Thoracic MRI in pleural infection - a feasibility study from patients' and radiographers' perspectives.

BACKGROUND: Pleural infections present significant clinical challenges, particularly in elderly or immunosuppressed patients, leading to prolonged hospital stay, high morbidity and high mortality. While CT and X-ray are standard imaging modalities, MRI's potential remain unexplored due to historical limitations in scan duration and patient discomfort. Advances in MRI technology, however, may enable its broader use in thoracic imaging. The study aimed to explore the feasibility of thoracic MRI from the radiographers' and patients' perspectives. METHODS: A prospective feasibility study was conducted involving thirteen patients with pleural infections who underwent thoracic MRI as an add-on within 48 h of the conventional contrast-enhanced chest CT. Feasibility was assessed on technical success, and scan duration. Patients and radiographers experiences were evaluated through questionnaires and qualitative comments. RESULTS: Technical success was high as all thirteen patients completed the scans. The mean in-room time was 30.7±5.5 minutes and the mean scan time was 23 ± 5.4 minutes. Radiographers reported the MRI scans as feasible with few patients requiring breaks or assistance. Most patients found the MRI experience manageable though two reported difficulties with breath-hold instructions. No patients were challenged by lying in supine position and no patients felt very anxious. No significant movement- or breathing artefacts were identified on MRI. CONCLUSION: Thoracic MRI is feasible with high technical success, acceptable scan time, and good patient experience in patients with pleural infections offering potential as a radiation-free imaging modality. Furthermore, compared to CT, the use of MRI showed potential advantages in identifying pleural effusion septations.

GOLD-Grade Specific Disease Characterization and Phenotyping of COPD Using Quantitative Computed Tomography in the Nationwide COSYCONET Multicenter Trial in Germany.

INTRODUCTION: The aim of this study was to apply quantitative computed tomography (QCT) for GOLD-grade specific disease characterization and phenotyping of air-trapping, emphysema, and airway abnormalities in patients with chronic obstructive pulmonary disease (COPD) from a nationwide cohort study. METHODS: As part of the COSYCONET multicenter study, standardized CT in ex- and inspiration, lung function assessment (FEV1/FVC), and clinical scores (BODE index) were prospectively acquired in 525 patients (192 women, 327 men, aged 65.7 ± 8.5 years) at risk for COPD and at GOLD1-4. QCT parameters such as total lung volume (TLV), emphysema index (EI), parametric response mapping (PRM) for emphysema (PRMEmph) and functional small airway disease (PRMfSAD), total airway volume (TAV), wall percentage (WP), and total diameter (TD) were computed using automated software. RESULTS: TLV, EI, PRMfSAD, and PRMEmph increased incrementally with each GOLD grade (p < 0.001). Aggregated WP5-10 of subsegmental airways was higher from GOLD1 to GOLD3 and lower again at GOLD4 (p < 0.001), whereas TD5-10 was significantly dilated only in GOLD4 (p < 0.001). Fifty-eight patients were phenotyped as "non-airway non-emphysema type," 202 as "airway type," 96 as "emphysema type," and 169 as "mixed type." FEV1/FVC was best in "non-airway non-emphysema type" compared to other phenotypes, while "mixed type" had worst FEV1/FVC (p < 0.001). BODE index was 0.56 ± 0.72 in the "non-airway non-emphysema type" and highest with 2.55 ± 1.77 in "mixed type" (p < 0.001). CONCLUSION: QCT demonstrates increasing hyperinflation and emphysema depending on the GOLD grade, while airway wall thickening increases until GOLD3 and airway dilatation occur in GOLD4. QCT identifies four disease phenotypes with implications for lung function and prognosis.

How do deep-learning models generalize across populations? Cross-ethnicity generalization of COPD detection.

OBJECTIVES: To evaluate the performance and potential biases of deep-learning models in detecting chronic obstructive pulmonary disease (COPD) on chest CT scans across different ethnic groups, specifically non-Hispanic White (NHW) and African American (AA) populations. MATERIALS AND METHODS: Inspiratory chest CT and clinical data from 7549 Genetic epidemiology of COPD individuals (mean age 62 years old, 56-69 interquartile range), including 5240 NHW and 2309 AA individuals, were retrospectively analyzed. Several factors influencing COPD binary classification performance on different ethnic populations were examined: (1) effects of training population: NHW-only, AA-only, balanced set (half NHW, half AA) and the entire set (NHW + AA all); (2) learning strategy: three supervised learning (SL) vs. three self-supervised learning (SSL) methods. Distribution shifts across ethnicity were further assessed for the top-performing methods. RESULTS: The learning strategy significantly influenced model performance, with SSL methods achieving higher performances compared to SL methods (p < 0.001), across all training configurations. Training on balanced datasets containing NHW and AA individuals resulted in improved model performance compared to population-specific datasets. Distribution shifts were found between ethnicities for the same health status, particularly when models were trained on nearest-neighbor contrastive SSL. Training on a balanced dataset resulted in fewer distribution shifts across ethnicity and health status, highlighting its efficacy in reducing biases. CONCLUSION: Our findings demonstrate that utilizing SSL methods and training on large and balanced datasets can enhance COPD detection model performance and reduce biases across diverse ethnic populations. These findings emphasize the importance of equitable AI-driven healthcare solutions for COPD diagnosis. CRITICAL RELEVANCE STATEMENT: Self-supervised learning coupled with balanced datasets significantly improves COPD detection model performance, addressing biases across diverse ethnic populations and emphasizing the crucial role of equitable AI-driven healthcare solutions. KEY POINTS: Self-supervised learning methods outperform supervised learning methods, showing higher AUC values (p < 0.001). Balanced datasets with non-Hispanic White and African American individuals improve model performance. Training on diverse datasets enhances COPD detection accuracy. Ethnically diverse datasets reduce bias in COPD detection models. SimCLR models mitigate biases in COPD detection across ethnicities.

Expiratory Venous Volume and Arterial Tortuosity are Associated with Disease Severity and Mortality Risk in Patients with COPD: Results from COSYCONET.

Purpose: The aim of this study was to evaluate the association between computed tomography (CT) quantitative pulmonary vessel morphology and lung function, disease severity, and mortality risk in patients with chronic obstructive pulmonary disease (COPD). Patients and Methods: Participants of the prospective nationwide COSYCONET cohort study with paired inspiratory-expiratory CT were included. Fully automatic software, developed in-house, segmented arterial and venous pulmonary vessels and quantified volume and tortuosity on inspiratory and expiratory scans. The association between vessel volume normalised to lung volume and tortuosity versus lung function (forced expiratory volume in 1 sec [FEV1]), air trapping (residual volume to total lung capacity ratio [RV/TLC]), transfer factor for carbon monoxide (TLCO), disease severity in terms of Global Initiative for Chronic Obstructive Lung Disease (GOLD) group D, and mortality were analysed by linear, logistic or Cox proportional hazard regression. Results: Complete data were available from 138 patients (39% female, mean age 65 years). FEV1, RV/TLC and TLCO, all as % predicted, were significantly (p < 0.05 each) associated with expiratory vessel characteristics, predominantly venous volume and arterial tortuosity. Associations with inspiratory vessel characteristics were absent or negligible. The patterns were similar for relationships between GOLD D and mortality with vessel characteristics. Expiratory venous volume was an independent predictor of mortality, in addition to FEV1. Conclusion: By using automated software in patients with COPD, clinically relevant information on pulmonary vasculature can be extracted from expiratory CT scans (although not inspiratory scans); in particular, expiratory pulmonary venous volume predicted mortality. Trial Registration: NCT01245933.

Midregional Proatrial Natriuretic Peptide (MRproANP) is associated with vertebral fractures and low bone density in patients with chronic obstructive pulmonary disease (COPD).

BACKGROUND: Patients with COPD are often affected by loss of bone mineral density (BMD) and osteoporotic fractures. Natriuretic peptides (NP) are known as cardiac markers, but have also been linked to fragility-associated fractures in the elderly. As their functions include regulation of fluid and mineral balance, they also might affect bone metabolism, particularly in systemic disorders such as COPD. RESEARCH QUESTION: We investigated the association between NP serum levels, vertebral fractures and BMD assessed by chest computed tomography (CT) in patients with COPD. METHODS: Participants of the COSYCONET cohort with CT scans were included. Mean vertebral bone density on CT (BMD-CT) as a risk factor for osteoporosis was assessed at the level of TH12 (AI-Rad Companion), and vertebral compression fractures were visually quantified by two readers. Their relationship with N-terminal pro-B-type natriuretic peptide (NT-proBNP), Mid-regional pro-atrial natriuretic peptide (MRproANP) and Midregional pro-adrenomedullin (MRproADM) was determined using group comparisons and multivariable analyses. RESULTS: Among 418 participants (58% male, median age 64 years, FEV1 59.6% predicted), vertebral fractures in TH12 were found in 76 patients (18.1%). Compared to patients without fractures, these had elevated serum levels (p ≤ 0.005) of MRproANP and MRproADM. Using optimal cut-off values in multiple logistic regression analyses, MRproANP levels ≥ 65 nmol/l (OR 2.34; p = 0.011) and age (p = 0.009) were the only significant predictors of fractures after adjustment for sex, BMI, smoking status, FEV1% predicted, SGRQ Activity score, daily physical activity, oral corticosteroids, the diagnosis of cardiac disease, and renal impairment. Correspondingly, MRproANP (p < 0.001), age (p = 0.055), SGRQ Activity score (p = 0.061) and active smoking (p = 0.025) were associated with TH12 vertebral density. INTERPRETATION: MRproANP was a marker for osteoporotic vertebral fractures in our COPD patients from the COSYCONET cohort. Its association with reduced vertebral BMD on CT and its known modulating effects on fluid and ion balance are suggestive of direct effects on bone mineralization. TRIAL REGISTRATION: ClinicalTrials.gov NCT01245933, Date of registration: 18 November 2010.

Capturing COPD heterogeneity: anomaly detection and parametric response mapping comparison for phenotyping on chest computed tomography.

Background: Chronic obstructive pulmonary disease (COPD) poses a substantial global health burden, demanding advanced diagnostic tools for early detection and accurate phenotyping. In this line, this study seeks to enhance COPD characterization on chest computed tomography (CT) by comparing the spatial and quantitative relationships between traditional parametric response mapping (PRM) and a novel self-supervised anomaly detection approach, and to unveil potential additional insights into the dynamic transitional stages of COPD. Methods: Non-contrast inspiratory and expiratory CT of 1,310 never-smoker and GOLD 0 individuals and COPD patients (GOLD 1-4) from the COPDGene dataset were retrospectively evaluated. A novel self-supervised anomaly detection approach was applied to quantify lung abnormalities associated with COPD, as regional deviations. These regional anomaly scores were qualitatively and quantitatively compared, per GOLD class, to PRM volumes (emphysema: PRMEmph, functional small-airway disease: PRMfSAD) and to a Principal Component Analysis (PCA) and Clustering, applied on the self-supervised latent space. Its relationships to pulmonary function tests (PFTs) were also evaluated. Results: Initial t-Distributed Stochastic Neighbor Embedding (t-SNE) visualization of the self-supervised latent space highlighted distinct spatial patterns, revealing clear separations between regions with and without emphysema and air trapping. Four stable clusters were identified among this latent space by the PCA and Cluster Analysis. As the GOLD stage increased, PRMEmph, PRMfSAD, anomaly score, and Cluster 3 volumes exhibited escalating trends, contrasting with a decline in Cluster 2. The patient-wise anomaly scores significantly differed across GOLD stages (p < 0.01), except for never-smokers and GOLD 0 patients. In contrast, PRMEmph, PRMfSAD, and cluster classes showed fewer significant differences. Pearson correlation coefficients revealed moderate anomaly score correlations to PFTs (0.41-0.68), except for the functional residual capacity and smoking duration. The anomaly score was correlated with PRMEmph (r = 0.66, p < 0.01) and PRMfSAD (r = 0.61, p < 0.01). Anomaly scores significantly improved fitting of PRM-adjusted multivariate models for predicting clinical parameters (p < 0.001). Bland-Altman plots revealed that volume agreement between PRM-derived volumes and clusters was not constant across the range of measurements. Conclusion: Our study highlights the synergistic utility of the anomaly detection approach and traditional PRM in capturing the nuanced heterogeneity of COPD. The observed disparities in spatial patterns, cluster dynamics, and correlations with PFTs underscore the distinct - yet complementary - strengths of these methods. Integrating anomaly detection and PRM offers a promising avenue for understanding of COPD pathophysiology, potentially informing more tailored diagnostic and intervention approaches to improve patient outcomes.

Phenotyping of COPD with MRI in comparison to same-day CT in a multi-centre trial.

OBJECTIVES: A prospective, multi-centre study to evaluate concordance of morphologic lung MRI and CT in chronic obstructive pulmonary disease (COPD) phenotyping for airway disease and emphysema. METHODS: A total of 601 participants with COPD from 15 sites underwent same-day morpho-functional chest MRI and paired inspiratory-expiratory CT. Two readers systematically scored bronchial wall thickening, bronchiectasis, centrilobular nodules, air trapping and lung parenchyma defects in each lung lobe and determined COPD phenotype. A third reader acted as adjudicator to establish consensus. Inter-modality and inter-reader agreement were assessed using Cohen's kappa (im-κ and ir-κ). RESULTS: The mean combined MRI score for bronchiectasis/bronchial wall thickening was 4.5/12 (CT scores, 2.2/12 for bronchiectasis and 6/12 for bronchial wall thickening; im-κ, 0.04-0.3). Expiratory right/left bronchial collapse was observed in 51 and 47/583 on MRI (62 and 57/599 on CT; im-κ, 0.49-0.52). Markers of small airways disease on MRI were 0.15/12 for centrilobular nodules (CT, 0.34/12), 0.94/12 for air trapping (CT, 0.9/12) and 7.6/12 for perfusion deficits (CT, 0.37/12 for mosaic attenuation; im-κ, 0.1-0.41). The mean lung defect score on MRI was 1.3/12 (CT emphysema score, 5.8/24; im-κ, 0.18-0.26). Airway-/emphysema/mixed COPD phenotypes were assigned in 370, 218 and 10 of 583 cases on MRI (347, 218 and 34 of 599 cases on CT; im-κ, 0.63). For all examined features, inter-reader agreement on MRI was lower than on CT. CONCLUSION: Concordance of MRI and CT for phenotyping of COPD in a multi-centre setting was substantial with variable inter-modality and inter-reader concordance for single diagnostic key features. CLINICAL RELEVANCE STATEMENT: MRI of lung morphology may well serve as a radiation-free imaging modality for COPD in scientific and clinical settings, given that its potential and limitations as shown here are carefully considered. KEY POINTS: • In a multi-centre setting, MRI and CT showed substantial concordance for phenotyping of COPD (airway-/emphysema-/mixed-type). • Individual features of COPD demonstrated variable inter-modality concordance with features of pulmonary hypertension showing the highest and bronchiectasis showing the lowest concordance. • For all single features of COPD, inter-reader agreement was lower on MRI than on CT.

Prediction of disease severity in COPD: a deep learning approach for anomaly-based quantitative assessment of chest CT.

OBJECTIVES: To quantify regional manifestations related to COPD as anomalies from a modeled distribution of normal-appearing lung on chest CT using a deep learning (DL) approach, and to assess its potential to predict disease severity. MATERIALS AND METHODS: Paired inspiratory/expiratory CT and clinical data from COPDGene and COSYCONET cohort studies were included. COPDGene data served as training/validation/test data sets (N = 3144/786/1310) and COSYCONET as external test set (N = 446). To differentiate low-risk (healthy/minimal disease, [GOLD 0]) from COPD patients (GOLD 1-4), the self-supervised DL model learned semantic information from 50 × 50 × 50 voxel samples from segmented intact lungs. An anomaly detection approach was trained to quantify lung abnormalities related to COPD, as regional deviations. Four supervised DL models were run for comparison. The clinical and radiological predictive power of the proposed anomaly score was assessed using linear mixed effects models (LMM). RESULTS: The proposed approach achieved an area under the curve of 84.3 ± 0.3 (p < 0.001) for COPDGene and 76.3 ± 0.6 (p < 0.001) for COSYCONET, outperforming supervised models even when including only inspiratory CT. Anomaly scores significantly improved fitting of LMM for predicting lung function, health status, and quantitative CT features (emphysema/air trapping; p < 0.001). Higher anomaly scores were significantly associated with exacerbations for both cohorts (p < 0.001) and greater dyspnea scores for COPDGene (p < 0.001). CONCLUSION: Quantifying heterogeneous COPD manifestations as anomaly offers advantages over supervised methods and was found to be predictive for lung function impairment and morphology deterioration. CLINICAL RELEVANCE STATEMENT: Using deep learning, lung manifestations of COPD can be identified as deviations from normal-appearing chest CT and attributed an anomaly score which is consistent with decreased pulmonary function, emphysema, and air trapping. KEY POINTS: • A self-supervised DL anomaly detection method discriminated low-risk individuals and COPD subjects, outperforming classic DL methods on two datasets (COPDGene AUC = 84.3%, COSYCONET AUC = 76.3%). • Our contrastive task exhibits robust performance even without the inclusion of expiratory images, while voxel-based methods demonstrate significant performance enhancement when incorporating expiratory images, in the COPDGene dataset. • Anomaly scores improved the fitting of linear mixed effects models in predicting clinical parameters and imaging alterations (p < 0.001) and were directly associated with clinical outcomes (p < 0.001).

ERS/ESTS/ESTRO/ESR/ESTI/EFOMP statement on management of incidental findings from low dose CT screening for lung cancer.

BACKGROUND: Screening for lung cancer with low radiation dose computed tomography has a strong evidence base, is being introduced in several European countries and is recommended as a new targeted cancer screening programme. The imperative now is to ensure that implementation follows an evidence-based process that will ensure clinical and cost effectiveness. This European Respiratory Society (ERS) task force was formed to provide an expert consensus for the management of incidental findings which can be adapted and followed during implementation. METHODS: A multi-European society collaborative group was convened. 23 topics were identified, primarily from an ERS statement on lung cancer screening, and a systematic review of the literature was conducted according to ERS standards. Initial review of abstracts was completed and full text was provided to members of the group for each topic. Sections were edited and the final document approved by all members and the ERS Science Council. RESULTS: Nine topics considered most important and frequent were reviewed as standalone topics (interstitial lung abnormalities, emphysema, bronchiectasis, consolidation, coronary calcification, aortic valve disease, mediastinal mass, mediastinal lymph nodes and thyroid abnormalities). Other topics considered of lower importance or infrequent were grouped into generic categories, suitable for general statements. CONCLUSIONS: This European collaborative group has produced an incidental findings statement that can be followed during lung cancer screening. It will ensure that an evidence-based approach is used for reporting and managing incidental findings, which will mean that harms are minimised and any programme is as cost-effective as possible.

ERS/ESTS/ESTRO/ESR/ESTI/EFOMP statement on management of incidental findings from low dose CT screening for lung cancer.

BACKGROUND: Screening for lung cancer with low radiation dose computed tomography has a strong evidence base, is being introduced in several European countries and is recommended as a new targeted cancer screening programme. The imperative now is to ensure that implementation follows an evidence-based process that will ensure clinical and cost effectiveness. This European Respiratory Society (ERS) task force was formed to provide an expert consensus for the management of incidental findings which can be adapted and followed during implementation. METHODS: A multi-European society collaborative group was convened. 23 topics were identified, primarily from an ERS statement on lung cancer screening, and a systematic review of the literature was conducted according to ERS standards. Initial review of abstracts was completed and full text was provided to members of the group for each topic. Sections were edited and the final document approved by all members and the ERS Science Council. RESULTS: Nine topics considered most important and frequent were reviewed as standalone topics (interstitial lung abnormalities, emphysema, bronchiectasis, consolidation, coronary calcification, aortic valve disease, mediastinal mass, mediastinal lymph nodes and thyroid abnormalities). Other topics considered of lower importance or infrequent were grouped into generic categories, suitable for general statements. CONCLUSIONS: This European collaborative group has produced an incidental findings statement that can be followed during lung cancer screening. It will ensure that an evidence-based approach is used for reporting and managing incidental findings, which will mean that harms are minimised and any programme is as cost-effective as possible.

Metal Artifact Reduction in Photon-Counting Detector CT: Quantitative Evaluation of Artifact Reduction Techniques.

OBJECTIVES: With the introduction of clinical photon-counting detector computed tomography (PCD-CT) and its novel reconstruction techniques, a quantitative investigation of different acquisition and reconstruction settings is necessary to optimize clinical acquisition protocols for metal artifact reduction. MATERIALS AND METHODS: A multienergy phantom was scanned on a clinical dual-source PCD-CT (NAEOTOM Alpha; Siemens Healthcare GmbH) with 4 different central inserts: water-equivalent plastic, aluminum, steel, and titanium. Acquisitions were performed at 120 kVp and 140 kVp (CTDIvol 10 mGy) and reconstructed as virtual monoenergetic images (VMIs; 110-150 keV), as T3D, and with the standard reconstruction "none" (70 keV VMI) using different reconstruction kernels (Br36, Br56) and with as well as without iterative metal artifact reduction (iMAR). Metal artifacts were quantified, calculating relative percentages of metal artifacts. Mean CT numbers of an adjacent water-equivalent insert and different tissue-equivalent inserts were evaluated, and eccentricity of metal rods was measured. Repeated-measures analysis of variance was performed for statistical analysis. RESULTS: Metal artifacts were most prevalent for the steel insert (12.6% average artifacts), followed by titanium (4.2%) and aluminum (1.0%). The strongest metal artifact reduction was noted for iMAR (with iMAR: 1.4%, without iMAR: 10.5%; P < 0.001) or VMI (VMI: 110 keV 2.6% to 150 keV 3.3%, T3D: 11.0%, and none: 16.0%; P < 0.001) individually, with best results when combining iMAR and VMI at 110 keV (1.2%). Changing acquisition tube potential (120 kV: 6.6%, 140 kV: 5.2%; P = 0.33) or reconstruction kernel (Br36: 5.5%, Br56: 6.4%; P = 0.17) was less effective. Mean CT numbers and standard deviations were significantly affected by iMAR (with iMAR: -3.0 ± 21.5 HU, without iMAR: -8.5 ± 24.3 HU; P < 0.001), VMI (VMI: 110 keV -3.6 ± 21.6 HU to 150 keV -1.4 ± 21.2 HU, T3D: -11.7 ± 23.8 HU, and none: -16.9 ± 29.8 HU; P < 0.001), tube potential (120 kV: -4.7 ± 22.8 HU, 140 kV: -6.8 ± 23.0 HU; P = 0.03), and reconstruction kernel (Br36: -5.5 ± 14.2 HU, Br56: -6.8 ± 23.0 HU; P < 0.001). Both iMAR and VMI improved quantitative CT number accuracy and metal rod eccentricity for the steel rod, but iMAR was of limited effectiveness for the aluminum rod. CONCLUSIONS: For metal artifact reduction in PCD-CT, a combination of iMAR and VMI at 110 keV demonstrated the strongest artifact reduction of the evaluated options, whereas the impact of reconstruction kernel and tube potential was limited.

MRI Compared with Low-Dose CT for Incidental Lung Nodule Detection in COPD: A Multicenter Trial.

Purpose: To investigate morphofunctional chest MRI for the detection and management of incidental pulmonary nodules in participants with chronic obstructive pulmonary disease (COPD). Materials and Methods: In this prospective study, 567 participants (mean age, 66 years ± 9 [SD]; 340 men) underwent same-day contrast-enhanced MRI and nonenhanced low-dose CT (LDCT) in a nationwide multicenter trial (clinicaltrials.gov: NCT01245933). Nodule dimensions, morphologic features, and Lung Imaging Reporting and Data System (Lung-RADS) category were assessed at MRI by two blinded radiologists, and consensual LDCT results served as the reference standard. Comparisons were performed using the Student t test, and agreements were assessed using the Cohen weighted κ. Results: A total of 525 nodules larger than 3 mm in diameter were detected at LDCT in 178 participants, with a mean diameter of 7.2 mm ± 6.1 (range, 3.1-63.1 mm). Nodules were not detected in the remaining 389 participants. Sensitivity and positive predictive values with MRI for readers 1 and 2, respectively, were 63.0% and 84.8% and 60.2% and 83.9% for solid nodules (n = 495), 17.6% and 75.0% and 17.6% and 60.0% for part-solid nodules (n = 17), and 7.7% and 100% and 7.7% and 50.0% for ground-glass nodules (n = 13). For nodules 6 mm or greater in diameter, sensitivity and positive predictive values were 73.3% and 92.2% for reader 1 and 71.4% and 93.2% for reader 2, respectively. Readers underestimated the long-axis diameter at MRI by 0.5 mm ± 1.7 (reader 1) and 0.5 mm ± 1.5 (reader 2) compared with LDCT (P < .001). For Lung-RADS categorization per nodule using MRI, there was substantial to perfect interreader agreement (κ = 0.75-1.00) and intermethod agreement compared with LDCT (κ = 0.70-1.00 and 0.69-1.00). Conclusion: In a multicenter setting, morphofunctional MRI showed moderate sensitivity for detection of incidental pulmonary nodules in participants with COPD but high agreement with LDCT for Lung-RADS classification of nodules.Clinical trial registration no. NCT01245933 and NCT02629432Keywords: MRI, CT, Thorax, Lung, Chronic Obstructive Pulmonary Disease, Screening© RSNA, 2023 Supplemental material is available for this article.

High resolution propagation-based lung imaging at clinically relevant X-ray dose levels.

Absorption-based clinical computed tomography (CT) is the current imaging method of choice in the diagnosis of lung diseases. Many pulmonary diseases are affecting microscopic structures of the lung, such as terminal bronchi, alveolar spaces, sublobular blood vessels or the pulmonary interstitial tissue. As spatial resolution in CT is limited by the clinically acceptable applied X-ray dose, a comprehensive diagnosis of conditions such as interstitial lung disease, idiopathic pulmonary fibrosis or the characterization of small pulmonary nodules is limited and may require additional validation by invasive lung biopsies. Propagation-based imaging (PBI) is a phase sensitive X-ray imaging technique capable of reaching high spatial resolutions at relatively low applied radiation dose levels. In this publication, we present technical refinements of PBI for the characterization of different artificial lung pathologies, mimicking clinically relevant patterns in ventilated fresh porcine lungs in a human-scale chest phantom. The combination of a very large propagation distance of 10.7 m and a photon counting detector with [Formula: see text] pixel size enabled high resolution PBI CT with significantly improved dose efficiency, measured by thermoluminescence detectors. Image quality was directly compared with state-of-the-art clinical CT. PBI with increased propagation distance was found to provide improved image quality at the same or even lower X-ray dose levels than clinical CT. By combining PBI with iodine k-edge subtraction imaging we further demonstrate that, the high quality of the calculated iodine concentration maps might be a potential tool for the analysis of lung perfusion in great detail. Our results indicate PBI to be of great value for accurate diagnosis of lung disease in patients as it allows to depict pathological lesions non-invasively at high resolution in 3D. This will especially benefit patients at high risk of complications from invasive lung biopsies such as in the setting of suspected idiopathic pulmonary fibrosis (IPF).

Implementation of artificial intelligence in thoracic imaging-a what, how, and why guide from the European Society of Thoracic Imaging (ESTI).

This statement from the European Society of Thoracic imaging (ESTI) explains and summarises the essentials for understanding and implementing Artificial intelligence (AI) in clinical practice in thoracic radiology departments. This document discusses the current AI scientific evidence in thoracic imaging, its potential clinical utility, implementation and costs, training requirements and validation, its' effect on the training of new radiologists, post-implementation issues, and medico-legal and ethical issues. All these issues have to be addressed and overcome, for AI to become implemented clinically in thoracic radiology. KEY POINTS: • Assessing the datasets used for training and validation of the AI system is essential. • A departmental strategy and business plan which includes continuing quality assurance of AI system and a sustainable financial plan is important for successful implementation. • Awareness of the negative effect on training of new radiologists is vital.

Standardized airway wall thickness Pi10 from routine CT scans of COPD patients as imaging biomarker for disease severity, lung function decline, and mortality.

BACKGROUND: Chest computed tomography (CT) is increasingly used for phenotyping and monitoring of patients with COPD. The aim of this work was to evaluate the association of Pi10 as a measure of standardized airway wall thickness on CT with exacerbations, mortality, and response to triple therapy. METHODS: Patients of GOLD grades 1-4 of the COSYCONET cohort with prospective CT scans were included. Pi10 was automatically computed and analyzed for its relationship to COPD severity, comorbidities, lung function, respiratory therapy, and mortality over a 6-year period, using univariate and multivariate comparisons. RESULTS: We included n = 433 patients (61%male). Pi10 was dependent on both GOLD grades 1-4 (p = 0.009) and GOLD groups A-D (p = 0.008); it was particularly elevated in group D, and ROC analysis yielded a cut-off of 0.26 cm. Higher Pi10 was associated to lower FEV1 % predicted and higher RV/TLC, moreover the annual changes of lung function parameters (p < 0.05), as well as to an airway-dominated phenotype and a history of myocardial infarction (p = 0.001). These associations were confirmed in multivariate analyses. Pi10 was lower in patients receiving triple therapy, in particular in patients of GOLD groups C and D. Pi10 was also a significant predictor for mortality (p = 0.006), even after including multiple other predictors. CONCLUSION: In summary, Pi10 was found to be predictive for the course of the disease in COPD, in particular mortality. The fact that Pi10 was lower in patients with severe COPD receiving triple therapy might hint toward additional effects of this functional therapy on airway remodeling. REGISTRATION: ClinicalTrials.gov, Identifier: NCT01245933.

Echo time-dependent observed T1 and quantitative perfusion in chronic obstructive pulmonary disease using magnetic resonance imaging.

Introduction: Due to hypoxic vasoconstriction, perfusion is interesting in the lungs. Magnetic Resonance Imaging (MRI) perfusion imaging based on Dynamic Contrast Enhancement (DCE) has been demonstrated in patients with Chronic Obstructive Pulmonary Diseases (COPD) using visual scores, and quantification methods were recently developed further. Inter-patient correlations of echo time-dependent observed T1 [T1(TE)] have been shown with perfusion scores, pulmonary function testing, and quantitative computed tomography. Here, we examined T1(TE) quantification and quantitative perfusion MRI together and investigated both inter-patient and local correlations between T1(TE) and quantitative perfusion. Methods: 22 patients (age 68.0 ± 6.2) with COPD were examined using morphological MRI, inversion recovery multi-echo 2D ultra-short TE (UTE) in 1-2 slices for T1(TE) mapping, and 4D Time-resolved angiography With Stochastic Trajectories (TWIST) for DCE. T1(TE) maps were calculated from 2D UTE at five TEs from 70 to 2,300 µs. Pulmonary Blood Flow (PBF) and perfusion defect (QDP) maps were produced from DCE measurements. Lungs were automatically segmented on UTE images and morphological MRI and these segmentations registered to DCE images. DCE images were separately registered to UTE in corresponding slices and divided into corresponding subdivisions. Spearman's correlation coefficients were calculated for inter-patient correlations using the entire segmented slices and for local correlations separately using registered images and subdivisions for each TE. Median T1(TE) in normal and defect areas according to QDP maps were compared. Results: Inter-patient correlations were strongest on average at TE2 = 500 µs, reaching up to |ρ| = 0.64 for T1 with PBF and |ρ| = 0.76 with QDP. Generally, local correlations of T1 with PBF were weaker at TE2 than at TE1 or TE3 and with maximum values of |ρ| = 0.66 (from registration) and |ρ| = 0.69 (from subdivision). In 18 patients, T1 was shorter in defect areas than in normal areas, with the relative difference smallest at TE2. Discussion: The inter-patient correlations of T1 with PBF and QDP found show similar strength and TE-dependence as those previously reported for visual perfusion scores and quantitative computed tomography. The local correlations and median T1 suggest that not only base T1 but also the TE-dependence of observed T1 in normal areas is closer to that found previously in healthy volunteers than in defect areas.

Asynchronous calibration of quantitative computed tomography bone mineral density assessment for opportunistic osteoporosis screening: phantom-based validation and parameter influence evaluation.

Asynchronous calibration could allow opportunistic screening based on routine CT for early osteoporosis detection. In this phantom study, a bone mineral density (BMD) calibration phantom and multi-energy CT (MECT) phantom were imaged on eight different CT scanners with multiple tube voltages (80-150 kVp) and image reconstruction settings (e.g. soft/hard kernel). Reference values for asynchronous BMD estimation were calculated from the BMD-phantom and validated with six calcium composite inserts of the MECT-phantom with known ground truth. Relative errors/changes in estimated BMD were calculated and investigated for influence of tube voltage, CT scanner and reconstruction setting. Reference values for 282 acquisitions were determined, resulting in an average relative error between calculated BMD and ground truth of - 9.2% ± 14.0% with a strong correlation (R2 = 0.99; p < 0.0001). Tube voltage and CT scanner had a significant effect on calculated BMD (p < 0.0001), with relative differences in BMD of 3.8% ± 28.2% when adapting reference values for tube voltage, - 5.6% ± 9.2% for CT scanner and 0.2% ± 0.2% for reconstruction setting, respectively. Differences in BMD were small when using reference values from a different CT scanner of the same model (0.0% ± 1.4%). Asynchronous phantom-based calibration is feasible for opportunistic BMD assessment based on CT images with reference values adapted for tube voltage and CT scanner model.

Association of coronary artery calcification with clinical and physiological characteristics in patients with COPD: Results from COSYCONET.

Chronic obstructive pulmonary disease (COPD) is frequently associated with coronary artery disease (CAD). When considering computed tomography (CT) for COPD phenotyping, coronary vessel wall calcification would be a potential marker of cardiac disease. However, non-ECG gated scans as used in COPD monitoring do not comply with established quantitative approaches using ECG-triggered CT and the Agatston score. We studied the diagnostic potential of Agatston scores from non-triggered scans for cardiac disease. The study population was a sub-group of the COPD cohort COSYCONET that underwent CT scanning in addition to comprehensive clinical assessments, echocardiographic data and physician-based diagnoses of comorbidities. Agatston scores from non-contrast enhanced, non-triggered CT were used to identify a cut-off value for CAD via ROC analysis. 399 patients were included (152 female, mean age 66.0 ± 8.2 y). In terms of CAD, an Agatston score ≥1500 AU performed best (AUC 0.765; 95% CI: 0.700, 0.831) and was superior to the conventional cut-off value (400 AU). Using this value for defining groups, there were differences (p < 0.05) in lung function, left atrial diameter and left ventricular end-systolic diameter as well as CT-determined central airway wall thickness pointing towards a bronchitis phenotype. In multivariate analysis, BMI, hyperlipidemia, arterial hypertension, GOLD D (p < 0.05) but particularly Agatston score ≥1500 AU (Odds ratio 10.5; 95% CI: 4.8; 22.6)) were predictors of CAD. We conclude that in COPD patients, Agatston scores derived from non-ECG gated CT showed a much higher cut-off value (1500 AU) for actionable coronary artery disease than the score derived from ECG-triggered CT in cardiology patients.

[Chest X-ray: implementation and indication : Recommendations of the thoracic imaging working group of the German Radiological Society]. / Röntgen-Thorax: Durchführung und Indikationen : Empfehlungen der AG Thoraxdiagnostik der Deutschen Röntgengesellschaft.

BACKGROUND: Even after more than 100 years, the chest X­ray is still an important technique to detect important pathological changes of lungs, heart and vessels in a fast and low-dose manner. For the German-speaking regions, there are only recommendations available published by the "Ständigen Strahlenschutzkommission (SSK)" regarding the indication. These recommendations are not updated on a regular basis and more recent developments are only integrated with delayed. METHODS: The chest division of the German Radiological Society has summarized their expertise for the usage and indication of the chest X­ray. Especially within the field of oncology the usage of chest X­ray is evaluated differently to the aforementioned recommendations; here chest computed tomography (CT) is much more sensitive for evaluation of metastasis and local invasion of tumors. Also, within the area of infectious diseases in non-immunocompetent patients, CT is the method of choice. Based on the structure of the current recommendations, many current guidelines and indications are summarized and presented within the context of the usage of chest X­ray.