic asthma (EA) patients to identify novel BMs for BA. We confirmed EA-specific BMs via data-independent acquisition validation in 61 BA patients and 23 controls. To further validate these findings, we performed data-independent acquisition for 6 patients with chronic rhinosinusitis without nasal polyps and 7 patients with chronic rhinosinusitis with nasal polyps. RESULTS: We identified 3032 proteins, 23 of which exhibited differential expression in EA. Ingenuity pathway analysis revealed that protein signatures from each phenotype reflected disease characteristics. Validation revealed 5 EA-specific BMs, including galectin-10 (Gal10), eosinophil peroxidase, major basic protein, eosinophil-derived neurotoxin, and arachidonate 15-lipoxygenase. The potential of Gal10 in EVs was superior to that of eosinophils in terms of diagnostic capability and detection of airway obstruction. In rhinosinusitis patients, 1752 and 8413 proteins were identified from EVs and tissues, respectively. Among 11 BMs identified in EVs and tissues from patients with chronic rhinosinusitis with nasal polyps, 5 (including Gal10 and eosinophil peroxidase) showed significant correlations between EVs and tissues. Gal10 release from EVs was implicated in eosinophil extracellular trapped cell death in vitro and in vivo. CONCLUSION: Novel BMs such as Gal10 from serum EVs reflect disease pathophysiology in BA and may represent a new target for liquid biopsy approaches.

Imaging of Pulmonary Fibrosis: An Update, From the AJR Special Series on Imaging of Fibrosis.

Pulmonary fibrosis is recognized as occurring in association with a wide and increasing array of conditions, and it presents with a spectrum of chest CT appearances. Idiopathic pulmonary fibrosis (IPF), which corresponds histologically with usual interstitial pneumonia and represents the most common idiopathic interstitial pneumonia, is a chronic progressive fibrotic interstitial lung disease (ILD) of unknown cause. Progressive pulmonary fibrosis (PPF) describes the radiologic development of pulmonary fibrosis in patients with ILD of a known or unknown cause other than IPF. The recognition of PPF impacts management of patients with ILD-for example, in guiding initiation of antifibrotic therapy. Interstitial lung abnormalities are an incidental CT finding in patients without suspected ILD and may represent an early intervenable form of pulmonary fibrosis. Traction bronchiectasis and/or bronchiolectasis, when detected in the setting of chronic fibrosis, is generally considered evidence of irreversible disease, and progression predicts worsening mortality risk. Awareness of the association between pulmonary fibrosis and connective tissue diseases, particularly rheumatoid arthritis, is increasing. This review provides an update on the imaging of pulmonary fibrosis, with attention given to recent advances in disease understanding with relevance to radiologic practice. The essential role of a multidisciplinary approach to clinical and radiologic data is highlighted.

Suspected Interstitial Lung Disease in COPDGene Study.

Rationale: Although interstitial lung abnormalities (ILA), specific patterns of incidentally-detected abnormal density on computed tomography, have been associated with abnormal lung function and increased mortality, it is unclear if a subset with incidental interstitial lung disease (ILD) accounts for these adverse consequences. Objectives: To define the prevalence and risk factors of suspected ILD and assess outcomes. Methods: Suspected ILD was evaluated in the COPDGene (Chronic Obstructive Pulmonary Disease Genetic Epidemiology) study, defined as ILA and at least one additional criterion: definite fibrosis on computed tomography, FVC less than 80% predicted, or DLCO less than 70% predicted. Multivariable linear, longitudinal, and Cox proportional hazards regression models were used to assess associations with St. George's Respiratory Questionnaire, 6-minute-walk test, supplemental oxygen use, respiratory exacerbations, and mortality. Measurements and Main Results: Of 4,361 participants with available data, 239 (5%) had evidence for suspected ILD, whereas 204 (5%) had ILA without suspected ILD. In multivariable analyses, suspected ILD was associated with increased St. George's Respiratory Questionnaire score (mean difference [MD], 3.9 points; 95% confidence interval [CI], 0.6-7.1; P = 0.02), reduced 6-minute-walk test (MD, -35 m; 95% CI, -56 m to -13 m; P = 0.002), greater supplemental oxygen use (odds ratio [OR], 2.3; 95% CI, 1.1-5.1; P = 0.03) and severe respiratory exacerbations (OR, 2.9; 95% CI, 1.1-7.5; P = 0.03), and higher mortality (hazard ratio, 2.4; 95% CI, 1.2-4.6; P = 0.01) compared with ILA without suspected ILD. Risk factors associated with suspected ILD included self-identified Black race (OR, 2.0; 95% CI, 1.1-3.3; P = 0.01) and pack-years smoking history (OR, 1.2; 95% CI, 1.1-1.3; P = 0.0005). Conclusions: Suspected ILD is present in half of those with ILA in COPDGene and is associated with exercise decrements and increased symptoms, supplemental oxygen use, severe respiratory exacerbations, and mortality.

Prevalence and mortality associations of interstitial lung abnormalities in rheumatoid arthritis within a multicentre prospective cohort of smokers.

OBJECTIVE: To investigate the prevalence and mortality impact of interstitial lung abnormalities (ILAs) in RA and non-RA comparators. METHODS: We analysed associations between ILAs, RA, and mortality in COPDGene, a multicentre prospective cohort study of current and past smokers, excluding known interstitial lung disease (ILD) or bronchiectasis. All participants had research chest high-resolution CT (HRCT) reviewed by a sequential reading method to classify ILA as present, indeterminate or absent. RA cases were identified by self-report RA and DMARD use; non-RA comparators had neither an RA diagnosis nor used DMARDs. We examined the association and mortality risk of RA and ILA using multivariable logistic regression and Cox regression. RESULTS: We identified 83 RA cases and 8725 non-RA comparators with HRCT performed for research purposes. ILA prevalence was 16.9% in RA cases and 5.0% in non-RA comparators. After adjusting for potential confounders, including genetics, current/past smoking and other lifestyle factors, ILAs were more common among those with RA compared with non-RA [odds ratio 4.76 (95% CI 2.54, 8.92)]. RA with ILAs or indeterminate for ILAs was associated with higher all-cause mortality compared with non-RA without ILAs [hazard ratio (HR) 3.16 (95% CI 2.11, 4.74)] and RA cases without ILA [HR 3.02 (95% CI 1.36, 6.75)]. CONCLUSIONS: In this cohort of smokers, RA was associated with ILAs and this persisted after adjustment for current/past smoking and genetic/lifestyle risk factors. RA with ILAs in smokers had a 3-fold increased all-cause mortality, emphasizing the importance of further screening and treatment strategies for preclinical ILD in RA.

Radiologists with and without deep learning-based computer-aided diagnosis: comparison of performance and interobserver agreement for characterizing and diagnosing pulmonary nodules/masses.

OBJECTIVES: To compare the performance of radiologists in characterizing and diagnosing pulmonary nodules/masses with and without deep learning (DL)-based computer-aided diagnosis (CAD). METHODS: We studied a total of 101 nodules/masses detected on CT performed between January and March 2018 at Osaka University Hospital (malignancy: 55 cases). SYNAPSE SAI Viewer V1.4 was used to analyze the nodules/masses. In total, 15 independent radiologists were grouped (n = 5 each) according to their experience: L (< 3 years), M (3-5 years), and H (> 5 years). The likelihoods of 15 characteristics, such as cavitation and calcification, and the diagnosis (malignancy) were evaluated by each radiologist with and without CAD, and the assessment time was recorded. The AUCs compared with the reference standard set by two board-certified chest radiologists were analyzed following the multi-reader multi-case method. Furthermore, interobserver agreement was compared using intraclass correlation coefficients (ICCs). RESULTS: The AUCs for ill-defined boundary, irregular margin, irregular shape, calcification, pleural contact, and malignancy in all 15 radiologists, irregular margin and irregular shape in L and ill-defined boundary and irregular margin in M improved significantly (p < 0.05); no significant improvements were found in H. L showed the greatest increase in the AUC for malignancy (not significant). The ICCs improved in all groups and for nearly all items. The median assessment time was not prolonged by CAD. CONCLUSIONS: DL-based CAD helps radiologists, particularly those with < 5 years of experience, to accurately characterize and diagnose pulmonary nodules/masses, and improves the reproducibility of findings among radiologists. KEY POINTS: • Deep learning-based computer-aided diagnosis improves the accuracy of characterizing nodules/masses and diagnosing malignancy, particularly by radiologists with < 5 years of experience. • Computer-aided diagnosis increases not only the accuracy but also the reproducibility of the findings across radiologists.

Traction Bronchiectasis/Bronchiolectasis on CT Scans in Relationship to Clinical Outcomes and Mortality: The COPDGene Study.

Background The clinical impact of interstitial lung abnormalities (ILAs) on poor prognosis has been reported in many studies, but risk stratification in ILA will contribute to clinical practice. Purpose To investigate the association of traction bronchiectasis/bronchiolectasis index (TBI) with mortality and clinical outcomes in individuals with ILA by using the COPDGene cohort. Materials and Methods This study was a secondary analysis of prospectively collected data. Chest CT scans of participants with ILA for traction bronchiectasis/bronchiolectasis were evaluated and outcomes were compared with participants without ILA from the COPDGene study (January 2008 to June 2011). TBI was classified as follows: TBI-0, ILA without traction bronchiectasis/bronchiolectasis; TBI-1, ILA with bronchiolectasis but without bronchiectasis or architectural distortion; TBI-2, ILA with mild to moderate traction bronchiectasis; and TBI-3, ILA with severe traction bronchiectasis and/or honeycombing. Clinical outcomes and overall survival were compared among the TBI groups and the non-ILA group by using multivariable linear regression model and Cox proportional hazards model, respectively. Results Overall, 5295 participants (median age, 59 years; IQR, 52-66 years; 2779 men) were included, and 582 participants with ILA and 4713 participants without ILA were identified. TBI groups were associated with poorer clinical outcomes such as quality of life scores in the multivariable linear regression model (TBI-0: coefficient, 3.2 [95% CI: 0.6, 5.7; P = .01]; TBI-1: coefficient, 3.3 [95% CI: 1.1, 5.6; P = .003]; TBI-2: coefficient, 7.6 [95% CI: 4.0, 11; P < .001]; TBI-3: coefficient, 32 [95% CI: 17, 48; P < .001]). The multivariable Cox model demonstrated that ILA without traction bronchiectasis (TBI-0-1) and with traction bronchiectasis (TBI-2-3) were associated with shorter overall survival (TBI-0-1: hazard ratio [HR], 1.4 [95% CI: 1.0, 1.9; P = .049]; TBI-2-3: HR, 3.8 [95% CI: 2.6, 5.6; P < .001]). Conclusion Traction bronchiectasis/bronchiolectasis was associated with poorer clinical outcomes compared with the group without interstitial lung abnormalities; TBI-2 and 3 were associated with shorter survival. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Lee and Im in this issue.

Vascular imaging of patients with refractory Takayasu arteritis treated with tocilizumab: post hoc analysis of a randomized controlled trial.

OBJECTIVES: Tocilizumab, an anti-IL-6 receptor antibody, was investigated in patients with refractory Takayasu arteritis (TAK) in a phase 3 randomized controlled trial. In this post hoc analysis, we investigated whether tocilizumab treatment inhibited the progression of vascular lesions caused by TAK in these patients. METHODS: Included patients received at least one dose of tocilizumab and underwent CT at baseline and at week 48 after tocilizumab initiation. Three radiologists not involved in the original trial independently evaluated the CT images. Twenty-two arteries from each patient were assessed for change from baseline in wall thickness (primary endpoint), dilatation/aneurysm, stenosis/occlusion or wall enhancement for at least 96 weeks after tocilizumab initiation. Patient-level assessments were also conducted. RESULTS: In 28 patients, 86.7% of 22 arteries had improved or stable wall thickness at week 96. Proportions of patients with improved or stable, partially progressed or newly progressed lesions were 57.1%, 10.7% and 28.6%, respectively, for wall thickness; proportions with improved or stable lesions were 92.9% for dilatation/aneurysm, and 85.7% for stenosis/occlusion. Patients with newly progressed lesions, reflecting more refractory disease, were prescribed glucocorticoids at dosages that could not be reduced below 0.1 mg/kg/day at week 96. CONCLUSIONS: Approximately 60% of patients with TAK did not experience progression in wall thickness within 96 weeks after initiation of tocilizumab treatment. Few patients experienced progressed dilatation/aneurysm, or stenosis/occlusion. Wall thickness progression likely resulted from refractory TAK. Patients who experience this should be monitored regularly by imaging, and additional glucocorticoid or immunosuppressive treatment should be considered to avoid vascular progression. TRIAL REGISTRATION: Japan Pharmaceutical Information Centre number, JapicCTI-142616.

Blood gene expression risk profiles and interstitial lung abnormalities: COPDGene and ECLIPSE cohort studies.

BACKGROUND: Interstitial lung abnormalities (ILA) are radiologic findings that may progress to idiopathic pulmonary fibrosis (IPF). Blood gene expression profiles can predict IPF mortality, but whether these same genes associate with ILA and ILA outcomes is unknown. This study evaluated if a previously described blood gene expression profile associated with IPF mortality is associated with ILA and all-cause mortality. METHODS: In COPDGene and ECLIPSE study participants with visual scoring of ILA and gene expression data, we evaluated the association of a previously described IPF mortality score with ILA and mortality. We also trained a new ILA score, derived using genes from the IPF score, in a subset of COPDGene. We tested the association with ILA and mortality on the remainder of COPDGene and ECLIPSE. RESULTS: In 1469 COPDGene (training n = 734; testing n = 735) and 571 ECLIPSE participants, the IPF score was not associated with ILA or mortality. However, an ILA score derived from IPF score genes was associated with ILA (meta-analysis of test datasets OR 1.4 [95% CI: 1.2-1.6]) and mortality (HR 1.25 [95% CI: 1.12-1.41]). Six of the 11 genes in the ILA score had discordant directions of effects compared to the IPF score. The ILA score partially mediated the effects of age on mortality (11.8% proportion mediated). CONCLUSIONS: An ILA gene expression score, derived from IPF mortality-associated genes, identified genes with concordant and discordant effects on IPF mortality and ILA. These results suggest shared, and unique biologic processes, amongst those with ILA, IPF, aging, and death.

Interstitial Lung Abnormalities at CT: Subtypes, Clinical Significance, and Associations with Lung Cancer.

Interstitial lung abnormality (ILA) is defined as an interstitial change detected incidentally on CT images. It is seen in 4%-9% of smokers and 2%-7% of nonsmokers. ILA has a tendency to progress with time and is associated with respiratory symptoms, decreased exercise capability, reduced pulmonary function, and increased mortality. ILAs can be classified into three subcategories: nonsubpleural, subpleural nonfibrotic, and subpleural fibrotic. In cases of ILA, clinically significant interstitial lung disease should be identified and requires clinically driven management by a pulmonologist. Risk factors for the progression of ILA include clinical elements (ie, inhalation exposures, medication use, radiation therapy, thoracic surgery, physiologic findings, and gas exchange findings) and radiologic elements (ie, basal and peripheral predominance and fibrotic findings). It is recommended that individuals with one or more clinical or radiologic risk factors for progression of ILA be actively monitored with pulmonary function testing and CT. To avoid overcalling ILA at CT, radiologists must recognize the imaging pitfalls, including centrilobular nodularity, dependent abnormality, suboptimal inspiration, osteophyte-related lesions, apical cap and pleuroparenchymal fibroelastosis-like lesions, aspiration, and infection. There is a close association between ILA and lung cancer, and many studies have reported an increased incidence of lung cancer, worse prognoses, and/or increased pulmonary complications in relation to cancer treatment in patients with ILA. ILA is considered to be an important comorbidity in patients with lung cancer. Accordingly, all radiologists involved with body CT must have sound knowledge of ILAs owing to the high prevalence and potential clinical significance of these anomalies. An overview of ILAs, including a literature review of the associations between ILAs and lung cancer, is presented. ©RSNA, 2022.

Interstitial Lung Abnormalities: State of the Art.

The clinical importance of interstitial lung abnormality (ILA) is increasingly recognized. In July 2020, the Fleischner Society published a position paper about ILA. The purposes of this article are to summarize the definition, existing evidence, clinical management, and unresolved issues for ILA from a radiologic standpoint and to provide a practical guide for radiologists. ILA is a common incidental finding at CT and is often progressive and associated with worsened clinical outcomes. The hazard ratios for mortality range from 1.3 to 2.7 in large cohorts. Risk factors for ILA include age, smoking status, other inhalational exposures, and genetic factors (eg, gene encoding mucin 5B variant). Radiologists should systematically record the presence, morphologic characteristics, distribution, and subcategories of ILA (ie, nonsubpleural, subpleural nonfibrotic, and subpleural fibrotic), as these are informative for predicting progression and mortality. Clinically significant interstitial lung disease should not be considered ILA. Individuals with ILA are triaged into higher- and lower-risk groups depending on their risk factors for progression, and systematic follow-up, including CT, should be considered for the higher-risk group. Artificial intelligence-based automated analysis for ILA may be helpful, but further validation and improvement are needed. Radiologists have a central role in clinical management and research on ILA.

Diagnostic performance for pulmonary adenocarcinoma on CT: comparison of radiologists with and without three-dimensional convolutional neural network.

OBJECTIVES: To compare diagnostic performance for pulmonary invasive adenocarcinoma among radiologists with and without three-dimensional convolutional neural network (3D-CNN). METHODS: Enrolled were 285 patients with adenocarcinoma in situ (AIS, n = 75), minimally invasive adenocarcinoma (MIA, n = 58), and invasive adenocarcinoma (IVA, n = 152). A 3D-CNN model was constructed with seven convolution-pooling and two max-pooling layers and fully connected layers, in which batch normalization, residual connection, and global average pooling were used. Only the flipping process was performed for augmentation. The output layer comprised two nodes for two conditions (AIS/MIA and IVA) according to prognosis. Diagnostic performance of the 3D-CNN model in 285 patients was calculated using nested 10-fold cross-validation. In 90 of 285 patients, results from each radiologist (R1, R2, and R3; with 9, 14, and 26 years of experience, respectively) with and without the 3D-CNN model were statistically compared. RESULTS: Without the 3D-CNN model, accuracy, sensitivity, and specificity of the radiologists were as follows: R1, 70.0%, 52.1%, and 90.5%; R2, 72.2%, 75%, and 69%; and R3, 74.4%, 89.6%, and 57.1%, respectively. With the 3D-CNN model, accuracy, sensitivity, and specificity of the radiologists were as follows: R1, 72.2%, 77.1%, and 66.7%; R2, 74.4%, 85.4%, and 61.9%; and R3, 74.4%, 93.8%, and 52.4%, respectively. Diagnostic performance of each radiologist with and without the 3D-CNN model had no significant difference (p > 0.88), but the accuracy of R1 and R2 was significantly higher with than without the 3D-CNN model (p < 0.01). CONCLUSIONS: The 3D-CNN model can support a less-experienced radiologist to improve diagnostic accuracy for pulmonary invasive adenocarcinoma without deteriorating any diagnostic performances. KEY POINTS: • The 3D-CNN model is a non-invasive method for predicting pulmonary invasive adenocarcinoma in CT images with high sensitivity. • Diagnostic accuracy by a less-experienced radiologist was better with the 3D-CNN model than without the model.

Deep learning algorithm for detection of aortic dissection on non-contrast-enhanced CT.

OBJECTIVES: To develop a deep learning-based algorithm to detect aortic dissection (AD) and evaluate the diagnostic ability of the algorithm compared with those of radiologists. METHODS: Included in the study were 170 patients (85 with AD and 85 without AD). An AD detection algorithm was developed using a convolutional neural network with Xception architecture. Of the patient data, 80% were used for training and validation and 20% were used for testing. Fivefold cross-validation was performed to evaluate the method. An average of 6688 non-contrast-enhanced CT images (slice thickness, 5 mm) were used for training. A radiologist reviewed both contrast-enhanced and non-contrast-enhanced images and identified the slices of AD. The identified slices were used as ground truth. Receiver operating characteristic curve and area under the curve (AUC) analysis was performed. Five radiologists independently evaluated the images. The accuracy, sensitivity, and specificity of the algorithm and those of the radiologists were compared. RESULTS: The AUC of the developed algorithm was 0.940, and a cutoff value of 0.400 provided accuracy of 90.0%, sensitivity of 91.8%, and specificity of 88.2%. For the radiologists, median (range) accuracy, sensitivity, and specificity were 88.8 (83.5-94.1)%, 90.6 (83.5-94.1)%, and 94.1 (72.9-97.6)%, respectively. There was no significant difference in performance in terms of accuracy, sensitivity, or specificity between the algorithm and the average performance of the radiologists (p > 0.05). CONCLUSIONS: The developed algorithm showed comparable diagnostic performance to radiologists for detecting AD, which suggests the potential of the proposed method to support clinical practice by reducing missed ADs. KEY POINTS: • A deep learning-based algorithm for detecting aortic dissection was developed using the non-contrast-enhanced CT images of 170 patients. • The algorithm had an AUC of 0.940 for detecting aortic dissection. • The accuracy, sensitivity, and specificity of the algorithm were comparable to those of radiologists.

Lung Adenocarcinoma at CT with 0.25-mm Section Thickness and a 2048 Matrix: High-Spatial-Resolution Imaging for Predicting Invasiveness.

Background High-spatial-resolution (HSR) CT provides detailed information and clear delineation of lung anatomy and disease states. HSR CT may have high diagnostic performance for predicting invasiveness of lung adenocarcinoma. Purpose To examine the diagnostic performance of HSR CT in predicting the invasiveness of lung adenocarcinoma. Materials and Methods In this retrospective study, 89 consecutive patients with adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), or invasive adenocarcinoma (IVA) were included who underwent surgery for lung cancer between January 2018 and December 2019. All patients underwent HSR CT with 0.25-mm section thickness and a 2048 matrix. Two independent observers evaluated the images for the presence or absence of the following HSR CT findings: lobulation, spiculation, pleural indentation, vessel convergence, homogeneity of ground-glass opacity, reticulation, irregularity and centrality of solid portion, and air bronchiologram (irregularity, disruption, or dilatation). The total diameter (≤1.6 cm or >1.6 cm) and the longest diameter of the solid portion (≤0.8 cm or >0.8 cm) were evaluated. Logistic regression models were used to identify findings associated with MIA plus IVA. Receiver operating characteristic analysis was performed to determine diagnostic performance. Results Eighty-nine patients (mean, 69 years ± 11 [standard deviation]; 49 men) were evaluated. The size of the nodules with invasion was a mean of 2.5 cm ± 1.2. Univariable analysis revealed lobulation, spiculation, pleural indentation, irregular and central solid portion, air bronchiologram with disruption and/or irregular dilatation, and total and solid portion diameters as associated with MIA plus IVA (all, P < .05). After adjustment for age, sex, and pack-years of smoking, disruption of air bronchogram and solid portion diameter greater than 0.8 cm remained as predictors of invasiveness (P = .001 and P = .02, respectively). The diagnostic performance of these two findings combined were as follows: sensitivity of 97% (59 of 61 patients; 95% confidence interval: 94%, 100%) and specificity of 86% (19 of 22 patients; 95% confidence interval: 65%, 97%), with an area under the curve of 0.94. Conclusion Using high-spatial-resolution CT, disruption of air bronchiologram and a solid portion greater than 0.8 cm were independently associated with a greater likelihood of invasiveness in lung adenocarcinoma. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Lynch and Oh in this issue.

Influence of field of view size on image quality: ultra-high-resolution CT vs. conventional high-resolution CT.

OBJECTIVES: This study was conducted in order to compare the effect of field of view (FOV) size on image quality between ultra-high-resolution CT (U-HRCT) and conventional high-resolution CT (HRCT). METHODS: Eleven cadaveric lungs were scanned with U-HRCT and conventional HRCT and reconstructed with five FOVs (40, 80, 160, 240, and 320 mm). Three radiologists evaluated and scored the images. Three image evaluations were performed, comparing the image quality with the five FOVs with respect to the 160-mm FOV. The first evaluation was performed on conventional HRCT images, and the second evaluation on U-HRCT images. Images were scored on normal structure, abnormal findings, and overall image quality. The third evaluation was a comparison of the images obtained with conventional HRCT and U-HRCT, with scoring performed on overall image quality. Quantitative evaluation of noise was performed by setting ROIs. RESULTS: In conventional HRCT, image quality was improved when the FOV was reduced to 160 mm. In U-HRCT, image quality, except for noise, improved when the FOV was reduced to 80 mm. In the third evaluation, overall image quality was improved in U-HRCT over conventional HRCT at all FOVs. Noise of U-HRCT increased with respect to conventional HRCT when the FOV was reduced from 160 to 40 mm. However, at 240- and 320-mm FOVs, the noise of U-HRCT and conventional HRCT showed no differences. CONCLUSIONS: In conventional HRCT, image quality did not improve when the FOV was reduced below 160 mm. However, in U-HRCT, image quality improved even when the FOV was reduced to 80 mm. KEY POINTS: • Reducing the size of the field of view to 160 mm improves diagnostic imaging quality in high-resolution CT. • In ultra-high-resolution CT, improvements in image quality can be obtained by reducing the size of the field of view to 80 mm. • Ultra-high-resolution CT produces images of higher quality compared with conventional HRCT irrespective of the size of the field of view.

Combination of Deep Learning-Based Denoising and Iterative Reconstruction for Ultra-Low-Dose CT of the Chest: Image Quality and Lung-RADS Evaluation.

OBJECTIVE. The objective of our study was to assess the effect of the combination of deep learning-based denoising (DLD) and iterative reconstruction (IR) on image quality and Lung Imaging Reporting and Data System (Lung-RADS) evaluation on chest ultra-low-dose CT (ULDCT). MATERIALS AND METHODS. Forty-one patients with 252 nodules were evaluated retrospectively. All patients underwent ULDCT (mean ± SD, 0.19 ± 0.01 mSv) and standard-dose CT (SDCT) (6.46 ± 2.28 mSv). ULDCT images were reconstructed using hybrid iterative reconstruction (HIR) and model-based iterative reconstruction (MBIR), and they were postprocessed using DLD (i.e., HIR-DLD and MBIR-DLD). SDCT images were reconstructed using filtered back projection. Three independent radiologists subjectively evaluated HIR, HIR-DLD, MBIR, and MBIR-DLD images on a 5-point scale in terms of noise, streak artifact, nodule edge, clarity of small vessels, homogeneity of the normal lung parenchyma, and overall image quality. Two radiologists independently evaluated the nodules according to Lung-RADS using HIR, MBIR, HIR-DLD, and MBIR-DLD ULDCT images and SDCT images. The median scores for subjective analysis were analyzed using Wilcoxon signed rank test with Bonferroni correction. Intraobserver agreement for Lung-RADS category between ULDCT and SDCT was evaluated using the weighted kappa coefficient. RESULTS. In the subjective analysis, ULDCT with DLD showed significantly better scores than did ULDCT without DLD (p < 0.001), and MBIR-DLD showed the best scores among the ULDCT images (p < 0.001) for all items. In the Lung-RADS evaluation, HIR showed fair or moderate agreement (reader 1 and reader 2: κw = 0.46 and 0.32, respectively); MBIR, moderate or good agreement (κw = 0.68 and 0.57); HIR-DLD, moderate agreement (κw = 0.53 and 0.48); and MBIR-DLD, good agreement (κw = 0.70 and 0.72). CONCLUSION. DLD improved the image quality of both HIR and MBIR on ULDCT. MBIR-DLD was superior to HIR_DLD for image quality and for Lung-RADS evaluation.

Subjective and objective comparisons of image quality between ultra-high-resolution CT and conventional area detector CT in phantoms and cadaveric human lungs.

OBJECTIVES: To compare the image quality of the lungs between ultra-high-resolution CT (U-HRCT) and conventional area detector CT (AD-CT) images. METHODS: Image data of slit phantoms (0.35, 0.30, and 0.15 mm) and 11 cadaveric human lungs were acquired by both U-HRCT and AD-CT devices. U-HRCT images were obtained with three acquisition modes: normal mode (U-HRCTN: 896 channels, 0.5 mm × 80 rows; 512 matrix), super-high-resolution mode (U-HRCTSHR: 1792 channels, 0.25 mm × 160 rows; 1024 matrix), and volume mode (U-HRCTSHR-VOL: non-helical acquisition with U-HRCTSHR). AD-CT images were obtained with the same conditions as U-HRCTN. Three independent observers scored normal anatomical structures (vessels and bronchi), abnormal CT findings (faint nodules, solid nodules, ground-glass opacity, consolidation, emphysema, interlobular septal thickening, intralobular reticular opacities, bronchovascular bundle thickening, bronchiectasis, and honeycombing), noise, artifacts, and overall image quality on a 3-point scale (1 = worst, 2 = equal, 3 = best) compared with U-HRCTN. Noise values were calculated quantitatively. RESULTS: U-HRCT could depict a 0.15-mm slit. Both U-HRCTSHR and U-HRCTSHR-VOL significantly improved visualization of normal anatomical structures and abnormal CT findings, except for intralobular reticular opacities and reduced artifacts, compared with AD-CT (p < 0.014). Visually, U-HRCTSHR-VOL has less noise than U-HRCTSHR and AD-CT (p < 0.00001). Quantitative noise values were significantly higher in the following order: U-HRCTSHR (mean, 30.41), U-HRCTSHR-VOL (26.84), AD-CT (16.03), and U-HRCTN (15.14) (p < 0.0001). U-HRCTSHR and U-HRCTSHR-VOL resulted in significantly higher overall image quality than AD-CT and were almost equal to U-HRCTN (p < 0.0001). CONCLUSIONS: Both U-HRCTSHR and U-HRCTSHR-VOL can provide higher image quality than AD-CT, while U-HRCTSHR-VOL was less noisy than U-HRCTSHR. KEY POINTS: • Ultra-high-resolution CT (U-HRCT) can improve spatial resolution. • U-HRCT can reduce streak and dark band artifacts. • U-HRCT can provide higher image quality than conventional area detector CT. • In U-HRCT, the volume mode is less noisy than the super-high-resolution mode. • U-HRCT may provide more detailed information about the lung anatomy and pathology.

Pulmonary Emphysema Quantification on Ultra-Low-Dose Computed Tomography Using Model-Based Iterative Reconstruction With or Without Lung Setting.

OBJECTIVES: To evaluate the influence of model-based iterative reconstruction (MBIR) with lung setting and conventional setting on pulmonary emphysema quantification by ultra-low-dose computed tomography (ULDCT) compared with standard-dose CT (SDCT). METHODS: Forty-five patients who underwent ULDCT (0.18 ± 0.02 mSv) and SDCT (6.66 ± 2.69 mSv) were analyzed in this retrospective study. Images were reconstructed using filtered back projection (FBP) with smooth and sharp kernels and MBIR with conventional and lung settings. Extent of emphysema was evaluated using fully automated software. Correlation between ULDCT and SDCT was assessed by interclass correlation coefficiency (ICC) and Bland-Altman analysis. RESULTS: Excellent correlation was seen between MBIR with conventional setting on ULDCT and FBP with smooth kernel on SDCT (ICC, 0.97; bias, -0.31%) and between MBIR with lung setting on ULDCT and FBP with sharp kernel on SDCT (ICC, 0.82; bias, -2.10%). CONCLUSION: Model-based iterative reconstruction improved the agreement between ULDCT and SDCT on emphysema quantification.

Submillisievert CT using model-based iterative reconstruction with lung-specific setting: An initial phantom study.

OBJECTIVE: To assess image quality of filtered back-projection (FBP) and model-based iterative reconstruction (MBIR) with a conventional setting and a new lung-specific setting on submillisievert CT. METHODS: A lung phantom with artificial nodules was scanned with 10 mA at 120 kVp and 80 kVp (0.14 mSv and 0.05 mSv, respectively); images were reconstructed using FBP and MBIR with conventional setting (MBIRStnd) and lung-specific settings (MBIRRP20/Tx and MBIRRP20). Three observers subjectively scored overall image quality and image findings on a 5-point scale (1 = worst, 5 = best) compared with reference standard images (50 mA-FBP at 120, 100, 80 kVp). Image noise was measured objectively. RESULTS: MBIRRP20/Tx performed significantly better than MBIRStnd for overall image quality in 80-kVp images (p < 0.01), blurring of the border between lung and chest wall in 120p-kVp images (p < 0.05) and the ventral area of 80-kVp images (p < 0.001), and clarity of small vessels in the ventral area of 80-kVp images (p = 0.037). At 120 kVp, 10 mA-MBIRRP20 and 10 mA-MBIRRP20/Tx showed similar performance to 50 mA-FBP. MBIRStnd was better for noise reduction. Except for blurring in 120 kVp-MBIRStnd, MBIRs performed better than FBP. CONCLUSION: Although a conventional setting was advantageous in noise reduction, a lung-specific setting can provide more appropriate image quality, even on submillisievert CT. KEY POINTS: • Lung-specific submillisievert 10 mA-MBIR CT setting has similar performance to 50 mA-FBP • The new lung-specific settings improve vessel clarity and blurring of borders • The new settings may provide more appropriate images than conventional settings.