Non-Contrast-Enhanced Functional Lung MRI to Evaluate Treatment Response of Allergic Bronchopulmonary Aspergillosis in Patients With Cystic Fibrosis: A Pilot Study.

BACKGROUND: Allergic bronchopulmonary aspergillosis (ABPA) in cystic fibrosis (CF) patients is associated with severe lung damage and requires specific therapeutic management. Repeated imaging is recommended to both diagnose and follow-up response to treatment of ABPA in CF. However, high risk of cumulative radiation exposure requires evaluation of free-radiation techniques in the follow-up of CF patients with ABPA. PURPOSE: To evaluate whether Fourier decomposition (FD) functional lung MRI can detect response to treatment of ABPA in CF patients. STUDY TYPE: Retrospective longitudinal. POPULATION: Twelve patients (7M, median-age:14 years) with CF and ABPA with pre- and post-treatment MRI. FIELD STRENGTH/SEQUENCE: 2D-balanced-steady-state free-precession (bSSFP) sequence with FD at 1.5T. ASSESSMENT: Ventilation-weighted (V) and perfusion-weighted (Q) maps were obtained after FD processing of 2D-coronal bSSFP time-resolved images acquired before and 3-9 months after treatment. Defects extent was assessed on the functional maps using a qualitative semi-quantitative score (0 = absence/negligible, 1 = <50%, 2 = >50%). Mean and coefficient of variation (CV) of the ventilation signal-intensity (VSI) and the perfusion signal-intensity (QSI) were calculated. Measurements were performed independently by three readers and averaged. Inter-reader reproducibility of the measurements was assessed. Pulmonary function tests (PFTs) were performed within 1 week of both MRI studies as markers of the airflow-limitation severity. STATISTICAL TESTS: Comparisons of medians were performed using the paired Wilcoxon-test. Reproducibility was assessed using intraclass correlation coefficient (ICC). Correlations between MRI and PFT parameters were assessed using the Spearman-test (rho correlation-coefficient). A P-value <0.05 was considered as significant. RESULTS: Defects extent on both V and Q maps showed a significant reduction after ABPA treatment (4.25 vs. 1.92 for V-defect-score and 5 vs. 2.75 for Q-defect-score). VSI_mean was significantly increased after treatment (280 vs. 167). Qualitative analyses reproducibility showed an ICC > 0.90, while the ICCs of the quantitative measurements was almost perfect (>0.99). Changes in VSI_cv and QSI_cv before and after treatment correlated inversely with changes of FEV1%p (rho = -0.68 for both). DATA CONCLUSION: Non-contrast-enhanced FD lung MRI has potential to reproducibly assess response to treatment of ABPA in CF patients and correlates with PFT obstructive parameters. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 3.

Advanced Myocardial MRI Tissue Characterization Combining Contrast Agent-Free T1-Rho Mapping With Fully Automated Analysis.

BACKGROUND: Myocardial T1-rho (T1ρ) mapping is a promising method for identifying and quantifying myocardial injuries without contrast agents, but its clinical use is hindered by the lack of dedicated analysis tools. PURPOSE: To explore the feasibility of clinically integrated artificial intelligence-driven analysis for efficient and automated myocardial T1ρ mapping. STUDY TYPE: Retrospective. POPULATION: Five hundred seventy-three patients divided into a training (N = 500) and a test set (N = 73) including ischemic and nonischemic cases. FIELD STRENGTH/SEQUENCE: Single-shot bSSFP T1ρ mapping sequence at 1.5 T. ASSESSMENT: The automated process included: left ventricular (LV) wall segmentation, right ventricular insertion point detection and creation of a 16-segment model for segmental T1ρ value analysis. Two radiologists (20 and 7 years of MRI experience) provided ground truth annotations. Interobserver variability and segmentation quality were assessed using the Dice coefficient with manual segmentation as reference standard. Global and segmental T1ρ values were compared. Processing times were measured. STATISTICAL TESTS: Intraclass correlation coefficients (ICCs) and Bland-Altman analysis (bias ±2SD); Paired Student's t-tests and one-way ANOVA. A P value <0.05 was considered significant. RESULTS: The automated approach significantly reduced processing time (3 seconds vs. 1 minute 51 seconds ± 22 seconds). In the test set, automated LV wall segmentation closely matched manual results (Dice 81.9% ± 9.0) and closely aligned with interobserver segmentation (Dice 82.2% ± 6.5). Excellent ICCs were achieved on a patient basis (0.94 [95% CI: 0.91 to 0.96]) with bias of -0.93 cm2 ± 6.60. There was no significant difference in global T1ρ values between manual (54.9 msec ± 4.6; 95% CI: 53.8 to 56.0 msec, range: 46.6-70.9 msec) and automated processing (55.4 msec ± 5.1; 95% CI: 54.2 to 56.6 msec; range: 46.4-75.1 msec; P = 0.099). The pipeline demonstrated a high level of agreement with manual-derived T1ρ values at the patient level (ICC = 0.85; bias +0.52 msec ± 5.18). No significant differences in myocardial T1ρ values were found between methods across the 16 segments (P = 0.75). DATA CONCLUSION: Automated myocardial T1ρ mapping shows promise for the rapid and noninvasive assessment of heart disease. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 1.

Relationships between asbestos exposure and pleural plaques: dose and time effects using fractional polynomials.

OBJECTIVE: The aim of this study was to confirm the relationship between several parameters of exposure to asbestos and pleural plaques (PP) using data from a large cohort of retired workers occupationally exposed to asbestos in France. METHOD: A large screening programme, including high-resolution CT (HRCT) examinations at inclusion and two other HRCT campaigns, was organised from 2003 to 2016 in four regions of France for voluntary, formerly asbestos-exposed workers. Exposure to asbestos has been evaluated by industrial hygienists based on the complete work history. The time since first exposure, the time since last exposure, Cumulative Exposure Index and maximum level of exposure to asbestos, were used in logistic regression using fractional polynomials to model the relationship with PP. RESULTS: The study included 5392 subjects with at least one HRCT available. There was a significant non-linear effect of time since first exposure, time since last exposure and Cumulative Exposure Index to asbestos on the presence of PP. The risk of PP increased with increasing Cumulative Exposure Index to asbestos adjusted for time since first exposure, age and smoking status. Models also show that PP odds rise with increasing time since first exposure adjusted for cumulative index exposure, age and smoking status. PP odds decrease when time since last exposure increases. CONCLUSION: The study provides new data on the link between asbestos exposure and the presence of PP using fractional polynomials with non-linear relationships for time exposure parameters and asbestos exposure parameters.

Radiological Diagnosis of Pulmonary Aspergillosis.

Imaging plays an important role in the various forms of Aspergillus-related pulmonary disease. Depending on the immune status of the patient, three forms are described with distinct imaging characteristics: invasive aspergillosis affecting severely immunocompromised patients, chronic pulmonary aspergillosis affecting less severely immunocompromised patients but suffering from a pre-existing structural lung disease, and allergic bronchopulmonary aspergillosis related to respiratory exposure to Aspergillus species in patients with asthma and cystic fibrosis. Computed tomography (CT) has been demonstrated more sensitive and specific than chest radiographs and its use has largely contributed to the diagnosis, follow-up, and evaluation of treatment in each condition. In the last few decades, CT has also been described in the specific context of cystic fibrosis. In this particular clinical setting, magnetic resonance imaging and the recent developments in artificial intelligence have shown promising results.

Generating High-Resolution Synthetic CT from Lung MRI with Ultrashort Echo Times: Initial Evaluation in Cystic Fibrosis.

Background Lung MRI with ultrashort echo times (UTEs) enables high-resolution and radiation-free morphologic imaging; however, its image quality is still lower than that of CT. Purpose To assess the image quality and clinical applicability of synthetic CT images generated from UTE MRI by a generative adversarial network (GAN). Materials and Methods This retrospective study included patients with cystic fibrosis (CF) who underwent both UTE MRI and CT on the same day at one of six institutions between January 2018 and December 2022. The two-dimensional GAN algorithm was trained using paired MRI and CT sections and tested, along with an external data set. Image quality was assessed quantitatively by measuring apparent contrast-to-noise ratio, apparent signal-to-noise ratio, and overall noise and qualitatively by using visual scores for features including artifacts. Two readers evaluated CF-related structural abnormalities and used them to determine clinical Bhalla scores. Results The training, test, and external data sets comprised 82 patients with CF (mean age, 21 years ± 11 [SD]; 42 male), 28 patients (mean age, 18 years ± 11; 16 male), and 46 patients (mean age, 20 years ± 11; 24 male), respectively. In the test data set, the contrast-to-noise ratio of synthetic CT images (median, 303 [IQR, 221-382]) was higher than that of UTE MRI scans (median, 9.3 [IQR, 6.6-35]; P < .001). The median signal-to-noise ratio was similar between synthetic and real CT (88 [IQR, 84-92] vs 88 [IQR, 86-91]; P = .96). Synthetic CT had a lower noise level than real CT (median score, 26 [IQR, 22-30] vs 42 [IQR, 32-50]; P < .001) and the lowest level of artifacts (median score, 0 [IQR, 0-0]; P < .001). The concordance between Bhalla scores for synthetic and real CT images was almost perfect (intraclass correlation coefficient, ≥0.92). Conclusion Synthetic CT images showed almost perfect concordance with real CT images for the depiction of CF-related pulmonary alterations and had better image quality than UTE MRI. Clinical trial registration no. NCT03357562 © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Schiebler and Glide-Hurst in this issue.

Longitudinal Evaluation of Bronchial Changes in Cystic Fibrosis Patients Undergoing Elexacaftor/Tezacaftor/Ivacaftor Therapy Using Lung MRI With Ultrashort Echo-Times.

BACKGROUND: Lung magnetic resonance imaging (MRI) with ultrashort echo-times (UTE-MRI) allows high-resolution and radiation-free imaging of the lung structure in cystic fibrosis (CF). In addition, the combination of elexacaftor/tezacaftor/ivacaftor (ETI) has improved CF clinical outcomes such as need for hospitalization. However, the effect on structural disease still needs longitudinal evaluation at high resolution. PURPOSE: To analyze the effects of ETI on lung structural alterations using UTE-MRI, with a focus on bronchiectasis reversibility. STUDY TYPE: Retrospective. POPULATION: Fifty CF patients (mean age 24.3 ± 9.2; 23 males). FIELD STRENGTH/SEQUENCE: 1.5 T, UTE-MRI. ASSESSMENT: All subjects completed both UTE-MRI and pulmonary function tests (PFTs) during two annual visits (M0 and M12), and 30 of them completed a CT scan. They initiated ETI treatment after M0 within a maximum of 3 months from the annual examinations. Three observers scored a clinical MRI Bhalla score on UTE-MRI. Bronchiectasis reversibility was defined as a reduction in both outer and inner bronchial dimensions. Correlations were searched between the Bhalla score and PFT such as the forced expiratory volume in 1 second percentage predicted (FEV1%p). STATISTICAL TESTS: Comparison was assessed using the paired t-test, correlation using the Spearman correlation test with a significance level of 0.05. Concordance and reproducibility were assessed using intraclass correlation coefficient (ICC). RESULTS: There was a significant improvement in MRI Bhalla score after ETI treatment. UTE-MRI demonstrated bronchiectasis reversibility in a subgroup of 18 out of 50 CF patients (36%). These patients with bronchiectasis reversibility were significantly younger, with lower severity of wall thickening but no difference in mucus plugging extent (P = 0.39) was found. The reproducibility of UTE-MRI evaluations was excellent (ICC ≥ 0.95), was concordant with CT scan (N = 30; ICC ≥ 0.90) and significantly correlated to FEV1% at PFT at M0 (N = 50; r = 0.71) and M12 (N = 50; r = 0.72). DATA CONCLUSION: UTE-MRI is a reproducible tool for the longitudinal follow-up of CF patients, allowing to quantify the response to ETI and demonstrating the reversibility of some structural alterations such as bronchiectasis in a substantial fraction of this study population. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY: Stage 2.

Development and external validation of a prediction model for the transition from mild to moderate or severe form of COVID-19.

OBJECTIVES: COVID-19 pandemic seems to be under control. However, despite the vaccines, 5 to 10% of the patients with mild disease develop moderate to critical forms with potential lethal evolution. In addition to assess lung infection spread, chest CT helps to detect complications. Developing a prediction model to identify at-risk patients of worsening from mild COVID-19 combining simple clinical and biological parameters with qualitative or quantitative data using CT would be relevant to organizing optimal patient management. METHODS: Four French hospitals were used for model training and internal validation. External validation was conducted in two independent hospitals. We used easy-to-obtain clinical (age, gender, smoking, symptoms' onset, cardiovascular comorbidities, diabetes, chronic respiratory diseases, immunosuppression) and biological parameters (lymphocytes, CRP) with qualitative or quantitative data (including radiomics) from the initial CT in mild COVID-19 patients. RESULTS: Qualitative CT scan with clinical and biological parameters can predict which patients with an initial mild presentation would develop a moderate to critical form of COVID-19, with a c-index of 0.70 (95% CI 0.63; 0.77). CT scan quantification improved the performance of the prediction up to 0.73 (95% CI 0.67; 0.79) and radiomics up to 0.77 (95% CI 0.71; 0.83). Results were similar in both validation cohorts, considering CT scans with or without injection. CONCLUSION: Adding CT scan quantification or radiomics to simple clinical and biological parameters can better predict which patients with an initial mild COVID-19 would worsen than qualitative analyses alone. This tool could help to the fair use of healthcare resources and to screen patients for potential new drugs to prevent a pejorative evolution of COVID-19. CLINICAL TRIAL REGISTRATION: NCT04481620. CLINICAL RELEVANCE STATEMENT: CT scan quantification or radiomics analysis is superior to qualitative analysis, when used with simple clinical and biological parameters, to determine which patients with an initial mild presentation of COVID-19 would worsen to a moderate to critical form. KEY POINTS: • Qualitative CT scan analyses with simple clinical and biological parameters can predict which patients with an initial mild COVID-19 and respiratory symptoms would worsen with a c-index of 0.70. • Adding CT scan quantification improves the performance of the clinical prediction model to an AUC of 0.73. • Radiomics analyses slightly improve the performance of the model to a c-index of 0.77.

Artificial intelligence in computed tomography for quantifying lung changes in the era of CFTR modulators.

BACKGROUND: Chest computed tomography (CT) remains the imaging standard for demonstrating cystic fibrosis (CF) airway structural disease in vivo. However, visual scoring systems as an outcome measure are time consuming, require training and lack high reproducibility. Our objective was to validate a fully automated artificial intelligence (AI)-driven scoring system of CF lung disease severity. METHODS: Data were retrospectively collected in three CF reference centres, between 2008 and 2020, in 184 patients aged 4-54 years. An algorithm using three 2D convolutional neural networks was trained with 78 patients' CT scans (23 530 CT slices) for the semantic labelling of bronchiectasis, peribronchial thickening, bronchial mucus, bronchiolar mucus and collapse/consolidation. 36 patients' CT scans (11 435 CT slices) were used for testing versus ground-truth labels. The method's clinical validity was assessed in an independent group of 70 patients with or without lumacaftor/ivacaftor treatment (n=10 and n=60, respectively) with repeat examinations. Similarity and reproducibility were assessed using the Dice coefficient, correlations using the Spearman test, and paired comparisons using the Wilcoxon rank test. RESULTS: The overall pixelwise similarity of AI-driven versus ground-truth labels was good (Dice 0.71). All AI-driven volumetric quantifications had moderate to very good correlations to a visual imaging scoring (p<0.001) and fair to good correlations to forced expiratory volume in 1 s % predicted at pulmonary function tests (p<0.001). Significant decreases in peribronchial thickening (p=0.005), bronchial mucus (p=0.005) and bronchiolar mucus (p=0.007) volumes were measured in patients with lumacaftor/ivacaftor. Conversely, bronchiectasis (p=0.002) and peribronchial thickening (p=0.008) volumes increased in patients without lumacaftor/ivacaftor. The reproducibility was almost perfect (Dice >0.99). CONCLUSION: AI allows fully automated volumetric quantification of CF-related modifications over an entire lung. The novel scoring system could provide a robust disease outcome in the era of effective CF transmembrane conductance regulator modulator therapy.

Quantification of MRI T2 Interstitial Lung Disease Signal-Intensity Volume in Idiopathic Pulmonary Fibrosis: A Pilot Study.

BACKGROUND: Imaging has played a pivotal role in the diagnosis of idiopathic pulmonary fibrosis (IPF). Recent reports suggest that T2 -weighted MRI could be sensitive to monitor signal-intensity modifications of the lung parenchyma, which may relate to the disease activity in IPF. However, there is a lack of automated tools to reproducibly quantify the extent of the disease, especially using MRI. PURPOSE: To assess the feasibility of T2 interstitial lung disease signal-intensity volume quantification using a semiautomated method in IPF. STUDY TYPE: Single center, retrospective. POPULATION: A total of 21 adult IPF patients and four control subjects without lung interstitial abnormalities. FIELD STRENGTH/SEQUENCE: Both free-breathing ultrashort echo time (TE) lung MRI using the spiral volume interpolated breath hold examination (VIBE) sequence (3D-UTE) and T2 -BLADE at 1.5T. ASSESSMENT: Semiautomated segmentation of the lung volume was done using 3D-UTE and registered to the T2 -BLADE images. The interstitial lung disease signal-intensity volume (ISIV) was quantified using a Gaussian mixture model clustering and then normalized to the lung volume to calculate T2 -ISIV. The composite physiological index (CPI) and forced vital capacity (FVC) were measured as known biomarkers of IPF severity. Measurements were performed independently by three readers and averaged. The reproducibility between measurements was also assessed. STATISTICAL TESTS: Reproducibility was assessed using the intraclass correlation coefficient (ICC) and Bland-Altman analysis. Correlations were assessed using Spearman test. Comparison of median was assessed using the Mann-Whitney test. RESULTS: The reproducibility of T2 -ISIV was high, with ICCs = 0.99. Using Bland-Altman analysis, the mean differences were found between -0.8 to 0.1. T2 -ISIV significantly correlated with CPI and FVC (rho = 0.48 and 0.50, respectively; P < 0.05). T2 -ISIV was significantly higher in IPF than in controls (P < 0.05). DATA CONCLUSION: T2 -ISIV appears to be able to reproducibly assess the volumetric extent of abnormal interstitial lung signal-intensity modifications in patients with IPF, and correlate with disease severity. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY STAGE: 1.

Structural and functional changes in COPD: What we have learned from imaging.

Chronic obstructive pulmonary disease (COPD) is the third leading cause of mortality worldwide. It is a heterogeneous disease involving different components of the lung to varying extents. Developments in medical imaging and image analysis techniques provide new insights in the assessment of the structural and functional changes of the disease. This article reviews the leading imaging techniques: CT and MRI of the lung in research settings and clinical routine. Both visual and quantitative methods are reviewed, emphasizing their relevance to patient phenotyping and outcome prediction.

Quantification of MRI T2-weighted High Signal Volume in Cystic Fibrosis: A Pilot Study.

Background In patients with cystic fibrosis (CF), pulmonary structures with high MRI T2 signal intensity relate to inflammatory changes in the lung and bronchi. These areas of pathologic abnormalities can serve as imaging biomarkers. The feasibility of automated quantification is unknown. Purpose To quantify the MRI T2 high-signal-intensity lung volume and T2-weighted volume-intensity product (VIP) by using a black-blood T2-weighted radial fast spin-echo sequence in participants with CF. Materials and Methods Healthy individuals and study participants with CF were prospectively enrolled between January 2017 and November 2017. All participants underwent a lung MRI protocol including T2-weighted radial fast spin-echo sequence. Participants with CF also underwent pulmonary function tests the same day. Participants with CF exacerbation underwent repeat MRI after their treatment with antibiotics. Two observers supervised automated quantification of T2-weighted high-signal-intensity volume (HSV) and T2-weighted VIP independently, and the average score was chosen as consensus. Statistical analysis used the Mann-Whitney test for comparison of medians, correlations used the Spearman test, comparison of paired medians used the Wilcoxon signed rank test, and reproducibility was evaluated by using intraclass correlation coefficient. Results In 10 healthy study participants (median age, 21 years [age range, 18-27 years]; six men) and 12 participants with CF (median age, 18 years [age range, 9-40 years]; eight men), T2-weighted HSV was equal to 0% and 4.1% (range, 0.1%-17%), respectively, and T2-weighted VIP was equal to 0 msec and 303 msec (range, 39-1012 msec), respectively (P < .001). In participants with CF, T2-weighted HSV or T2-weighted VIP were associated with forced expiratory volume in 1 second percentage predicted (ρ = -0.88 and ρ = -0.94, respectively; P < .001). In six participants with CF exacerbation and follow-up after treatment, a decrease in both T2-weighted HSV and T2-weighted VIP was observed (P = .03). The intra- and interobserver reproducibility of MRI were good (intraclass correlation coefficients, >0.99 and >0.99, respectively). Conclusion In patients with cystic fibrosis (CF), automated quantification of lung MRI high-signal-intensity volume was reproducible and correlated with pulmonary function testing severity, and it improved after treatment for CF exacerbation. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Revel and Chassagnon in this issue.

Volumetric quantification of lung MR signal intensities using ultrashort TE as an automated score in cystic fibrosis.

OBJECTIVES: The study aimed to validate automated quantification of high and low signal intensity volumes using ultrashort echo-time MRI, with CT and pulmonary function test (PFT) as references, to assess the severity of structural alterations in cystic fibrosis (CF). METHODS: This prospective study was performed in a single center between May 2015 and September 2017. Participants with CF completed clinical examination, CT, MRI, and PFT the same day during routine clinical follow-up (M0), and then 1 year after (M12) except for CT. Using MRI, percentage high (%MR-HSV), low (%MR-LSV), and total abnormal (%MR-TSV) signal intensity volumes were recorded, as well as their corresponding attenuation values using CT (%CT-HAV, %CT-LAV, %CT-TAV, respectively). Automated quantifications and visual Bhalla score were evaluated independently by two observers. Correlations were assessed using the Spearman test, comparisons using the Mann-Whitney test, and reproducibility using the intraclass correlation coefficient (ICC). RESULTS: A total of 30 participants were enrolled (median age 27 years, 18 men). At M0, there was a good correlation between %MR-HSV and %CT-HAV (ρ = 0.70; p < 0.001) and %MR-LSV and %CT-LAV (ρ = 0.60; p < 0.001). Automated MR metrics correlated to PFTs and Bhalla score (p < 0.05) while %MR-TSV was significantly different between CF with and without respiratory exacerbation (p = 0.01) at both M0 and M12. The variation of %MR-HSV correlated to the variation of FEV1% at PFT (ρ = - 0.49; p = 0.008). Reproducibility was almost perfect (ICCs > 0.95). CONCLUSIONS: Automated quantification of abnormal signal intensity volumes relates to CF severity and allows reproducible cross-sectional and longitudinal assessment. TRIAL REGISTRATION: Clinical trial identifier: NCT02449785 KEY POINTS: • Cross-sectionally, the automated quantifications of high and low signal intensity volumes at UTE correlated to the quantification of high and low attenuation using CT as reference. • Longitudinally, the variation of high signal intensity volume at UTE correlated to the variation of pulmonary function test and was significantly reduced in CF with an improvement in exacerbation status. • Automated quantification of abnormal signal intensity volumes are objective and reproducible tools to assess structural alterations in CF and follow-up longitudinally, for both research and clinical purposes.

Automated Volumetric Quantification of Emphysema Severity by Using Ultrashort Echo Time MRI: Validation in Participants with Chronic Obstructive Pulmonary Disease.

Background The validity of three-dimensional (3D) ultrashort echo time (UTE) MRI for the assessment of emphysema in patients with chronic obstructive pulmonary disease (COPD) at high spatial resolution is, to the knowledge of the authors, unknown. Purpose To assess whether noncontrast agent-enhanced 3D UTE MRI at submillimeter spatial resolution can be used to determine the extent of emphysema by using both qualitative visual scoring and fully automated volumetric quantification. Materials and Methods Twenty-eight participants with COPD and 10 control participants (mean age, 70 years ± 7 [standard deviation] and 64 years ± 4, respectively) were prospectively enrolled between 2015 and 2017. Participants underwent pulmonary function testing, CT, and MRI. CT was used as the reference standard. Qualitative scoring of emphysema extent was performed by two readers. Fully automated quantification of percentage of low-attenuation volume by using a threshold of -950 HU (%LAV-950HU) at CT and percentage of low-signal-intensity volume by using an adaptive threshold of 0.20 (%LSV0.20) at MRI were the respective emphysema indexes. Comparison of means was performed by using Student t test, correlation was determined by using Pearson test, agreement was found by using weighted κ index, and reproducibility was determined by using intraclass correlation coefficient. Diagnostic performance was assessed by calculating the area under the receiver operating characteristics curve (AUC). Results With qualitative scoring, agreement between UTE MRI and CT was good (weighted κ, 0.79; 95% confidence interval: 0.71, 0.83). With automated volumetric quantification, %LSV0.20 was significantly correlated with %LAV-950HU in participants with COPD (r, -0.80; P < .001) and correlated with forced expiratory volume in 1 second percentage predicted (r, -0.55; P = .002). %LSV0.20 was significantly higher in participants with COPD than in control participants (P < .001). The diagnostic performance and reproducibility of %LSV0.20 were good (AUC, 1.00 [95% confidence interval: 0.88, 1.00], and intraclass correlation coefficient, > 0.99, respectively). Conclusion A fully automated method with three-dimensional ultrashort echo time MRI reproducibly quantified the volumetric extent of emphysema in participants with chronic obstructive pulmonary disease. © RSNA, 2019 Online supplemental material is available for this article.

3D ultrashort echo time MRI of the lung using stack-of-spirals and spherical k-Space coverages: Evaluation in healthy volunteers and parenchymal diseases.

BACKGROUND: Ultrashort echo time (UTE) has been shown to improve lung MRI quality in three dimensions. The evaluation of 3D-UTE stack-of-spirals VIBE (3D-USV) sequence for parenchymal diseases and a comparison of performance with that of a spherical mode of acquisition is needed. PURPOSE: To assess MRI quality using a prototypical 3D-USV sequence and to compare performance with that of a spherical acquisition using Pointwise Encoding Time Reduction with Radial Acquisition (PETRA). STUDY TYPE: Monocenter, prospective. POPULATION: Twelve healthy volunteers and 32 adult patients with either cystic fibrosis (CF; n = 16) or interstitial lung disease (ILD; n = 16). FIELD STRENGTH/SEQUENCE: Both free-breathing 3D-USV and PETRA were completed at 1.5T. ASSESSMENT: In healthy volunteers, visual analysis of imaging quality was scored using a Likert scale. Quantitative evaluation of apparent signal ratio (Sr) and contrast ratio (Cr) was measured. Patients with CF and ILD completed both computed tomography (CT) and MRI. Depiction of structural alterations was assessed using dedicated clinical scores. All evaluations were done in consensus by two readers. STATISTICAL TESTS: Comparison of means was assessed using the Wilcoxon signed rank test. Concordance and agreement between CT and MRI were assessed using the intraclass correlation coefficient (ICC) and kappa test. RESULTS: In controls, 3D-USV yielded lower artifacts owing to better automatic respiratory synchronization than PETRA (P < 0.001). However, Sr and Cr of 3D-USV were found significantly lower by 2.25- and 2.36-fold, respectively (P < 0.001). In patients, 3D-USV and PETRA showed comparable performances to assess airway severity in CF (Bhalla score, ICC = 0.89 and ICC = 0.92, respectively) and presence of structural alterations in ILD such as honeycombing (kappa = 0.68 and kappa = 0.69, respectively). DATA CONCLUSION: 3D-USV enables high-resolution morphological imaging of the lung without need of an external device to compensate respiratory motions. Automation and robustness of the method may facilitate clinical application for both airway and interstitial lung investigations. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018;48:1489-1497.

Ventilation and perfusion MRI at a 0.35 T MR-Linac: feasibility and reproducibility study.

BACKGROUND: Hybrid devices that combine radiation therapy and MR-imaging have been introduced in the clinical routine for the treatment of lung cancer. This opened up not only possibilities in terms of accurate tumor tracking, dose delivery and adapted treatment planning, but also functional lung imaging. The aim of this study was to show the feasibility of Non-uniform Fourier Decomposition (NuFD) MRI at a 0.35 T MR-Linac as a potential treatment response assessment tool, and propose two signal normalization strategies for enhancing the reproducibility of the results. METHODS: Ten healthy volunteers (median age 28 ± 8 years, five female, five male) were repeatedly scanned at a 0.35 T MR-Linac using an optimized 2D+t balanced steady-state free precession (bSSFP) sequence for two coronal slice positions. Image series were acquired in normal free breathing with breaks inside and outside the scanner as well as deep and shallow breathing. Ventilation- and perfusion-weighted maps were generated for each image series using NuFD. For intra-volunteer ventilation map reproducibility, a normalization factor was defined based on the linear correlation of the ventilation signal and diaphragm position of each scan as well as the diaphragm motion amplitude of a reference scan. This allowed for the correction of signal dependency on the diaphragm motion amplitude, which varies with breathing patterns. The second strategy, which can be used for ventilation and perfusion, eliminates the dependency on the signal amplitude by normalizing the ventilation/perfusion maps with the average ventilation/perfusion signal within a selected region-of-interest (ROI). The position and size dependency of this ROI was analyzed. To evaluate the performance of both approaches, the normalized ventilation/perfusion-weighted maps were compared and the deviation of the mean ventilation/perfusion signal from the reference was calculated for each scan. Wilcoxon signed-rank tests were performed to test whether the normalization methods can significantly improve the reproducibility of the ventilation/perfusion maps. RESULTS: The ventilation- and perfusion-weighted maps generated with the NuFD algorithm demonstrated a mostly homogenous distribution of signal intensity as expected for healthy volunteers regardless of the breathing maneuver and slice position. Evaluation of the ROI's size and position dependency showed small differences in the performance. Applying both normalization strategies improved the reproducibility of the ventilation by reducing the median deviation of all scans to 9.1%, 5.7% and 8.6% for the diaphragm-based, the best and worst performing ROI-based normalization, respectively, compared to 29.5% for the non-normalized scans. The significance of this improvement was confirmed by the Wilcoxon signed rank test with [Formula: see text] at [Formula: see text]. A comparison of the techniques against each other revealed a significant difference in the performance between best ROI-based normalization and worst ROI ([Formula: see text]) and between best ROI-based normalization and scaling factor ([Formula: see text]), but not between scaling factor and worst ROI ([Formula: see text]). Using the ROI-based approach for the perfusion-maps, the uncorrected deviation of 10.2% was reduced to 5.3%, which was shown to be significant ([Formula: see text]). CONCLUSIONS: Using NuFD for non-contrast enhanced functional lung MRI at a 0.35 T MR-Linac is feasible and produces plausible ventilation- and perfusion-weighted maps for volunteers without history of chronic pulmonary diseases utilizing different breathing patterns. The reproducibility of the results in repeated scans significantly benefits from the introduction of the two normalization strategies, making NuFD a potential candidate for fast and robust early treatment response assessment of lung cancer patients during MR-guided radiotherapy.

Deep Learning for the Automatic Quantification of Pleural Plaques in Asbestos-Exposed Subjects.

OBJECTIVE: This study aimed to develop and validate an automated artificial intelligence (AI)-driven quantification of pleural plaques in a population of retired workers previously occupationally exposed to asbestos. METHODS: CT scans of former workers previously occupationally exposed to asbestos who participated in the multicenter APEXS (Asbestos PostExposure Survey) study were collected retrospectively between 2010 and 2017 during the second and the third rounds of the survey. A hundred and forty-one participants with pleural plaques identified by expert radiologists at the 2nd and the 3rd CT screenings were included. Maximum Intensity Projection (MIP) with 5 mm thickness was used to reduce the number of CT slices for manual delineation. A Deep Learning AI algorithm using 2D-convolutional neural networks was trained with 8280 images from 138 CT scans of 69 participants for the semantic labeling of Pleural Plaques (PP). In all, 2160 CT images from 36 CT scans of 18 participants were used for AI testing versus ground-truth labels (GT). The clinical validity of the method was evaluated longitudinally in 54 participants with pleural plaques. RESULTS: The concordance correlation coefficient (CCC) between AI-driven and GT was almost perfect (>0.98) for the volume extent of both PP and calcified PP. The 2D pixel similarity overlap of AI versus GT was good (DICE = 0.63) for PP, whether they were calcified or not, and very good (DICE = 0.82) for calcified PP. A longitudinal comparison of the volumetric extent of PP showed a significant increase in PP volumes (p < 0.001) between the 2nd and the 3rd CT screenings with an average delay of 5 years. CONCLUSIONS: AI allows a fully automated volumetric quantification of pleural plaques showing volumetric progression of PP over a five-year period. The reproducible PP volume evaluation may enable further investigations for the comprehension of the unclear relationships between pleural plaques and both respiratory function and occurrence of thoracic malignancy.