Rapid pulmonary 129Xe ventilation MRI of discharged COVID-19 patients with zigzag sampling.

PURPOSE: To demonstrate the feasibility of zigzag sampling for 3D rapid hyperpolarized 129Xe ventilation MRI in human. METHODS: Zigzag sampling in one direction was combined with gradient-recalled echo sequence (GRE-zigzag-Y) to acquire hyperpolarized 129Xe ventilation images. Image quality was compared with a balanced SSFP (bSSFP) sequence with the same spatial resolution for 12 healthy volunteers (HVs). For another 8 HVs and 9 discharged coronavirus disease 2019 subjects, isotropic resolution 129Xe ventilation images were acquired using zigzag sampling in two directions through GRE-zigzag-YZ. 129Xe ventilation defect percent (VDP) was quantified for GRE-zigzag-YZ and bSSFP acquisitions. Relationships and agreement between these VDP measurements were evaluated using Pearson correlation coefficient (r) and Bland-Altman analysis. RESULTS: For 12 HVs, GRE-zigzag-Y and bSSFP required 2.2 s and 10.5 s, respectively, to acquire 129Xe images with a spatial resolution of 3.96 × 3.96 × 10.5 mm3. Structural similarity index, mean absolute error, and Dice similarity coefficient between the two sets of images and ventilated lung regions were 0.85 ± 0.03, 0.0015 ± 0.0001, and 0.91 ± 0.02, respectively. For another 8 HVs and 9 coronavirus disease 2019 subjects, 129Xe images with a nominal spatial resolution of 2.5 × 2.5 × 2.5 mm3 were acquired within 5.5 s per subject using GRE-zigzag-YZ. VDP provided by GRE-zigzag-YZ was strongly correlated (R2 = 0.93, p < 0.0001) with that generated by bSSFP with minimal biases (bias = -0.005%, 95% limit-of-agreement = [-0.414%, 0.424%]). CONCLUSION: Zigzag sampling combined with GRE sequence provides a way for rapid 129Xe ventilation imaging.

Assessment of Global and Regional Lung Compliance in Pulmonary Fibrosis With Hyperpolarized Gas MRI.

BACKGROUND: Lung compliance, a biomarker of pulmonary fibrosis, is generally measured globally. Hyperpolarized 129Xe gas MRI offers the potential to evaluate lung compliance regionally, allowing for visualization of changes in lung compliance associated with fibrosis. PURPOSE: To assess global and regional lung compliance in a rat model of pulmonary fibrosis using hyperpolarized 129Xe gas MRI. STUDY TYPE: Prospective. ANIMAL MODEL: Twenty Sprague-Dawley male rats with bleomycin-induced fibrosis model (N = 10) and saline-treated controls (N = 10). FIELD STRENGTH/SEQUENCE: 7-T, fast low-angle shot (FLASH) sequence. ASSESSMENT: Lung compliance was determined by fitting lung volumes derived from segmented 129Xe MRI with an iterative selection method, to corresponding airway pressures. Similarly, lung compliance was obtained with computed tomography for cross-validation. Direction-dependencies of lung compliance were characterized by regional lung compliance ratios (R) in different directions. Pulmonary function tests (PFTs) and histological analysis were used to validate the pulmonary fibrosis model and assess its correlation with 129Xe lung compliance. STATISTICAL TESTS: Shapiro-Wilk tests, unpaired and paired t-tests, Mann-Whitney U and Wilcoxon signed-rank tests, and Pearson correlation coefficients. P < 0.05 was considered statistically significant. RESULTS: For the entire lung, the global and regional lung compliance measured with 129Xe gas MRI showed significant differences between the groups, and correlated with the global lung compliance measured using PFTs (global: r = 0.891; regional: r = 0.873). Additionally, for the control group, significant difference was found in mean regional compliance between areas, eg, 0.37 (0.32, 0.39) × 10-4 mL/cm H2O and 0.47 (0.41, 0.56) × 10-4 mL/cm H2O for apical and basal lung, respectively. The apical-basal direction R was 1.12 ± 0.09 and 1.35 ± 0.13 for fibrosis and control groups, respectively, indicating a significant difference. DATA CONCLUSION: Our findings demonstrate the feasibility of using hyperpolarized gas MRI to assess regional lung compliance. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 1.

Assessment of pulmonary physiological changes caused by aging, cigarette smoking, and COPD with hyperpolarized 129Xe magnetic resonance.

OBJECTIVE: To comprehensively assess the impact of aging, cigarette smoking, and chronic obstructive pulmonary disease (COPD) on pulmonary physiology using 129Xe MR. METHODS: A total of 90 subjects were categorized into four groups, including healthy young (HY, n = 20), age-matched control (AMC, n = 20), asymptomatic smokers (AS, n = 28), and COPD patients (n = 22). 129Xe MR was utilized to obtain pulmonary physiological parameters, including ventilation defect percent (VDP), alveolar sleeve depth (h), apparent diffusion coefficient (ADC), total septal wall thickness (d), and ratio of xenon signal from red blood cells and interstitial tissue/plasma (RBC/TP). RESULTS: Significant differences were found in the measured VDP (p = 0.035), h (p = 0.003), and RBC/TP (p = 0.003) between the HY and AMC groups. Compared with the AMC group, higher VDP (p = 0.020) and d (p = 0.048) were found in the AS group; higher VDP (p < 0.001), d (p < 0.001) and ADC (p < 0.001), and lower h (p < 0.001) and RBC/TP (p < 0.001) were found in the COPD group. Moreover, significant differences were also found in the measured VDP (p < 0.001), h (p < 0.001), ADC (p < 0.001), d (p = 0.008), and RBC/TP (p = 0.032) between the AS and COPD groups. CONCLUSION: Our findings indicate that pulmonary structure and functional changes caused by aging, cigarette smoking, and COPD are various, and show a progressive deterioration with the accumulation of these risk factors, including cigarette smoking and COPD. CLINICAL RELEVANCE STATEMENT: Pathophysiological changes can be difficult to comprehensively understand due to limitations in common techniques and multifactorial etiologies. 129Xe MRI can demonstrate structural and functional changes caused by several common factors and can be used to better understand patients' underlying pathology. KEY POINTS: Standard techniques for assessing pathophysiological lung function changes, spirometry, and chest CT come with limitations. 129Xe MR demonstrated progressive deterioration with accumulation of the investigated risk factors, without these limitations. 129Xe MR can assess lung changes related to these risk factors to stage and evaluate the etiology of the disease.

Pulmonary Ventilation Maps Generated with Free-breathing Proton MRI and a Deep Convolutional Neural Network.

Background Hyperpolarized noble gas MRI helps measure lung ventilation, but clinical translation remains limited. Free-breathing proton MRI may help quantify lung function using existing MRI systems without contrast material and may assist in providing information about ventilation not visible to the eye or easily extracted with segmentation methods. Purpose To explore the use of deep convolutional neural networks (DCNNs) to generate synthetic MRI ventilation scans from free-breathing MRI (deep learning [DL] ventilation MRI)-derived specific ventilation maps as a surrogate of noble gas MRI and to validate this approach across a wide range of lung diseases. Materials and Methods In this secondary analysis of prospective trials, 114 paired noble gas MRI and two-dimensional free-breathing MRI scans were obtained in healthy volunteers with no history of chronic or acute respiratory disease and in study participants with a range of different obstructive lung diseases, including asthma, bronchiectasis, chronic obstructive pulmonary disease, and non-small-cell lung cancer between September 2013 and April 2018 (ClinicalTrials.gov identifiers: NCT03169673, NCT02351141, NCT02263794, NCT02282202, NCT02279329, and NCT02002052). A U-Net-based DCNN model was trained to map free-breathing proton MRI to hyperpolarized helium 3 (3He) MRI ventilation and validated using a sixfold validation. During training, the DCNN ventilation maps were compared with noble gas MRI scans using the Pearson correlation coefficient (r) and mean absolute error. DCNN ventilation images were segmented for ventilation and ventilation defects and were compared with noble gas MRI scans using the Dice similarity coefficient (DSC). Relationships were evaluated with the Spearman correlation coefficient (rS). Results One hundred fourteen study participants (mean age, 56 years ± 15 [standard deviation]; 66 women) were evaluated. As compared with 3He MRI, DCNN model ventilation maps had a mean r value of 0.87 ± 0.08. The mean DSC for DL ventilation MRI and 3He MRI ventilation was 0.91 ± 0.07. The ventilation defect percentage for DL ventilation MRI was highly correlated with 3He MRI ventilation defect percentage (rS = 0.83, P < .001, mean bias = -2.0% ± 5). Both DL ventilation MRI (rS = -0.51, P < .001) and 3He MRI (rS = -0.61, P < .001) ventilation defect percentage were correlated with the forced expiratory volume in 1 second. The DCNN model required approximately 2 hours for training and approximately 1 second to generate a ventilation map. Conclusion In participants with diverse pulmonary pathologic findings, deep convolutional neural networks generated ventilation maps from free-breathing proton MRI trained with a hyperpolarized noble-gas MRI ventilation map data set. The maps showed correlation with noble gas MRI ventilation and pulmonary function measurements. © RSNA, 2020 See also the editorial by Vogel-Claussen in this issue.

Cine and late gadolinium enhancement MRI registration and automated myocardial infarct heterogeneity quantification.

PURPOSE: To develop an approach for automated quantification of myocardial infarct heterogeneity in late gadolinium enhancement (LGE) cardiac MRI. METHODS: We acquired 2D short-axis cine and 3D LGE in 10 pigs with myocardial infarct. The 2D cine myocardium was segmented and registered to the LGE images. LGE image signal intensities within the warped cine myocardium masks were analyzed to determine the thresholds of infarct core (IC) and gray zone (GZ) for the standard-deviation (SD) and full-width-at-halfmaximum (FWHM) methods. The initial IC, GZ, and IC + GZ segmentations were postprocessed using a normalized cut approach. Cine segmentation and cine-LGE registration accuracies were evaluated using dice similarity coefficient and average symmetric surface distance. Automated IC, GZ, and IC + GZ volumes were compared with manual results using Pearson correlation coefficient (r), Bland-Altman analyses, and intraclass correlation coefficient. RESULTS: For n = 87 slices containing scar, we achieved cine segmentation dice similarity coefficient = 0.87 ± 0.12, average symmetric surface distance = 0.94 ± 0.74 mm (epicardium), and 1.03 ± 0.82 mm (endocardium) in the scar region. For cine-LGE registration, dice similarity coefficient was 0.90 ± 0.06 and average symmetric surface distance was 0.72 ± 0.39 mm (epicardium) and 0.86 ± 0.53 mm (endocardium) in the scar region. For both SD and FWHM methods, automated IC, GZ, and IC + GZ volumes were strongly (r > 0.70) correlated with manual measurements, and the correlations were not significantly different from interobserver correlations (P > .05). The agreement between automated and manual scar volumes (intraclass correlation coefficient = 0.85-0.96) was similar to that between two observers (intraclass correlation coefficient = 0.81-0.99); automated scar segmentation errors were not significantly different from interobserver segmentation differences (P > .05). CONCLUSIONS: Our approach provides fully automated cine-LGE MRI registration and LGE myocardial infarct heterogeneity quantification in preclinical studies.

Accelerated 129 Xe MRI morphometry of terminal airspace enlargement: Feasibility in volunteers and those with alpha-1 antitrypsin deficiency.

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.

A framework for Fourier-decomposition free-breathing pulmonary 1 H MRI ventilation measurements.

PURPOSE: To develop a rapid Fourier decomposition (FD) free-breathing pulmonary 1 H MRI (FDMRI) image processing and biomarker pipeline for research use. METHODS: We acquired MRI in 20 asthmatic subjects using a balanced steady-state free precession (bSSFP) sequence optimized for ventilation imaging. 2D 1 H MRI series were segmented by enforcing the spatial similarity between adjacent images and the right-to-left lung volume-ratio. The segmented lung series were co-registered using a coarse-to-fine deformable registration framework that used dual optimization techniques. All pairwise registrations were implemented in parallel and FD was performed to generate 2D ventilation-weighted maps and ventilation-defect-percent (VDP). Lung segmentation and registration accuracy were evaluated by comparing algorithm and manual lung-masks, deformed manual lung-masks, and fiducials in the moving and fixed images using Dice-similarity-coefficient (DSC), mean-absolute-distance (MAD), and target-registration-error (TRE). The relationship of FD-VDP and 3 He-VDP was evaluated using the Pearson-correlation-coefficient (r) and Bland Altman analysis. Algorithm reproducibility was evaluated using the coefficient-of-variation (CoV) and intra-class-correlation-coefficient (ICC) for segmentation, registration, and FD-VDP components. RESULTS: For lung segmentation, there was a DSC of 95 ± 1.5% and MAD of 2.3 ± 0.5 mm, and for registration there was a DSC of 97 ± 0.8%, MAD of 1.6 ± 0.4 mm and TRE of 3.6 ± 1.2 mm. Reproducibility for segmentation DSC (CoV/ICC = 0.5%/0.92), registration TRE (CoV/ICC = 0.4%/0.98), and FD-VDP (Cov/ICC = 3.9%/0.97) was high. The pipeline required 10 min/subject. FD-VDP was correlated with 3 He-VDP (r = 0.69, P < 0.001) although there was a bias toward lower FD-VDP (bias = -4.9%). CONCLUSIONS: We developed and evaluated a pipeline that provides a rapid and precise method for FDMRI ventilation maps.

Rapid single-breath hyperpolarized noble gas MRI-based biomarkers of airspace enlargement.

BACKGROUND: Multi-b diffusion-weighted hyperpolarized-gas MRI measures pulmonary airspace-enlargement using apparent diffusion coefficients (ADCs) and mean-linear-intercepts (Lm ). PURPOSE: To develop single-breath 3D multi-b diffusion-weighted 3 He and 129 Xe MRI using k-space undersampling. Rapid, cost-efficient, single-breath acquisitions may facilitate clinical translation. STUDY TYPE: Prospective. SUBJECTS: We evaluated 12 participants, including nine subjects (mean age = 69 ± 9) who were included in the retrospective experiment and three chronic pulmonary obstruction disease (COPD) patients (mean age = 81 ± 6) who participated in the prospective study. FIELD STRENGTH: A whole-body 3 T 2D/3D fast gradient recall echo (FGRE) sequence. ASSESSMENT: Hyperpolarized 3 He/129 Xe MRI, spirometry, plethysmography computed tomography (CT). We evaluated 129 Xe ADC/morphometry estimates by retrospectively undersampling previously acquired fully sampled multibreath, multi-b diffusion-weighted data. Next, we prospectively evaluated the feasibility of accelerated (AF = 7) 3 He MRI static-ventilation/T2 * (extra short-TE, b = 0 image) and ADC/morphometry (five b-values) maps using a single gas-dose and 16-second breath-hold. To conservatively evaluate cost-improvement, we compared total costs of single vs. multiple 129 Xe doses. STATISTICAL TESTS: Multivariate analysis of variance, independent t-tests and voxel-by-voxel basis difference test. RESULTS: For the retrospectively undersampled 129 Xe data, a nonsignificant mean difference for ADC/Lm of 14%/12%, 12%/8%, and 11%/9% was observed (all, P > 0.4) between the fully sampled and accelerated data for the never-smoker, COPD, and alpha-1 antitrypsin deficiency (AATD) groups, respectively. The control never-smoker group had significantly lower ADC (P < 0.001) and Lm (P < 0.001) than the COPD/AATD group for both fully sampled and accelerated data. For the prospectively acquired 3 He MRI data, static-ventilation, T2 *, ADC, and morphometry maps were acquired using a single 16-second breath-hold scan and single gas dose. Accelerated imaging resulted in cost savings of ~$US 1000/patient, a conservative estimate based on 129 Xe MRI dose savings (single vs. five doses). DATA CONCLUSION: This is a proof-of-concept demonstration of accelerated (7×) morphometry that shows that less cost- and time-efficient multibreath methods that lead to variability and patient fatigue may be avoided in the future. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2018.

Free-breathing Pulmonary MR Imaging to Quantify Regional Ventilation.

Purpose To measure regional specific ventilation with free-breathing hydrogen 1 (1H) magnetic resonance (MR) imaging without exogenous contrast material and to investigate correlations with hyperpolarized helium 3 (3He) MR imaging and pulmonary function test measurements in healthy volunteers and patients with asthma. Materials and Methods Subjects underwent free-breathing 1H and static breath-hold hyperpolarized 3He MR imaging as well as spirometry and plethysmography; participants were consecutively recruited between January and June 2017. Free-breathing 1H MR imaging was performed with an optimized balanced steady-state free-precession sequence; images were retrospectively grouped into tidal inspiration or tidal expiration volumes with exponentially weighted phase interpolation. MR imaging volumes were coregistered by using optical flow deformable registration to generate 1H MR imaging-derived specific ventilation maps. Hyperpolarized 3He MR imaging- and 1H MR imaging-derived specific ventilation maps were coregistered to quantify regional specific ventilation within hyperpolarized 3He MR imaging ventilation masks. Differences between groups were determined with the Mann-Whitney test and relationships were determined with Spearman (ρ) correlation coefficients. Statistical analyses were performed with software. Results Thirty subjects (median age: 50 years; interquartile range [IQR]: 30 years), including 23 with asthma and seven healthy volunteers, were evaluated. Both 1H MR imaging-derived specific ventilation and hyperpolarized 3He MR imaging-derived ventilation percentage were significantly greater in healthy volunteers than in patients with asthma (specific ventilation: 0.14 [IQR: 0.05] vs 0.08 [IQR: 0.06], respectively, P < .0001; ventilation percentage: 99% [IQR: 1%] vs 94% [IQR: 5%], P < .0001). For all subjects, 1H MR imaging-derived specific ventilation correlated with plethysmography-derived specific ventilation (ρ = 0.54, P = .002) and hyperpolarized 3He MR imaging-derived ventilation percentage (ρ = 0.67, P < .0001) as well as with forced expiratory volume in 1 second (FEV1) (ρ = 0.65, P = .0001), ratio of FEV1 to forced vital capacity (ρ = 0.75, P < .0001), ratio of residual volume to total lung capacity (ρ = -0.68, P < .0001), and airway resistance (ρ = -0.51, P = .004). 1H MR imaging-derived specific ventilation was significantly greater in the gravitational-dependent versus nondependent lung in healthy subjects (P = .02) but not in patients with asthma (P = .1). In patients with asthma, coregistered 1H MR imaging specific ventilation and hyperpolarized 3He MR imaging maps showed that specific ventilation was diminished in corresponding 3He MR imaging ventilation defects (0.05 ± 0.04) compared with well-ventilated regions (0.09 ± 0.05) (P < .0001). Conclusion 1H MR imaging-derived specific ventilation correlated with plethysmography-derived specific ventilation and ventilation defects seen by using hyperpolarized 3He MR imaging. © RSNA, 2018 Online supplemental material is available for this article.

Ultrashort echo time MRI biomarkers of asthma.

PURPOSE: To develop and assess ultrashort echo-time (UTE) magnetic resonance imaging (MRI) biomarkers of lung function in asthma patients. MATERIALS AND METHODS: Thirty participants including 13 healthy volunteers and 17 asthmatics provided written informed consent to UTE and pulmonary function tests in addition to hyperpolarized-noble-gas 3T MRI and computed tomography (CT) for asthmatics only. The difference in MRI signal-intensity (SI) across four lung volumes (full-expiration, functional-residual-capacity [FRC], FRC+1L, and full-inspiration) was determined on a voxel-by-voxel basis to generate dynamic proton-density (DPD) maps. MRI ventilation-defect-percent (VDP), UTE SI, and DPD values as well as CT radiodensity were determined for whole lung and individual lobes. RESULTS: Mean SI at full-expiration (P < 0.01), FRC (P < 0.05), and DPD (P < 0.01) were greater in healthy volunteers compared to asthmatics. In asthmatics, UTE SI at full-expiration and DPD were correlated with FEV1 /FVC (SI r = 0.73/P = 0.002; DPD r = 0.75/P = 0.003), RV/TLC (SI r = -0.57/P = 0.02), or RV (DPD r = -0.62/P = 0.02), CT radiodensity (SI r = 0.83/P = 0.006; DPD r = 0.71/P = 0.01), and lobar VDP (SI rs = -0.33/P = 0.02; DPD rs = -0.47/P = 0.01). CONCLUSION: In patients with asthma, UTE SI and dynamic proton-density were related to pulmonary function measurements, whole lung and lobar VDP, as well as CT radiodensity. Thus, UTE MRI biomarkers may reflect ventilation heterogeneity and/or gas-trapping in asthmatics using conventional equipment, making this approach potentially amenable for clinical use. LEVEL OF EVIDENCE: 2 J. Magn. Reson. Imaging 2017;45:1204-1215.

Pulmonary Imaging Biomarkers of Gas Trapping and Emphysema in COPD: (3)He MR Imaging and CT Parametric Response Maps.

PURPOSE: To directly compare magnetic resonance (MR) imaging and computed tomography (CT) parametric response map (PRM) measurements of gas trapping and emphysema in ex-smokers both with and without chronic obstructive pulmonary disease (COPD). MATERIALS AND METHODS: Participants provided written informed consent to a protocol that was approved by a local research ethics board and Health Canada and was compliant with the HIPAA (Institutional Review Board Reg. #00000940). The prospectively planned study was performed from March 2014 to December 2014 and included 58 ex-smokers (mean age, 73 years ± 9) with (n = 32; mean age, 74 years ± 7) and without (n = 26; mean age, 70 years ± 11) COPD. MR imaging (at functional residual capacity plus 1 L), CT (at full inspiration and expiration), and spirometry or plethysmography were performed during a 2-hour visit to generate ventilation defect percent (VDP), apparent diffusion coefficient (ADC), and PRM gas trapping and emphysema measurements. The relationships between pulmonary function and imaging measurements were determined with analysis of variance (ANOVA), Holm-Bonferroni corrected Pearson correlations, multivariate regression modeling, and the spatial overlap coefficient (SOC). RESULTS: VDP, ADC, and PRM gas trapping and emphysema (ANOVA, P < .001) measurements were significantly different in healthy ex-smokers than they were in ex-smokers with COPD. In all ex-smokers, VDP was correlated with PRM gas trapping (r = 0.58, P < .001) and with PRM emphysema (r = 0.68, P < .001). VDP was also significantly correlated with PRM in ex-smokers with COPD (gas trapping: r = 0.47 and P = .03; emphysema: r = 0.62 and P < .001) but not in healthy ex-smokers. In a multivariate model that predicted PRM gas trapping, the forced expiratory volume in 1 second normalized to the forced vital capacity (standardized coefficients [ßS] = -0.69, P = .001) and airway wall area percent (ßS = -0.22, P = .02) were significant predictors. PRM emphysema was predicted by the diffusing capacity for carbon monoxide (ßS = -0.29, P = .03) and VDP (ßS = 0.41, P = .001). Helium 3 ADC values were significantly elevated in PRM gas-trapping regions (P < .001). The spatial relationship for ventilation defects was significantly greater with PRM gas trapping than with PRM emphysema in patients with mild (for gas trapping, SOC = 36% ± 28; for emphysema, SOC = 1% ± 2; P = .001) and moderate (for gas trapping, SOC = 34% ± 28; for emphysema, SOC = 7% ± 15; P = .006) COPD. For severe COPD, the spatial relationship for ventilation defects with PRM emphysema (SOC = 64% ± 30) was significantly greater than that for PRM gas trapping (SOC = 36% ± 18; P = .01). CONCLUSION: In all ex-smokers, ADC values were significantly elevated in regions of PRM gas trapping, and VDP was quantitatively and spatially related to both PRM gas trapping and PRM emphysema. In patients with mild to moderate COPD, VDP was related to PRM gas trapping, whereas in patients with severe COPD, VDP correlated with both PRM gas trapping and PRM emphysema.

Is ventilation heterogeneity related to asthma control?

In asthma patients, magnetic resonance imaging (MRI) and the lung clearance index (LCI) have revealed persistent ventilation heterogeneity, although its relationship to asthma control is not well understood. Therefore, our goal was to explore the relationship of MRI ventilation defects and the LCI with asthma control and quality of life in patients with severe, poorly controlled asthma.18 patients with severe, poorly controlled asthma (mean±sd 46±12 years, six males/12 females) provided written informed consent to an ethics board approved protocol, and underwent spirometry, LCI and (3)He MRI during a single 2-h visit. Asthma control and quality of life were evaluated using the Asthma Control Questionnaire (ACQ) and Asthma Quality of Life Questionnaire (AQLQ). Ventilation heterogeneity was quantified using the LCI and (3)He MRI ventilation defect percent (VDP).All participants reported poorly controlled disease (mean±sd ACQ score=2.3±0.9) and highly heterogeneous ventilation (mean±sd VDP=12±11% and LCI=10.5±3.0). While VDP and LCI were strongly correlated (r=0.86, p<0.0001), in a multivariate model that included forced expiratory volume in 1 s, VDP and LCI, VDP was the only independent predictor of asthma control (R(2)=0.38, p=0.01). There was also a significantly worse VDP, but not LCI in asthma patients with an ACQ score >2 (p=0.04) and AQLQ score <5 (p=0.04), and a trend towards worse VDP (p=0.053), but not LCI in asthma patients reporting ≥1 exacerbation in the past 6 months.In patients with poorly controlled, severe asthma MRI ventilation, but not LCI was significantly worse in those with worse ACQ and AQLQ.

Oscillatory Positive Expiratory Pressure in Chronic Obstructive Pulmonary Disease.

Evidence-based guidance for the use of airway clearance techniques (ACT) in chronic obstructive pulmonary disease (COPD) is lacking in-part because well-established measurements of pulmonary function such as the forced expiratory volume in 1s (FEV1) are relatively insensitive to ACT. The objective of this crossover study was to evaluate daily use of an oscillatory positive expiratory pressure (oPEP) device for 21-28 days in COPD patients who were self-identified as sputum-producers or non-sputum-producers. COPD volunteers provided written informed consent to daily oPEP use in a randomized crossover fashion. Participants completed baseline, crossover and study-end pulmonary function tests, St. George's Respiratory Questionnaire (SGRQ), Patient Evaluation Questionnaire (PEQ), Six-Minute Walk Test and (3)He magnetic resonance imaging (MRI) for the measurement of ventilation abnormalities using the ventilation defect percent (VDP). Fourteen COPD patients, self-identified as sputum-producers and 13 COPD-non-sputum-producers completed the study. Post-oPEP, the PEQ-ease-bringing-up-sputum was improved for sputum-producers (p = 0.005) and non-sputum-producers (p = 0.04), the magnitude of which was greater for sputum-producers (p = 0.03). There were significant post-oPEP improvements for sputum-producers only for FVC (p = 0.01), 6MWD (p = 0.04), SGRQ total score (p = 0.01) as well as PEQ-patient-global-assessment (p = 0.02). Clinically relevant post-oPEP improvements for PEQ-ease-bringing-up-sputum/PEQ-patient-global-assessment/SGRQ/VDP were observed in 8/7/9/6 of 14 sputum-producers and 2/0/3/3 of 13 non-sputum-producers. The post-oPEP change in (3)He MRI VDP was related to the change in PEQ-ease-bringing-up-sputum (r = 0.65, p = 0.0004) and FEV1 (r = -0.50, p = 0.009). In COPD patients with chronic sputum production, PEQ and SGRQ scores, FVC and 6MWD improved post-oPEP. FEV1 and PEQ-ease-bringing-up-sputum improvements were related to improved ventilation providing mechanistic evidence to support oPEP use in COPD. Clinical Trials # NCT02282189 and NCT02282202.

Regional Heterogeneity of Chronic Obstructive Pulmonary Disease Phenotypes: Pulmonary (3)He Magnetic Resonance Imaging and Computed Tomography.

Pulmonary ventilation may be visualized and measured using hyperpolarized (3)He magnetic resonance imaging (MRI) while emphysema and its distribution can be quantified using thoracic computed tomography (CT). Our objective was to phenotype ex-smokers with COPD based on the apical-to-basal distribution of ventilation abnormalities and emphysema to better understand how these phenotypes change regionally as COPD progresses. We evaluated 100 COPD ex-smokers who provided written informed consent and underwent spirometry, CT and (3)He MRI. (3)He MRI ventilation imaging was used to quantify the ventilation defect percent (VDP) for whole-lung and individual lung lobes. Regional VDP was used to generate the apical-lung (AL)-to-basal-lung (BL) difference (ΔVDP); a positive ΔVDP indicated AL-predominant and negative ΔVDP indicated BL-predominant ventilation defects. Emphysema was quantified using the relative-area-of-the-lung ≤-950HU (RA950) of the CT density histogram for whole-lung and individual lung lobes. The AL-to-BL RA950 difference (ΔRA950) was generated with a positive ΔRA950 indicating AL-predominant emphysema and a negative ΔRA950 indicating BL-predominant emphysema. Seventy-two ex-smokers reported BL-predominant MRI ventilation defects and 71 reported AL-predominant CT emphysema. BL-predominant ventilation defects (AL/BL: GOLD I = 18%/82%, GOLD II = 24%/76%) and AL-predominant emphysema (AL/BL: GOLD I = 84%/16%, GOLD II = 72%/28%) were the major phenotypes in mild-moderate COPD. In severe COPD there was a more uniform distribution for ventilation defects (AL/BL: GOLD III = 40%/60%, GOLD IV = 43%/57%) and emphysema (AL/BL: GOLD III = 64%/36%, GOLD IV = 43%/57%). Basal-lung ventilation defects predominated in mild-moderate GOLD grades, and a more homogeneous distribution of ventilation defects was observed in more advanced grade COPD; these differences suggest that over time, regional ventilation abnormalities become more homogenously distributed during disease progression.

Ultra-short echo-time pulmonary MRI: evaluation and reproducibility in COPD subjects with and without bronchiectasis.

BACKGROUND: To evaluate ultra-short-echo-time (UTE) MRI pulmonary signal-intensity measurements and reproducibility in chronic obstructive pulmonary disease (COPD). METHODS: A two-dimensional sequence (echo-time = 0.05 ms; acquisition-time = 13 s) with interleaved half-pulse excitation and radial ramp-sampling was used with compressed-sensing to reconstruct UTE images from under-sampled data. Five healthy volunteers and 15 subjects with COPD provided written informed consent to imaging and pulmonary-function-tests. Healthy volunteers underwent MRI at four lung volumes: full-expiration, functional-residual-capacity (FRC), FRC+1L, and full-inhalation; COPD patients underwent computed-tomography (CT) and MRI at FRC+1L. Three-week reproducibility was evaluated and the relative area of the density histogram ≤ -950 HU (RA950 ) was compared with mean MRI signal-intensity. The 15th percentile of signal-intensity-histogram (SI15 ) was compared with the 15th percentile of the CT-density-histogram (HU15 ). RESULTS: In healthy subjects, signal-intensity correlated with the inverse of lung volume (r = 0.99; P = 0.007). Contrast-to-noise and signal-to-noise ratios were significantly improved for 32-channel UTE (P < 0.01). The coefficient of variation for 3-week repeated measurements was 4%. There were significant correlations for signal-intensity with RA950 (r = -0.71; P = 0.005), FEV1 /FVC (r = 0.59; P = 0.02), and for SI15 with HU15 (r = 0.62; P = 0.01). CONCLUSION: Pulmonary signal-intensity is reproducible and related to tissue density. In COPD subjects with and without bronchiectasis, signal-intensity was also related to pulmonary function and CT measurements.

MRI Accurately Visualizes RF Ablation Delivery Targeted to MRI-Defined Arrhythmia Substrates in the Left Ventricle.

OBJECTIVE: Investigate the capacity of MRI to evaluate efficacy of radiofrequency (RF) ablations delivered to MRI-defined arrhythmogenic substrates. METHODS: Baseline MRI was performed at 3T including 3D LGE in a swine model of chronic myocardial infarct (N=8). MRI-derived maps of scar and heterogeneous tissue channels (HTCs) were generated using ADAS 3D. Animals underwent electroanatomic mapping and ablation of the left ventricle in CARTO3, guided by MRI-derived scar maps. Post-ablation MRI (in vivo at 3T in 5/8 animals; ex vivo at 1.5T in 3/8) included 3D native T1-weighted IR-SPGR (TI=700-800ms) to visualize RF lesions. T1-derived RF lesions were compared against excised tissue. The locations of T1-derived RF lesions were compared against CARTO ablation tags, and segment-wise sensitivity and specificity of lesion detection were calculated within the AHA 17-segment model. RESULTS: RF lesions were clearly visualized in HTCs, scar, and myocardium. Ablation patterns delivered in CARTO matched T1-derived RF lesion patterns with high sensitivity (88.9%) and specificity (94.7%), and were closely matched in registered MR-EP data sets, with a displacement of 5.4 ±3.8mm (N=152 ablation tags). CONCLUSION: Integrating MRI into ablative procedures for RF lesion assessment is feasible. Patterns of RF lesions created using a standard 3D EAM system are accurately reflected by MRI visualization in healthy myocardium, scar, and HTCs comprising the MRI-defined arrhythmia substrate. SIGNIFICANCE: MRI visualization of RF lesions can provide near-immediate (<24h) assessment of ablation, potentially indicating whether critical MRI-defined ventricular tachycardia substrates have been adequately ablated.

Encoding Enhanced Complex CNN for Accurate and Highly Accelerated MRI.

Magnetic resonance imaging (MRI) using hyperpolarized noble gases provides a way to visualize the structure and function of human lung, but the long imaging time limits its broad research and clinical applications. Deep learning has demonstrated great potential for accelerating MRI by reconstructing images from undersampled data. However, most existing deep convolutional neural networks (CNN) directly apply square convolution to k-space data without considering the inherent properties of k-space sampling, limiting k-space learning efficiency and image reconstruction quality. In this work, we propose an encoding enhanced (EN2) complex CNN for highly undersampled pulmonary MRI reconstruction. EN2 complex CNN employs convolution along either the frequency or phase-encoding direction, resembling the mechanisms of k-space sampling, to maximize the utilization of the encoding correlation and integrity within a row or column of k-space. We also employ complex convolution to learn rich representations from the complex k-space data. In addition, we develop a feature-strengthened modularized unit to further boost the reconstruction performance. Experiments demonstrate that our approach can accurately reconstruct hyperpolarized 129Xe and 1H lung MRI from 6-fold undersampled k-space data and provide lung function measurements with minimal biases compared with fully sampled images. These results demonstrate the effectiveness of the proposed algorithmic components and indicate that the proposed approach could be used for accelerated pulmonary MRI in research and clinical lung disease patient care.

Carotid Vessel-Wall-Volume Ultrasound Measurement via a UNet++ Ensemble Algorithm Trained on Small Data Sets.

Vessel wall volume (VWV) is a 3-D ultrasound measurement for the assessment of therapy in patients with carotid atherosclerosis. Deep learning can be used to segment the media-adventitia boundary (MAB) and lumen-intima boundary (LIB) and to quantify VWV automatically; however, it typically requires large training data sets with expert manual segmentation, which are difficult to obtain. In this study, a UNet++ ensemble approach was developed for automated VWV measurement, trained on five small data sets (n = 30 participants) and tested on 100 participants with clinically diagnosed coronary artery disease enrolled in a multicenter CAIN trial. The Dice similarity coefficient (DSC), average symmetric surface distance (ASSD), Pearson correlation coefficient (r), Bland-Altman plots and coefficient of variation (CoV) were used to evaluate algorithm segmentation accuracy, agreement and reproducibility. The UNet++ ensemble yielded DSCs of 91.07%-91.56% and 87.53%-89.44% and ASSDs of 0.10-0.11 mm and 0.33-0.39 mm for the MAB and LIB, respectively; the algorithm VWV measurements were correlated (r = 0.763-0.795, p < 0.001) with manual segmentations, and the CoV for VWV was 8.89%. In addition, the UNet++ ensemble trained on 30 participants achieved a performance similar to that of U-Net and Voxel-FCN trained on 150 participants. These results suggest that our approach could provide accurate and reproducible carotid VWV measurements using relatively small training data sets, supporting deep learning applications for monitoring atherosclerosis progression in research and clinical trials.

High-expansion-ratio PLLA/PDLA/HNT composite foams with good thermally insulating property and enhanced compression performance via supercritical CO2.

It has been a great challenge to prepare high-expansion-ratio polylactide (PLA) foam with eminent thermal insulation and compression performance in packaging field. Herein, a naturally formed nanofiller halloysite nanotube (HNT) and stereocomplex (SC) crystallites were introduced into PLA with a supercritical CO2 foaming method to improve foaming behavior and physical properties. The compressive performance and thermal insulation properties of the obtained poly(L-lactic acid) (PLLA)/poly(D-lactic acid) (PDLA)/HNT composite foams were successfully investigated. At a HNT content of 1 wt%, the PLLA/PDLA/HNT blend foam with an expansion ratio of 36.7 folds showed a thermal conductivity as low as 30.60 mW/(m·K). Meanwhile, the compressive modulus of PLLA/PDLA/HNT foam was 115% higher than that of PLLA/PDLA foam without HNT. Moreover, the crystallinity of PLLA/PDLA/HNT foam was dramatically improved after annealing, thus the results showed that compressive modulus of the annealed foam increased by as high as 72%, while it still maintained good heat insulation with the thermal conductivity of 32.63 mW/(m·K). This work provides a green method for the preparation of biodegradable PLA foams with admirable heat resistance and mechanical performance.

Estimating Uncertainty in Neural Networks for Cardiac MRI Segmentation: A Benchmark Study.

OBJECTIVE: Convolutional neural networks (CNNs) have demonstrated promise in automated cardiac magnetic resonance image segmentation. However, when using CNNs in a large real-world dataset, it is important to quantify segmentation uncertainty and identify segmentations which could be problematic. In this work, we performed a systematic study of Bayesian and non-Bayesian methods for estimating uncertainty in segmentation neural networks. METHODS: We evaluated Bayes by Backprop, Monte Carlo Dropout, Deep Ensembles, and Stochastic Segmentation Networks in terms of segmentation accuracy, probability calibration, uncertainty on out-of-distribution images, and segmentation quality control. RESULTS: We observed that Deep Ensembles outperformed the other methods except for images with heavy noise and blurring distortions. We showed that Bayes by Backprop is more robust to noise distortions while Stochastic Segmentation Networks are more resistant to blurring distortions. For segmentation quality control, we showed that segmentation uncertainty is correlated with segmentation accuracy for all the methods. With the incorporation of uncertainty estimates, we were able to reduce the percentage of poor segmentation to 5% by flagging 31-48% of the most uncertain segmentations for manual review, substantially lower than random review without using neural network uncertainty (reviewing 75-78% of all images). CONCLUSION: This work provides a comprehensive evaluation of uncertainty estimation methods and showed that Deep Ensembles outperformed other methods in most cases. SIGNIFICANCE: Neural network uncertainty measures can help identify potentially inaccurate segmentations and alert users for manual review.