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Purpose: Our objective was to train machine-learning algorithms on hyperpolarized He 3 magnetic resonance imaging (MRI) datasets to generate models of accelerated lung function decline in participants with and without chronic-obstructive-pulmonary-disease. We hypothesized that hyperpolarized gas MRI ventilation, machine-learning, and multivariate modeling could be combined to predict clinically-relevant changes in forced expiratory volume in 1 s ( FEV 1 ) across 3 years. Approach: Hyperpolarized He 3 MRI was acquired using a coronal Cartesian fast gradient recalled echo sequence with a partial echo and segmented using a k-means clustering algorithm. A maximum entropy mask was used to generate a region-of-interest for texture feature extraction using a custom-developed algorithm and the PyRadiomics platform. The principal component and Boruta analyses were used for feature selection. Ensemble-based and single machine-learning classifiers were evaluated using area-under-the-receiver-operator-curve and sensitivity-specificity analysis. Results: We evaluated 88 ex-smoker participants with 31 ± 7 months follow-up data, 57 of whom (22 females/35 males, 70 ± 9 years) had negligible changes in FEV 1 and 31 participants (7 females/24 males, 68 ± 9 years) with worsening FEV 1 ≥ 60 mL / year . In addition, 3/88 ex-smokers reported a change in smoking status. We generated machine-learning models to predict FEV 1 decline using demographics, spirometry, and texture features, with the later yielding the highest classification accuracy of 81%. The combined model (trained on all available measurements) achieved the overall best classification accuracy of 82%; however, it was not significantly different from the model trained on MRI texture features alone. Conclusion: For the first time, we have employed hyperpolarized He 3 MRI ventilation texture features and machine-learning to identify ex-smokers with accelerated decline in FEV 1 with 82% accuracy.

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RATIONALE AND OBJECTIVES: Ex-smokers without spirometry or CT evidence of chronic obstructive pulmonary disease (COPD) but with mildly abnormal diffusing capacity of the lungs for carbon monoxide (DLCO) are at higher risk of developing COPD. It remains difficult to make clinical management decisions for such ex-smokers without other objective assessments consistent with COPD. Hence, our objective was to develop a machine-learning and CT texture-analysis pipeline to dichotomize ex-smokers with normal and abnormal DLCO (DLCO≥75%pred and DLCO<75%pred). MATERIALS AND METHODS: In this retrospective study, 71 ex-smokers (50-85yrs) without COPD underwent spirometry, plethysmography, thoracic CT, and 3He MRI to generate ventilation defect percent (VDP) and apparent diffusion coefficients (ADC). PyRadiomics was utilized to extract 496 CT texture-features; Boruta and principal component analysis were used for feature selection and various models were investigated for classification. Machine-learning classifiers were evaluated using area under the receiver operator characteristic curve (AUC), sensitivity, specificity, and F1-measure. RESULTS: Of 71 ex-smokers without COPD, 29 with mildly abnormal DLCO had significantly different MRI ADC (p < .001), residual-volume to total-lung-capacity ratio (p = .003), St. George's Respiratory Questionnaire (p = .029), and six-minute-walk distance (6MWD) (p < .001), but similar relative area of the lung < -950 Hounsfield-units (RA950) (p = .9) compared to 42 ex-smokers with normal DLCO. Logistic-regression machine-learning mixed-model trained on selected texture-features achieved the best classification accuracy of 87%. All clinical and imaging measurements were outperformed by high-high-pass filter high-gray-level-run-emphasis texture-feature (AUC=0.81), which correlated with DLCO (ρ = -0.29, p = .02), MRI ADC (ρ = 0.23, p = .048), and 6MWD (ρ = -0.25, p = .02). CONCLUSION: In ex-smokers with no CT evidence of emphysema, machine-learning models exclusively trained on CT texture-features accurately classified ex-smokers with abnormal diffusing capacity, outperforming conventional quantitative CT measurements.

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Pulmonary imaging measurements using magnetic resonance imaging (MRI) and computed tomography (CT) have the potential to deepen our understanding of chronic obstructive pulmonary disease (COPD) by measuring airway and parenchymal pathologic information that cannot be provided by spirometry. Currently, MRI and CT measurements are not included in mortality risk predictions, diagnosis, or COPD staging. We evaluated baseline pulmonary function, MRI and CT measurements alongside imaging texture-features to predict 10-year all-cause mortality in ex-smokers with (n = 93; 31 females; 70 ± 9years) and without (n = 69; 29 females, 69 ± 9years) COPD. CT airway and vessel measurements, helium-3 (3He) MRI ventilation defect percent (VDP) and apparent diffusion coefficients (ADC) were quantified. MRI and CT texture-features were extracted using PyRadiomics (version2.2.0). Associations between 10-year all-cause mortality and all clinical and imaging measurements were evaluated using multivariable regression model odds-ratios. Machine-learning predictive models for 10-year all-cause mortality were evaluated using area-under-receiver-operator-characteristic-curve (AUC), sensitivity and specificity analyses. DLCO (%pred) (HR = 0.955, 95%CI: 0.934-0.976, p < 0.001), MRI ADC (HR = 1.843, 95%CI: 1.260-2.871, p < 0.001), and CT informational-measure-of-correlation (HR = 3.546, 95% CI: 1.660-7.573, p = 0.001) were the strongest predictors of 10-year mortality. A machine-learning model trained on clinical, imaging, and imaging textures was the best predictive model (AUC = 0.82, sensitivity = 83%, specificity = 84%) and outperformed the solely clinical model (AUC = 0.76, sensitivity = 77%, specificity = 79%). In ex-smokers, regardless of COPD status, addition of CT and MR imaging texture measurements to clinical models provided unique prognostic information of mortality risk that can allow for better clinical management.Clinical Trial Registration: www.clinicaltrials.gov NCT02279329.

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Computed tomography (CT) total-airway-count (TAC) and airway wall-thickness differ across chronic obstructive pulmonary disease (COPD) severities, but longitudinal insights are lacking. The aim of this study was to evaluate longitudinal CT airway measurements over three-years in ex-smokers. In this prospective convenience sample study, ex-smokers with (n = 50; 13 female; age = 70 ± 9 years; pack-years = 43 ± 26) and without (n = 40; 17 female; age = 69 ± 10 years; pack-years = 31 ± 17) COPD completed CT, 3He magnetic resonance imaging (MRI), and pulmonary function tests at baseline and three-year follow-up. CT TAC, airway wall-area (WA), lumen-area (LA), and wall-area percent (WA%) were generated. Emphysema was quantified as the relative-area-of-the-lung with attenuation < -950 Hounsfield-units (RA950). MRI ventilation-defect-percent (VDP) was also quantified. Differences over time were evaluated using paired-samples t tests. Multivariable prediction models using the backwards approach were generated. After three-years, forced-expiratory-volume in 1-second (FEV1) was not different in ex-smokers with (p = 0.4) and without (p = 0.5) COPD, whereas RA950 was (p < 0.001, p = 0.02, respectively). In ex-smokers without COPD, there was no change in TAC (p = 0.2); however, LA (p = 0.009) and WA% (p = 0.01) were significantly different. In ex-smokers with COPD, TAC (p < 0.001), WA (p = 0.04), LA (p < 0.001), and WA% (p < 0.001) were significantly different. In all ex-smokers, TAC was related to VDP (baseline: ρ = -0.30, p = 0.005; follow-up: ρ = -0.33, p = 0.002). In significant multivariable models, baseline airway wall-thickness was predictive of TAC worsening. After three-years, in the absence of FEV1 worsening, TAC diminished only in ex-smokers with COPD and airway walls were thinner in all ex-smokers. These longitudinal findings suggest that the evaluation of CT airway remodeling may be a useful clinical tool for predicting disease progression and managing COPD.Clinical trial registration: www.clinicaltrials.gov NCT02279329.

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Multi-b diffusion-weighted hyperpolarized gas MRI measures pulmonary airspace enlargement using apparent diffusion coefficients (ADC) and mean linear intercepts (Lm). Rapid single-breath acquisitions may facilitate clinical translation, and, hence, we aimed to develop single-breath three-dimensional multi-b diffusion-weighted 129Xe MRI using k-space undersampling. We evaluated multi-b (0, 12, 20, 30 s/cm2) diffusion-weighted 129Xe ADC/morphometry estimates using a fully sampled and retrospectively undersampled k-space with two acceleration-factors (AF = 2 and 3) in never-smokers and ex-smokers with chronic obstructive pulmonary disease (COPD) or alpha-one anti-trypsin deficiency (AATD). For the three sampling cases, mean ADC/Lm values were not significantly different (all p > 0.5); ADC/Lm values were significantly different for the COPD subgroup (0.08 cm2s-1/580 µm, AF = 3; all p < 0.001) as compared to never-smokers (0.05 cm2s-1/300 µm, AF = 3). For never-smokers, mean differences of 7%/7% and 10%/7% were observed between fully sampled and retrospectively undersampled (AF = 2/AF = 3) ADC and Lm values, respectively. For the COPD subgroup, mean differences of 3%/4% and 11%/10% were observed between fully sampled and retrospectively undersampled (AF = 2/AF = 3) ADC and Lm, respectively. There was no relationship between acceleration factor with ADC or Lm (p = 0.9); voxel-wise ADC/Lm measured using AF = 2 and AF = 3 were significantly and strongly related to fully-sampled values (all p < 0.0001). Multi-b diffusion-weighted 129Xe MRI is feasible using two different acceleration methods to measure pulmonary airspace enlargement using Lm and ADC in COPD participants and never-smokers.

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BACKGROUND: Outside eosinophilia, current clinical asthma phenotypes do not show strong relationships with disease pathogenesis or treatment responses. While chest x-ray computed tomography (CT) phenotypes have previously been explored, functional MRI measurements provide complementary phenotypic information. PURPOSE: To derive novel data-driven asthma phenotypic clusters using functional MRI airway biomarkers that better describe airway pathologies in patients. STUDY TYPE: Retrospective. POPULATION: A total of 45 patients with asthma who underwent post-bronchodilator 129 Xe MRI, volume-matched CT, spirometry and plethysmography within a 90-minute visit. FIELD STRENGTH/SEQUENCE: Three-dimensional gradient-recalled echo 129 Xe ventilation sequence at 3 T. ASSESSMENT: We measured MRI ventilation defect percent (VDP), CT airway wall-area percent (WA%), wall-thickness (WT, WT* [*normalized for age/sex/height]), lumen-area (LA), lumen-diameter (D, D*) and total airway count (TAC). Univariate relationships were utilized to select variables for k-means cluster analysis and phenotypic subgroup generation. Spirometry and plethysmography measurements were compared across imaging-based clusters. STATISTICAL TESTS: Spearman correlation (ρ), one-way analysis of variance (ANOVA) or Kruskal-Wallis tests with post hoc Bonferroni correction for multiple comparisons, significance level 0.05. RESULTS: Based on limited common variance (Kaiser-Meyer-Olkin-measure = 0.44), four unique clusters were generated using MRI VDP, TAC, WT* and D* (52 ± 14 years, 27 female). Imaging measurements were significantly different across clusters as was the forced expiratory volume in 1-second (FEV1 %pred ), residual volume/total lung capacity and airways resistance. Asthma-control (P = 0.9), quality-of-life scores (P = 0.7) and the proportions of severe-asthma (P = 0.4) were not significantly different. Cluster1 (n = 15/8 female) reflected mildly abnormal CT airway measurements and FEV1 with moderately abnormal VDP. Cluster2 (n = 12/12 female) reflected moderately abnormal TAC, WT and FEV1 . In Cluster3 and Cluster4 (n = 14/6 female, n = 4/1 female, respectively), there was severely reduced TAC, D and FEV1 , but Cluster4 also had significantly worse, severely abnormal VDP (7 ± 5% vs. 41 ± 12%). DATA CONCLUSION: We generated four proof-of-concept MRI-derived clusters of asthma with distinct structure-function pathologies. Cluster analysis of asthma using 129 Xe MRI in combination with CT biomarkers is feasible and may challenge currently used paradigms for asthma phenotyping and treatment decisions. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage.

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BACKGROUND: Suction and capillary pull are 2 biopsy techniques used in endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA). Although these techniques have been shown to perform comparably in terms of overall diagnostic yield, we hypothesized that the capillary pull technique would be associated with improved rapid on-site evaluation (ROSE) adequacy rates thus allowing for a shorter procedure time. METHODS: One hundred eighteen patients undergoing EBUS-TBNA for any indication were randomized to suction or capillary pull techniques for the first biopsy pass; the technique used for all subsequent passes was based on operator preference and was not recorded. The first pass was subjected to ROSE and an adequacy assessment was given. ROSE slides were also scored for cellularity of diagnostic/lesional cells and blood contamination. The overall procedure time was also recorded. RESULTS: There were no significant differences between suction and capillary pull techniques in terms of ROSE adequacy rates. Cellularity of diagnostic/lesional cells and blood contamination scores were also comparable. There was no significant difference in procedure time for the 2 techniques. CONCLUSION: This study suggests no differences in ROSE outcomes between suction and capillary pull techniques in EBUS-TBNA. The technique used should therefore be left to the discretion of the operator.

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Patient-specific localisation of ventilation defects using hyperpolarised gas magnetic resonance imaging (MRI) introduces the possibility of regionally targeted bronchial thermoplasty (BT) for the treatment of severe asthma. We aimed to demonstrate that BT guided by MRI to ventilation defects reduces the number of radiofrequency activations while resulting in improved asthma quality-of-life and control scores that are non-inferior to standard BT. In a 1-year pilot randomised controlled trial, 14 patients with severe asthma who were clinically eligible to receive BT underwent hyperpolarised gas MRI to characterise ventilation defects and were randomised to MRI-guided or standard BT. End-points were improved Asthma Quality of Life Questionnaire (AQLQ) and Asthma Control Questionnaire (ACQ) scores, the proportion of AQLQ and ACQ responders and the number of radiofrequency activations and bronchoscopy sessions. Participants who underwent MRI-guided BT received 53% fewer radiofrequency activations than those who had standard BT (p=0.003). At 12 months, the mean improvement from baseline was similar between the MRI-guided group (n=5) and the standard group (n=7) for AQLQ score (MRI-guided: 1.8, 95% CI 0.1-3.5, p=0.04; standard: 0.7, 95% CI -0.9-2.3, p=0.30) (p=0.25) and ACQ-5 score (MRI-guided: -1.4, 95% CI -2.6- -0.2, p=0.03; standard: -0.7, 95% CI -1.3-0.0, p=0.04) (p=0.17). A similar proportion of participants in both groups achieved a clinically relevant improvement in AQLQ score (MRI-guided: 80%; standard: 71%) and ACQ-5 score (MRI-guided: 80%; standard: 57%). Hyperpolarised gas MRI-guided BT reduced the number of radiofrequency activations, and resulted in asthma quality of life and control improvements at 12 months that were non-inferior to standard BT.

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Ultra-short echo-time (UTE) magnetic resonance imaging (MRI) provides enhanced visualization of pulmonary structural and functional abnormalities and has shown promise in phenotyping lung disease. Here, we describe the development and evaluation of a lung segmentation approach to facilitate UTE MRI methods for patient-based imaging. The proposed approach employs a k-means algorithm in kernel space for pair-wise feature clustering and imposes image domain continuous regularization, coined as continuous kernel k-means (CKKM). The high-order CKKM algorithm was simplified through upper bound relaxation and solved within an iterative continuous max-flow framework. We combined the CKKM with U-net and atlas-based approaches and comprehensively evaluated the performance on 100 images from 25 patients with asthma and bronchial pulmonary dysplasia enrolled at Robarts Research Institute (Western University, London, Canada) and Centre Hospitalier Universitaire (Sainte-Justine, Montreal, Canada). For U-net, we trained the network five times on a mixture of five different images with under-annotations and applied the model to 64 images from the two centres. We also trained a U-net on five images with full and brush annotations from one centre, and tested the model on 32 images from the other centre. For an atlas-based approach, we employed three atlas images to segment 64 target images from the two centres through straightforward atlas registration and label fusion. We applied the CKKM algorithm to the baseline U-net and atlas outputs and refined the initial segmentation through multi-volume image fusion. The integration of CKKM substantially improved baseline results and yielded, with minimal computational cost, segmentation accuracy, and precision that were greater than some state-of-the-art deep learning models and similar to experienced observer manual segmentation. This suggests that deep learning and atlas-based approaches may be utilized to segment UTE MRI datasets using relatively small training datasets with under-annotations.

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RATIONALE AND OBJECTIVES: The relationships between computed tomography (CT) pulmonary vascularity and MRI ventilation is not well-understood in chronic obstructive pulmonary disease (COPD) patients. Our objective was to evaluate CT pulmonary vascular and MRI ventilation measurements in ex-smokers and to investigate their associations and how such measurements change over time. MATERIALS AND METHODS: Ninety ex-smokers (n = 41 without COPD 71 ± 10 years and n = 49 COPD 71 ± 8 years) provided written informed-consent to an ethics-board approved protocol and underwent imaging and pulmonary-function-tests twice, 31 ± 7 months apart. 3He MRI was acquired to generate ventilation-defect-percent (VDP). CT measurements of the relative area-of-the-lung with attenuation <-950 Hounsfield units (RA950), pulmonary vascular total-blood-volume (TBV) and percent of vessels with radius < one voxel (PV1) were evaluated. RESULTS: At baseline, there were significant differences in RA950 (p = 0.0001), VDP (p = 0.0001), total-blood-volume (p = 0.0001) and PV1 (p = 0.01) between ex-smokers and COPD participants as well as for VDP (p = 0.0001) in COPD participants with and without emphysema. The annual FEV1 change (-40 ± 93 mL/year) was not different among participant subgroups (p = 0.87), but the annual RA950 (p = 0.01) and PV1 (p = 0.007) changes were significantly different in participants with an accelerated annual FEV1 decline as compared to participants with a diminished annual FEV1 decline. There were significant but weak relationships for PV1 with FEV1%pred (p = 0.02), FEV1/FVC (p = 0.001), and log RA950 (p = 0.0001), but not VDP (p=0.20). The mean change in PV1 was also weakly but significantly related to the change in RA950 (p = 0.02). CONCLUSION: CT pulmonary vascular measurements were significantly different in ex-smokers and participants with COPD and related to RA950 but not VDP worsening over 2.5 years.

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RATIONALE AND OBJECTIVES: 129Xe MRI has been developed to noninvasively visualize and quantify the functional consequence of airway obstruction in asthma. Its widespread application requires evidence of intersite reproducibility and agreement. Our objective was to evaluate reproducibility and agreement of 129Xe ventilation MRI measurements in severe asthmatics at two sites. MATERIALS AND METHODS: In seven adults with severe asthma, 129Xe ventilation MRI was acquired pre- and post-bronchodilator at two geographic sites within 24-hours. 129Xe MRI signal-to-noise ratio (SNR) was calculated and ventilation abnormalities were quantified as the whole-lung and slice-by-slice ventilation defect percent (VDP). Intraclass correlation coefficients (ICC) and Bland-Altman analysis were used to determine intersite 129Xe VDP reproducibility and agreement. RESULTS: Whole-lung and slice-by-slice 129Xe VDP measured at both sites were correlated and reproducible (pre-bronchodilator: whole-lung ICC = 0.90, p = 0.005, slice-by-slice ICC = 0.78, p < 0.0001; post-bronchodilator: whole-lung ICC = 0.94, p < 0.0001, slice-by-slice ICC = 0.83, p < 0.0001) notwithstanding intersite differences in the 129Xe-dose-equivalent-volume (101 ± 15 mL site 1, 49 ± 6 mL site 2, p < 0.0001), gas-mixture (129Xe/4He site 1; 129Xe/N2 site 2) and SNR (40 ± 19 site 1, 23 ± 5 site 2, p = 0.02). Qualitative 129Xe gas distribution differences were observed between sites and slice-by-slice 129Xe VDP, but not whole-lung 129Xe VDP, was significantly lower at site 1 (pre-bronchodilator VDP: whole-lung bias = -3%, p > 0.99, slice-by-slice bias = -3%, p = 0.0001; post-bronchodilator VDP: whole-lung bias = -2%, p = 0.59, slice-by-slice-bias = -2%, p = 0.0003). CONCLUSION: 129Xe MRI VDP at two different sites measured within 24-hours in the same severe asthmatics were correlated. Qualitative and quantitative intersite differences in 129Xe regional gas distribution and VDP point to site-specific variability that may be due to differences in gas-mixture composition or SNR.

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Background Pulmonary imaging of chronic obstructive pulmonary disease (COPD) has focused on CT or MRI measurements, but these have not been evaluated in combination. Purpose To generate multiparametric response map (mPRM) measurements in ex-smokers with or without COPD by using volume-matched CT and hyperpolarized helium 3 (3He) MRI. Materials and Methods In this prospective study (https://clinicaltrials.gov, NCT02279329), participants underwent MRI and CT and completed pulmonary function tests, questionnaires, and the 6-minute walk test between December 2010 and January 2019. Disease status was determined by using Global initiative for chronic Obstructive Lung Disease (GOLD) criteria. The mPRM voxel values were generated by using co-registered MRI and CT labels. Kruskal-Wallis and Bonferroni tests were used to determine differences across disease severity, and correlations were determined by using Spearman coefficients. Results A total of 175 ex-smokers (mean age, 69 years ± 9 [standard deviation], 108 men) with or without COPD were evaluated. Ex-smokers without COPD had a larger fraction of normal mPRM voxels (60% vs 37%, 20%, and 7% for GOLD I, II, and III/IV disease, respectively; all P ≤ .001) and a smaller fraction of abnormal voxels, including small airways disease (normal CT, not ventilated: 5% vs 6% [not significant], 11%, and 19% [P ≤ .001 for both] for GOLD I, II, and III/IV disease, respectively) and mild emphysema (normal CT, abnormal apparent diffusion coefficient [ADC]: 33% vs 54%, 56%, and 54% for GOLD I, II, and III/IV disease respectively; all P ≤ .001). Normal mPRM measurements were positively correlated with forced expiratory volume in 1 second (FEV1) (r = 0.65, P < .001), the FEV1-to-forced vital capacity ratio (r = 0.81, P < .001), and diffusing capacity (r = 0.75, P < .001) and were negatively correlated with worse quality of life (r = -0.48, P < .001). Abnormal mPRM measurements of small airways disease (normal CT, not ventilated) and mild emphysema (normal CT, abnormal ADC) were negatively correlated with FEV1 (r = -0.65 and -0.42, respectively; P < .001) and diffusing capacity (r = -0.53 and -0.60, respectively; P < .001) and were positively correlated with worse quality of life (r = 0.45 and r = 0.33, respectively; P < .001), both of which were present in ex-smokers without COPD. Conclusion Multiparametric response maps revealed two abnormal structure-function results related to emphysema and small airways disease, both of which were unexpectedly present in ex-smokers with normal spirometry and CT findings. © RSNA, 2020 Online supplemental material is available for this article.

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Rationale: In patients with asthma, X-ray computed tomography (CT) has provided evidence of thickened airway walls and airway occlusions, but the total number of CT-visible airways and its relationship with disease severity is unknown.Objectives: To measure CT total airway count (TAC) in asthma and evaluate relationships with asthma severity, airway morphology, pulmonary function, and magnetic resonance imaging (MRI) ventilation.Methods: Participants underwent post-bronchodilator inspiratory CT, and prebronchodilator and post-bronchodilator spirometry and hyperpolarized 3He MRI. CT TAC was quantified as the sum of airways in the segmented airway tree, and airway wall area percent (WA%) and lumen area were measured. MRI ventilation abnormalities were quantified as the ventilation defect percent.Measurements and Main Results: We evaluated 70 participants, including 15 Global Initiative for Asthma (GINA) steps 1 to 3, 19 GINA 4, and 36 GINA 5 participants with asthma. As compared with GINA 1 to 3, TAC was significantly diminished in GINA 4 (P = 0.03) and GINA 5 (P = 0.045). Terminal airway intraluminal occlusion was present in 5 (2 GINA 4 and 3 GINA 5) of 70 participants. Sub-subsegmental airways were CT-invisible or missing in 69 out of 70 participants; the most common number of missing sub-subsegments was 10. Participants with ≥10 missing subsegments had worse WA% (P < 0.0001), lumen area (P < 0.0001), and ventilation defect percent (P = 0.03) than those with <10 missing subsegments. In a multivariable model, TAC (standardized regression coefficient = 0.50; P = 0.001) independently predicted FEV1 (R2 = 0.27; P = 0.003) and, in a separate model, TAC (standardized regression coefficient = -0.53; P < 0.0001) independently predicted airway WA% (R2 = 0.32; P = 0.0001).Conclusions: TAC was significantly diminished in participants with greater asthma severity and was related to airway wall thickness and ventilation defects. Fewer airways in severe than in mild asthma challenges our understanding of airway disease in asthma.Clinical trial registered with www.clinicaltrials.gov (NCT02351141).

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PURPOSE: Multi-b diffusion-weighted hyperpolarized inhaled-gas MRI provides imaging biomarkers of terminal airspace enlargement including ADC and mean linear intercept (Lm ), but clinical translation has been limited because image acquisition requires relatively long or multiple breath-holds that are not well-tolerated by patients. Therefore, we aimed to accelerate single breath-hold 3D multi-b diffusion-weighted 129 Xe MRI, using k-space undersampling in imaging direction using a different undersampling pattern for different b-values combined with the stretched exponential model to generate maps of ventilation, apparent transverse relaxation time constant ( T2∗ ), ADC, and Lm values in a single, short breath-hold; accelerated and non-accelerated measurements were directly compared. METHODS: We evaluated multi-b (0, 12, 20, 30, and 45.5 s/cm2 ) diffusion-weighted 129 Xe T2∗ /ADC/morphometry estimates using acceleration factor (AF = 1 and 7) and multi-breath sampling in 3 volunteers (HV), and 6 participants with alpha-1 antitrypsin deficiency (AATD). RESULTS: For the HV subgroup, mean differences of 5%, 2%, and 8% were observed between fully sampled and undersampled k-space for ADC, Lm , and T2∗ values, respectively. For the AATD subgroup, mean differences were 9%, 6%, and 12% between fully sampled and undersampled k-space for ADC, Lm and T2∗ values, respectively. Although mean differences of 1% and 4.5% were observed between accelerated and multi-breath sampled ADC and Lm values, respectively, mean ADC/Lm estimates were not significantly different from corresponding mean ADCM /LmM or mean ADCA /LmA estimates (all P > 0.60 , A = undersampled and M = multi-breath sampled). CONCLUSIONS: Accelerated multi-b diffusion-weighted 129 Xe MRI is feasible at AF = 7 for generating pulmonary ADC and Lm in AATD and normal lung.

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Recent pulmonary functional MRI findings of spatially and temporally persistent ventilation abnormalities in patients with asthma contrast with previous in silico modeling studies that suggest that in asthma, ventilation defects may be randomly distributed. In a case study that used pulmonary MRI, CT imaging, and pulmonary function tests, we prospectively evaluated over the course of 7 years, nonidentical female adult twins, each with a lifelong history of asthma. We evaluated pulmonary function and MRI ventilation heterogeneity at baseline and follow-up after 7 years. In both twins, there was a spatially identical MRI ventilation defect and an abnormal subsegmental left-sided upper lobe airway that persisted in the same spatial location after 7 years. If ventilation defects are randomly distributed, this bears a probability of approximately one per 130,000 people. Our MRI observations in related patients with asthma suggest that ventilation abnormalities may not be randomly distributed in patients with asthma and persist distal to airway abnormalities for long periods of time.

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Background Fixed airflow limitation and ventilation heterogeneity are common in chronic obstructive pulmonary disease (COPD). Conventional noncontrast CT provides airway and parenchymal measurements but cannot be used to directly determine lung function. Purpose To develop, train, and test a CT texture analysis and machine-learning algorithm to predict lung ventilation heterogeneity in participants with COPD. Materials and Methods In this prospective study (ClinicalTrials.gov: NCT02723474; conducted from January 2010 to February 2017), participants were randomized to optimization (n = 1), training (n = 67), and testing (n = 27) data sets. Hyperpolarized (HP) helium 3 (3He) MRI ventilation maps were co-registered with thoracic CT to provide ground truth labels, and 87 quantitative imaging features were extracted and normalized to lung averages to generate 174 features. The volume-of-interest dimension and the training data sampling method were optimized to maximize the area under the receiver operating characteristic curve (AUC). Forward feature selection was performed to reduce the number of features; logistic regression, linear support vector machine, and quadratic support vector machine classifiers were trained through fivefold cross validation. The highest-performing classification model was applied to the test data set. Pearson coefficients were used to determine the relationships between the model, MRI, and pulmonary function measurements. Results The quadratic support vector machine performed best in training and was applied to the test data set. Model-predicted ventilation maps had an accuracy of 88% (95% confidence interval [CI]: 88%, 88%) and an AUC of 0.82 (95% CI: 0.82, 0.83) when the HP 3He MRI ventilation maps were used as the reference standard. Model-predicted ventilation defect percentage (VDP) was correlated with VDP at HP 3He MRI (r = 0.90, P < .001). Both model-predicted and HP 3He MRI VDP were correlated with forced expiratory volume in 1 second (FEV1) (model: r = -0.65, P < .001; MRI: r = -0.70, P < .001), ratio of FEV1 to forced vital capacity (model: r = -0.73, P < .001; MRI: r = -0.75, P < .001), diffusing capacity (model: r = -0.69, P < .001; MRI: r = -0.65, P < .001), and quality-of-life score (model: r = 0.59, P = .001; MRI: r = 0.65, P < .001). Conclusion Model-predicted ventilation maps generated by using CT textures and machine learning were correlated with MRI ventilation maps (r = 0.90, P < .001). © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Fain in this issue.

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Background Longitudinal progression to irreversible airflow limitation occurs in approximately 10% of patients with asthma, but it is difficult to identify patients who are at risk for this transition. Purpose To investigate 6-year longitudinal changes in hyperpolarized helium 3 (3He) MRI ventilation defects in study participants with mild-to-moderate asthma and identify predictors of longitudinal changes in postbronchodilator forced expiratory volume in 1 second (FEV1) reversibility Materials and Methods Spirometry and hyperpolarized 3He MRI were evaluated in participants with mild-to-moderate asthma in two prospectively planned visits approximately 6 years apart. Participants underwent methacholine challenge at baseline (January 2010 to April 2011) and pre- and postbronchodilator evaluations at follow-up (November 2016 to June 2017). FEV1 and MRI ventilation defects, quantified as ventilation defect volume (VDV), were compared between visits by using paired t tests. Participants were dichotomized by postbronchodilator change in FEV1 at follow-up, and differences between reversible and not-reversible groups were determined by using unpaired t tests. Multivariable models were generated to explain postbronchodilator FEV1 reversibility at follow-up. Results Eleven participants with asthma (mean age, 42 years ± 9 [standard deviation]; seven men) were evaluated at baseline and after mean 78 months ± 7. Medications, exacerbations, FEV1 (76% predicted vs 76% predicted; P = .91), and VDV (240 mL vs 250 mL; P = .92) were not different between visits. In eight of 11 participants (73%), MRI ventilation defects at baseline were at the same location in the lung at follow-up MRI. In the remaining three participants (27%), MRI ventilation defects worsened at the same lung locations as depicted at baseline methacholine-induced ventilation. At follow-up, postbronchodilator FEV1 was not reversible in six of 11 participants; the concentration of methacholine to decrease FEV1 by 20% (PC20) was greater in FEV1-irreversible participants at follow-up (P = .01). In a multivariable model, baseline MRI VDV helped to predict postbronchodilator reversibility at follow-up (R 2 = 0.80; P < .01), but PC20, age, and FEV1 did not (R 2 = 0.63; P = .15). Conclusion MRI-derived, spatially persistent ventilation defects predict postbronchodilator reversibility 78 months ± 7 later for participants with mild-to-moderate asthma in whom there were no changes in lung function, medication, or exacerbations. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Stojanovska in this issue.

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There are serious clinical gaps in our understanding of chronic lung disease that require novel, sensitive, and noninvasive in vivo measurements of the lung parenchyma to measure disease pathogenesis and progressive changes over time as well as response to treatment. Until recently, our knowledge and appreciation of the tissue changes that accompany lung disease has depended on ex vivo biopsy and concomitant histological and stereological measurements. These measurements have revealed the underlying pathologies that drive lung disease and have provided important observations about airway occlusion, obliteration of the terminal bronchioles and airspace enlargement, or fibrosis and their roles in disease initiation and progression. ex vivo tissue stereology and histology are the established gold standards and, more recently, micro-computed tomography (CT) measurements of ex vivo tissue samples has also been employed to reveal new mechanistic findings about the progression of obstructive lung disease in patients. While these approaches have provided important understandings using ex vivo analysis of excised samples, recently developed hyperpolarized noble gas MRI methods provide an opportunity to noninvasively measure acinar duct and terminal airway dimensions and geometry in vivo, and, without radiation burden. Therefore, in this review we summarize emerging pulmonary MRI morphometry methods that provide noninvasive in vivo measurements of the lung in patients with bronchopulmonary dysplasia and chronic obstructive pulmonary disease, among others. We discuss new findings, future research directions, as well as clinical opportunities to address current gaps in patient care and for testing of new therapies. Level of Evidence: 5 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2019;50:28-40.

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