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Background: Ultrashort echo time (UTE) proton MRI has gained popularity for assessing lung structure and function in pulmonary imaging; however, the development of rapid biomarker extraction and regional quantification has lagged behind due to labor-intensive lung segmentation., Purpose: To evaluate a deep learning (DL) approach for automated lung segmentation to extract image-based biomarkers from functional lung imaging using 3D radial UTE oxygen-enhanced (OE) MRI., Study Type: Retrospective study aimed to evaluate a technical development., Population: Forty-five human subjects, including 16 healthy volunteers, 5 asthma, and 24 patients with cystic fibrosis., Field Strength/sequence: 1.5T MRI, 3D radial UTE (TE = 0.08 msec) sequence., Assessment: Two 3D radial UTE volumes were acquired sequentially under normoxic (21% O 2 ) and hyperoxic (100% O 2 ) conditions. Automated segmentation of the lungs using 2D convolutional encoder-decoder based DL method, and the subsequent functional quantification via adaptive K-means were compared with the results obtained from the reference method, supervised region growing., Statistical Tests: Relative to the reference method, the performance of DL on volumetric quantification was assessed using Dice coefficient with 95% confidence interval (CI) for accuracy, two-sided Wilcoxon signed-rank test for computation time, and Bland-Altman analysis on the functional measure derived from the OE images., Results: The DL method produced strong agreement with supervised region growing for the right (Dice: 0.97; 95% CI = [0.96, 0.97]; P < 0.001) and left lungs (Dice: 0.96; 95% CI = [0.96, 0.97]; P < 0.001). The DL method averaged 46 seconds to generate the automatic segmentations in contrast to 1.93 hours using the reference method (P < 0.001). Bland-Altman analysis showed nonsignificant intermethod differences of volumetric (P ≥ 0.12) and functional measurements (P ≥ 0.34) in the left and right lungs., Data Conclusion: DL provides rapid, automated, and robust lung segmentation for quantification of regional lung function using UTE proton MRI., Level of Evidence: 2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;50:1169-1181., (© 2019 International Society for Magnetic Resonance in Medicine.)

Background: MRI of hyperpolarized 129 Xenon (HP 129 Xe) is increasingly utilized for investigating pulmonary function. The solubility of HP 129 Xe in lung tissue, blood plasma (Barrier), and red blood cells (RBC), with unique chemical shifts, enables spectroscopic imaging of potential imaging biomarkers of gas exchange and microstructural pulmonary physiology., Purpose: To quantify global average and regional repeatability of Barrier:gas, RBC:gas, and RBC:Barrier ratios derived from dissolved-phase 129 Xe imaging and their dependence on intervisit changes in lung inflation volume., Study Type: Prospective., Population: Fourteen healthy volunteers. One subject was unable to complete the study resulting in 13 subjects for analysis (eight female, five male, ages 24-69, 53.8 ± 13.9)., Field Strength: 1.5T., Assessment: Subjects were imaged using a 3D radial 1-point Dixon method to separate Barrier and RBC component signals, at two different timepoints, with ~1 month between visits. RBC:Gas, Barrier:Gas, and RBC:Barrier measures were compared across time and with pulmonary function tests (PFTs)., Statistical Tests: Repeatablilty was quantified using Bland-Altman plots, coefficient of repeatability, coefficient of variation (CV), and intraclass correlation coefficients (ICCs). Dependence of imaging measures on PFTs and lung volume was evaluated using Spearman and Pearson correlation coefficients, respectively. Statistical significance was determined by F-test for intraclass correlations, and t-test for Spearman correlations and regression., Results: Mean RBC:Gas, Barrier:Gas, and RBC:Barrier had CVs of 19.2%, 20.0%, and 11.5%, respectively, and had significant ICCs, equal to 0.78, 0.79, and 0.92, respectively. Intervisit differences in RBC:Barrier were significantly correlated with intervisit differences in DL CO (r = 0.93, P = 0.007). Significant correlations with intervisit lung volume differences and intervisit differences in mean RBC:Gas (r = -0.73, P = 0.005) and Barrier:Gas (r = -0.69, P = 0.009) were found., Data Conclusion: Three commonly used 129 Xe MRI-based measures of gas-exchange show good repeatability, particularly the Barrier:RBC ratio, which did not depend on lung inflation volume and was strongly associated with intervisit changes in DL CO ., Level of Evidence: 1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;50:1182-1190., (© 2019 International Society for Magnetic Resonance in Medicine.)

Rationale: Hyperpolarized 129 Xe MRI enables quantitative evaluation of regional ventilation. To this end, multiple classifiers have been proposed to determine ventilation defect percentage (VDP) as well as other cluster populations. However, consensus has not yet been reached regarding which of these methods to deploy for multicenter clinical trials. Here, we compare two published classification techniques-linear-binning and adaptive K-means-to establish their limits of agreement and their robustness against reduced signal-to-noise ratio (SNR)., Methods: A total of 29 subjects (age: 38.4 ± 19.0 years) were retrospectively identified for inter-method comparison. For each 129 Xe ventilation image, 7 images with reduced SNR were generated with equal decrements relative to the native SNR. All 8 sets of images were then analyzed using both methods independently to classify all lung voxels into four clusters: VDP, low-, medium-, and high-ventilation-percentage (LVP, MVP and HVP). For each cluster, the percentage of the lung it comprised was compared between the two methods, as well as how these values persisted as SNR was degraded., Results: The limits of agreement for calculating VDP were [+0.2%, +4.0%] with a +1.5% bias for binning relative to K-means. However, the inter-method agreement for the other clusters was moderate, with biases of -5.7%, 8.1%, and -4.0% for LVP, MVP, and HVP, respectively. As SNR decreased below ∼4, both methods began reporting values that deviated substantially from the native image. By requiring VDP to remain within ≤1.8% of that calculated from the native image, the minimum tolerable SNR values were 2.4 ± 1.0 for the linear-binning, and 3.5 ± 1.5 for the K-means., Conclusions: Both methods agree well in quantifying VDP, but agreement for LVP and MVP remains variable. We suggest a required SNR threshold be two standard deviations above the minimum value of 3.5 ± 1.5 for robust determination of VDP, suggesting a minimum SNR of 6.6. However, robust quantification of the ventilated clusters required an SNR of 13.4., (Copyright © 2018 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Background: A previous study demonstrated the feasibility of using 3D radial ultrashort echo time (UTE) oxygen-enhanced MRI (UTE OE-MRI) for functional imaging of healthy human lungs. The repeatability of quantitative measures from UTE OE-MRI needs to be established prior to its application in clinical research., Purpose: To evaluate repeatability of obstructive patterns in asthma and cystic fibrosis (CF) with UTE OE-MRI with isotropic spatial resolution and full chest coverage., Study Type: Volunteer and patient repeatability., Population: Eighteen human subjects (five asthma, six CF, and seven normal subjects)., Field Strength/sequence: Respiratory-gated free-breathing 3D radial UTE (80 μs) sequence at 1.5T., Assessment: Two 3D radial UTE volumes were acquired sequentially under normoxic and hyperoxic conditions. A subset of subjects underwent repeat acquisitions on either the same day or ≤15 days apart. Asthma and CF subjects also underwent spirometry. A workflow including deformable registration and retrospective lung density correction was used to compute 3D isotropic percent signal enhancement (PSE) maps. Median PSE (MPSE) and ventilation defect percent (VDP) of the lung were measured from the PSE map., Statistical Tests: The relations between MPSE, VDP, and spirometric measures were assessed using Spearman correlations. The test-retest repeatability was evaluated using Bland-Altman analysis and intraclass correlation coefficients (ICC)., Results: Ventilation measures in normal subjects (MPSE = 8.0%, VDP = 3.3%) were significantly different from those in asthma (MPSE = 6.0%, P = 0.042; VDP = 21.7%, P = 0.018) and CF group (MPSE = 4.5%, P = 0.0006; VDP = 27.2%, P = 0.002). MPSE correlated significantly with forced expiratory lung volume in 1 second percent predicted (ρ = 0.72, P = 0.017). The ICC of the test-retest VDP and MPSE were both ≥0.90. In all subject groups, an anterior/posterior gradient was observed with higher MPSE and lower VDP in the posterior compared to anterior regions (P ≤ 0.0021 for all comparisons)., Data Conclusion: 3D radial UTE OE-MRI supports quantitative differentiation of diseased vs. healthy lungs using either whole lung VDP or MPSE with excellent test-retest repeatability., Level of Evidence: 2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018;47:1287-1297., (© 2017 International Society for Magnetic Resonance in Medicine.)

Rationale and Objectives: To determine lobar ventilation patterns in asthmatic lungs with hyperpolarized 3 He magnetic resonance imaging (HP 3 He MRI)., Materials and Methods: Eighty-two subjects (14 normal, 48 mild-to-moderate asthma, and 20 severe asthma) underwent HP 3 He MRI, computed tomography (CT), and pulmonary function testing. After registering proton to 3 He images, we segmented the lungs from proton MRI and further segmented the five lung lobes (right upper lobe [RUL], right middle lobe [RML], and right lower lobe [RLL]; left upper lobe and left lower lobe [LLL]) by referring to the lobar segmentation from CT. We classified the gas volume into four signal intensity levels as follows: ventilation defect percent (VDP), low ventilation percent, medium ventilation percent, and high ventilation percent. The local signal intensity variations in the ventilated volume were estimated using heterogeneity score (Hs). We compared each ventilation level and Hs measured in the whole lung and lobar regions across the three subject groups., Results: In mild-to-moderate asthma, the RML and RUL showed significantly greater VDP than the two lower lobes (RLL and LLL) (P ≤ .047). In severe asthma, the pattern was more variable with the VDP in the RUL significantly greater than in the RLL (P = .026). In both asthma groups, the lower lobes (RLL and LLL) showed significantly higher high ventilation percent and Hs compared to the three upper lobes (all P ≤ .015)., Conclusions: In asthma, the RML and RUL showed greater ventilation abnormalities, and the RLL and LLL were more highly ventilated with greater local heterogeneity. These findings may facilitate guided bronchoscopic sampling and localized airway treatment in future studies., (Copyright © 2018 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Rationale and Objectives: This study aimed to compare the performance of a semiautomated ventilation defect segmentation approach, adaptive K-means, with manual segmentation of hyperpolarized helium-3 magnetic resonance imaging in subjects with exercise-induced bronchoconstriction (EIB)., Materials and Methods: Six subjects with EIB underwent hyperpolarized helium-3 magnetic resonance imaging and spirometry tests at baseline, post exercise, and recovery over two separate visits. Ventilation defects were analyzed by two methods. First, two independent readers manually segmented ventilation defects. Second, defects were quantified by an adaptive K-means method that corrected for coil sensitivity, applied a vesselness filter to estimate pulmonary vasculature, and segmented defects adaptively based on the overall low-intensity signals in the lungs. These two methods were then compared in four aspects: (1) ventilation defect percent (VDP) measurements, (2) correlation between spirometric measures and measured VDP, (3) regional VDP variations pre- and post exercise challenge, and (4) Dice coefficient for spatial agreement., Results: The adaptive K-means method was ~5 times faster, and the measured VDP bias was under 2%. The correlation between predicted forced expiratory volume in 1 second over forced vital capacity and VDP measured by adaptive K-means (ρ = -0.64, P <0.0001) and by the manual method (ρ = -0.63, P <0.0001) yielded almost identical 95% confidence intervals. Neither method of measuring VDP indicated apical/basal or anterior dependence in this small study cohort., Conclusions: Compared to the manual method, the adaptive K-means method provided faster, reproducible, comparable measures of VDP in EIB and may be applied to a variety of lung diseases., (Copyright © 2016. Published by Elsevier Inc.)

Rationale and Objectives: The purpose of this review is to acquaint the reader with recent advances in ultrashort echo time (UTE) magnetic resonance imaging (MRI) of the lung and its implications for pulmonary MRI when used in conjunction with functional MRI technique.Materials and Methods: We provide an overview of recent technical advances of UTE and explore the advantages of combined structure-function pulmonary imaging in the context of restrictive and obstructive pulmonary diseases such as idiopathic pulmonary fibrosis (IPF) and cystic fibrosis (CF).Results: UTE MRI clearly shows the lung parenchymal changes due to IPF and CF. The use of UTE MRI, in conjunction with established functional lung MRI in chronic lung diseases, will serve to mitigate the need for computed tomography in children.Conclusion: Current limitations of UTE MRI include long scan times, poor delineation of thin-walled structures (e.g. cysts and reticulation) due to limited spatial resolution, low signal to noise ratio, and imperfect motion compensation. Despite these limitations, UTE MRI can now be considered as an alternative to multidetector computed tomography for the longitudinal follow-up of the morphological changes from lung diseases in neonates, children, and young adults, particularly as a complement to the unique functional capabilities of MRI. [ABSTRACT FROM AUTHOR]