Age, sex, and lung volume dependence of dissolved xenon-129 MRI gas exchange metrics.

PURPOSE: To characterize the dependence of Xe-MRI gas transfer metrics upon age, sex, and lung volume in a group of healthy volunteers. METHODS: Sixty-five subjects with no history of chronic lung disease were assessed with 129Xe-MRI using a four-echo 3D radial spectroscopic imaging sequence and a dose of xenon titrated according to subject height that was inhaled from a lung volume of functional residual capacity (FRC). Imaging was repeated in 34 subjects at total lung capacity (TLC). Regional maps of the fractions of dissolved xenon in red blood cells (RBC), membrane (M), and airspace (Gas) were acquired at an isotropic resolution of 2 cm, from which global averages of the ratios RBC:M, RBC:Gas, and M:Gas were computed. RESULTS: Data from 26 males and 36 females with a median age of 43 y (range: 20-69 y) were of sufficient quality to analyze. Age (p = 0.0006) and sex (p < 0.0001) were significant predictors for RBC:M, and a linear regression showed higher values and steeper decline in males: RBC:M(Males) = -0.00362 × Age + 0.60 (p = 0.01, R2 = 0.25); RBC:M(Females) = -0.00170 × Age + 0.44 (p = 0.02, R2 = 0.15). Similarly, age and sex were significant predictors for RBC:Gas but not for M:Gas. RBC:M, M:Gas and RBC:Gas were significantly lower at TLC than at FRC (plus inhaled volume), with an average 9%, 30% and 35% decrease, respectively. CONCLUSION: Expected age and sex dependence of pulmonary function concurs with 129Xe RBC:M imaging results, demonstrating that these variables must be considered when reporting Xe-MRI metrics. Xenon doses and breathing maneuvers should be controlled due to the strong dependence of Xe-MRI metrics upon lung volume.

Image Phenotyping of Preterm-Born Children Using Hyperpolarized 129Xe Lung Magnetic Resonance Imaging and Multiple-Breath Washout.

Rationale: Preterm birth is associated with low lung function in childhood, but little is known about the lung microstructure in childhood. Objectives: We assessed the differential associations between the historical diagnosis of bronchopulmonary dysplasia (BPD) and current lung function phenotypes on lung ventilation and microstructure in preterm-born children using hyperpolarized 129Xe ventilation and diffusion-weighted magnetic resonance imaging (MRI) and multiple-breath washout (MBW). Methods: Data were available from 63 children (aged 9-13 yr), including 44 born preterm (⩽34 weeks' gestation) and 19 term-born control subjects (⩾37 weeks' gestation). Preterm-born children were classified, using spirometry, as prematurity-associated obstructive lung disease (POLD; FEV1 < lower limit of normal [LLN] and FEV1/FVC < LLN), prematurity-associated preserved ratio of impaired spirometry (FEV1 < LLN and FEV1/FVC ⩾ LLN), preterm-(FEV1 ⩾ LLN) and term-born control subjects, and those with and without BPD. Ventilation heterogeneity metrics were derived from 129Xe ventilation MRI and SF6 MBW. Alveolar microstructural dimensions were derived from 129Xe diffusion-weighted MRI. Measurements and Main Results: 129Xe ventilation defect percentage and ventilation heterogeneity index were significantly increased in preterm-born children with POLD. In contrast, mean 129Xe apparent diffusion coefficient, 129Xe apparent diffusion coefficient interquartile range, and 129Xe mean alveolar dimension interquartile range were significantly increased in preterm-born children with BPD, suggesting changes of alveolar dimensions. MBW metrics were all significantly increased in the POLD group compared with preterm- and term-born control subjects. Linear regression confirmed the differential effects of obstructive disease on ventilation defects and BPD on lung microstructure. Conclusion: We show that ventilation abnormalities are associated with POLD, and BPD in infancy is associated with abnormal lung microstructure.

A Dual-Channel Deep Learning Approach for Lung Cavity Estimation From Hyperpolarized Gas and Proton MRI.

BACKGROUND: Hyperpolarized gas MRI can quantify regional lung ventilation via biomarkers, including the ventilation defect percentage (VDP). VDP is computed from segmentations derived from spatially co-registered functional hyperpolarized gas and structural proton (1 H)-MRI. Although acquired at similar lung inflation levels, they are frequently misaligned, requiring a lung cavity estimation (LCE). Recently, single-channel, mono-modal deep learning (DL)-based methods have shown promise for pulmonary image segmentation problems. Multichannel, multimodal approaches may outperform single-channel alternatives. PURPOSE: We hypothesized that a DL-based dual-channel approach, leveraging both 1 H-MRI and Xenon-129-MRI (129 Xe-MRI), can generate LCEs more accurately than single-channel alternatives. STUDY TYPE: Retrospective. POPULATION: A total of 480 corresponding 1 H-MRI and 129 Xe-MRI scans from 26 healthy participants (median age [range]: 11 [8-71]; 50% females) and 289 patients with pulmonary pathologies (median age [range]: 47 [6-83]; 51% females) were split into training (422 scans [88%]; 257 participants [82%]) and testing (58 scans [12%]; 58 participants [18%]) sets. FIELD STRENGTH/SEQUENCE: 1.5-T, three-dimensional (3D) spoiled gradient-recalled 1 H-MRI and 3D steady-state free-precession 129 Xe-MRI. ASSESSMENT: We developed a multimodal DL approach, integrating 129 Xe-MRI and 1 H-MRI, in a dual-channel convolutional neural network. We compared this approach to single-channel alternatives using manually edited LCEs as a benchmark. We further assessed a fully automatic DL-based framework to calculate VDPs and compared it to manually generated VDPs. STATISTICAL TESTS: Friedman tests with post hoc Bonferroni correction for multiple comparisons compared single-channel and dual-channel DL approaches using Dice similarity coefficient (DSC), average boundary Hausdorff distance (average HD), and relative error (XOR) metrics. Bland-Altman analysis and paired t-tests compared manual and DL-generated VDPs. A P value < 0.05 was considered statistically significant. RESULTS: The dual-channel approach significantly outperformed single-channel approaches, achieving a median (range) DSC, average HD, and XOR of 0.967 (0.867-0.978), 1.68 mm (37.0-0.778), and 0.066 (0.246-0.045), respectively. DL-generated VDPs were statistically indistinguishable from manually generated VDPs (P = 0.710). DATA CONCLUSION: Our dual-channel approach generated LCEs, which could be integrated with ventilated lung segmentations to produce biomarkers such as the VDP without manual intervention. EVIDENCE LEVEL: 4. TECHNICAL EFFICACY: Stage 1.

Multicenter Standardization of Phase-Resolved Functional Lung MRI in Patients With Suspected Chronic Thromboembolic Pulmonary Hypertension.

BACKGROUND: Detection of pulmonary perfusion defects is the recommended approach for diagnosing chronic thromboembolic pulmonary hypertension (CTEPH). This is currently achieved in a clinical setting using scintigraphy. Phase-resolved functional lung (PREFUL) magnetic resonance imaging (MRI) is an alternative technique for evaluating regional ventilation and perfusion without the use of ionizing radiation or contrast media. PURPOSE: To assess the feasibility and image quality of PREFUL-MRI in a multicenter setting in suspected CTEPH. STUDY TYPE: This is a prospective cohort sub-study. POPULATION: Forty-five patients (64 ± 16 years old) with suspected CTEPH from nine study centers. FIELD STRENGTH/SEQUENCE: 1.5 T and 3 T/2D spoiled gradient echo/bSSFP/T2 HASTE/3D MR angiography (TWIST). ASSESSMENT: Lung signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were compared between study centers with different MRI machines. The contrast between normally and poorly perfused lung areas was examined on PREFUL images. The perfusion defect percentage calculated using PREFUL-MRI (QDPPREFUL ) was compared to QDP from the established dynamic contrast-enhanced MRI technique (QDPDCE ). Furthermore, QDPPREFUL was compared between a patient subgroup with confirmed CTEPH or chronic thromboembolic disease (CTED) to other clinical subgroups. STATISTICAL TESTS: t-Test, one-way analysis of variance (ANOVA), Pearson's correlation. Significance level was 5%. RESULTS: Significant differences in lung SNR and CNR were present between study centers. However, PREFUL perfusion images showed a significant contrast between normally and poorly perfused lung areas (mean delta of normalized perfusion -4.2% SD 3.3) with no differences between study sites (ANOVA: P = 0.065). QDPPREFUL was significantly correlated with QDPDCE (r = 0.66), and was significantly higher in 18 patients with confirmed CTEPH or CTED (57.9 ± 12.2%) compared to subgroups with other causes of PH or with excluded PH (in total 27 patients with mean ± SD QDPPREFUL = 33.9 ± 17.2%). DATA CONCLUSION: PREFUL-MRI could be considered as a non-invasive method for imaging regional lung perfusion in multicenter studies. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 1.

Implementable Deep Learning for Multi-sequence Proton MRI Lung Segmentation: A Multi-center, Multi-vendor, and Multi-disease Study.

BACKGROUND: Recently, deep learning via convolutional neural networks (CNNs) has largely superseded conventional methods for proton (1 H)-MRI lung segmentation. However, previous deep learning studies have utilized single-center data and limited acquisition parameters. PURPOSE: Develop a generalizable CNN for lung segmentation in 1 H-MRI, robust to pathology, acquisition protocol, vendor, and center. STUDY TYPE: Retrospective. POPULATION: A total of 809 1 H-MRI scans from 258 participants with various pulmonary pathologies (median age (range): 57 (6-85); 42% females) and 31 healthy participants (median age (range): 34 (23-76); 34% females) that were split into training (593 scans (74%); 157 participants (55%)), testing (50 scans (6%); 50 participants (17%)) and external validation (164 scans (20%); 82 participants (28%)) sets. FIELD STRENGTH/SEQUENCE: 1.5-T and 3-T/3D spoiled-gradient recalled and ultrashort echo-time 1 H-MRI. ASSESSMENT: 2D and 3D CNNs, trained on single-center, multi-sequence data, and the conventional spatial fuzzy c-means (SFCM) method were compared to manually delineated expert segmentations. Each method was validated on external data originating from several centers. Dice similarity coefficient (DSC), average boundary Hausdorff distance (Average HD), and relative error (XOR) metrics to assess segmentation performance. STATISTICAL TESTS: Kruskal-Wallis tests assessed significances of differences between acquisitions in the testing set. Friedman tests with post hoc multiple comparisons assessed differences between the 2D CNN, 3D CNN, and SFCM. Bland-Altman analyses assessed agreement with manually derived lung volumes. A P value of <0.05 was considered statistically significant. RESULTS: The 3D CNN significantly outperformed its 2D analog and SFCM, yielding a median (range) DSC of 0.961 (0.880-0.987), Average HD of 1.63 mm (0.65-5.45) and XOR of 0.079 (0.025-0.240) on the testing set and a DSC of 0.973 (0.866-0.987), Average HD of 1.11 mm (0.47-8.13) and XOR of 0.054 (0.026-0.255) on external validation data. DATA CONCLUSION: The 3D CNN generated accurate 1 H-MRI lung segmentations on a heterogenous dataset, demonstrating robustness to disease pathology, sequence, vendor, and center. EVIDENCE LEVEL: 4. TECHNICAL EFFICACY: Stage 1.

Integrated Cardiopulmonary MRI Assessment of Pulmonary Hypertension.

Pulmonary hypertension (PH) is a heterogeneous condition that can affect the lung parenchyma, pulmonary vasculature, and cardiac chambers. Accurate diagnosis often requires multiple complex assessments of the cardiac and pulmonary systems. MRI is able to comprehensively assess cardiac structure and function, as well as lung parenchymal, pulmonary vascular, and functional lung changes. Therefore, MRI has the potential to provide an integrated functional and structural assessment of the cardiopulmonary system in a single exam. Cardiac MRI is used in the assessment of PH in most large PH centers, whereas lung MRI is an emerging technique in patients with PH. This article reviews the current literature on cardiopulmonary MRI in PH, including cine MRI, black-blood imaging, late gadolinium enhancement, T1 mapping, myocardial strain analysis, contrast-enhanced perfusion imaging and contrast-enhanced MR angiography, and hyperpolarized gas functional lung imaging. This article also highlights recent developments in this field and areas of interest for future research including cardiac MRI-based diagnostic models, machine learning in cardiac MRI, oxygen-enhanced 1 H imaging, contrast-free 1 H perfusion and ventilation imaging, contrast-free angiography and UTE imaging. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: Stage 3.

Quantification of pulmonary perfusion in idiopathic pulmonary fibrosis with first pass dynamic contrast-enhanced perfusion MRI.

INTRODUCTION: Idiopathic pulmonary fibrosis (IPF) is a fatal disease of lung scarring. Many patients later develop raised pulmonary vascular pressures, sometimes disproportionate to the interstitial disease. Previous therapeutic approaches that have targeted pulmonary vascular changes have not demonstrated clinical efficacy, and quantitative assessment of regional pulmonary vascular involvement using perfusion imaging may provide a biomarker for further therapeutic insights. METHODS: We studied 23 participants with IPF, using dynamic contrast-enhanced MRI (DCE-MRI) and pulmonary function tests, including forced vital capacity (FVC), transfer factor (TLCO) and coefficient (KCO) of the lungs for carbon monoxide. DCE-MRI parametric maps were generated including the full width at half maximum (FWHM) of the bolus transit time through the lungs. Key metrics used were mean (FWHMmean) and heterogeneity (FWHMIQR). Nineteen participants returned at 6 months for repeat assessment. RESULTS: Spearman correlation coefficients were identified between TLCO and FWHMIQR (r=-0.46; p=0.026), KCO and FWHMmean (r=-0.42; p=0.047) and KCO and FWHMIQR (r=-0.51; p=0.013) at baseline. No statistically significant correlations were seen between FVC and DCE-MRI metrics. Follow-up at 6 months demonstrated statistically significant decline in FVC (p=0.040) and KCO (p=0.014), with an increase in FWHMmean (p=0.040), but no significant changes in TLCO (p=0.090) nor FWHMIQR (p=0.821). CONCLUSIONS: DCE-MRI first pass perfusion demonstrates correlations with existing physiological gas exchange metrics, suggesting that capillary perfusion deficit (as well as impaired interstitial diffusion) may contribute to gas exchange limitation in IPF. FWHMmean showed a significant increase over a 6-month period and has potential as a quantitative biomarker of pulmonary vascular disease progression in IPF.

MR properties of 19 F C3 F8 gas in the lungs of healthy volunteers: T 2 ∗ and apparent diffusion coefficient at 1.5T and T 2 ∗ at 3T.

PURPOSE: To measure the transverse relaxation time ( T 2 ∗ ) and apparent diffusion coefficient (ADC) of 19 F-C3 F8 gas in vivo in human lungs at 1.5T and 3T, and to determine the representative distribution of values of these parameters in a cohort of healthy volunteers. METHODS: Mapping of ADC at lung inflation levels of functional residual capacity (FRC) and total lung capacity (TLC) was performed with inhaled 19 F-C3 F8 (eight subjects) and 129 Xe (six subjects) at 1.5T. T 2 ∗ mapping with 19 F-C3 F8 was performed at 1.5T (at FRC and TLC) for 8 subjects and at 3T (at TLC for seven subjects). RESULTS: At both FRC and TLC, the 19 F-C3 F8 ADC was smaller than the free diffusion coefficient demonstrating airway microstructural diffusion restriction. From FRC to TLC, the mean ADC significantly increased from 1.56 mm2 /s to 1.83 mm2 /s (P = .0017) for 19 F-C3 F8, and from 2.49 mm2 /s to 3.38 mm2 /s (P = .0015) for 129 Xe. The posterior-to-anterior gradient in ADC for FRC versus TLC in the superior half of the lungs was measured as 0.0308 mm2 /s per cm versus 0.0168 mm2 /s per cm for 19 F-C3 F8 and 0.0871 mm2 /s per cm versus 0.0326 mm2 /s per cm for 129 Xe. A consistent distribution of 19 F-C3 F8 T 2 ∗ values was observed in the lungs, with low values observed near the diaphragm and large pulmonary vessels. The mean T 2 ∗ across volunteers was 4.48 ms at FRC and 5.33 ms at TLC for 1.5T, and 3.78 ms at TLC for 3T. CONCLUSION: In this feasibility study, values of physiologically relevant parameters of lung microstructure measurable by MRI ( T 2 ∗ , and ADC) were established for C3 F8 in vivo lung imaging in healthy volunteers.

Dissolved 129 Xe lung MRI with four-echo 3D radial spectroscopic imaging: Quantification of regional gas transfer in idiopathic pulmonary fibrosis.

PURPOSE: Imaging of the different resonances of dissolved hyperpolarized xenon-129 (129 Xe) in the lung is performed using a four-echo flyback 3D radial spectroscopic imaging technique and is evaluated in healthy volunteers (HV) and subjects with idiopathic pulmonary fibrosis (IPF). THEORY AND METHODS: 10 HV and 25 subjects with IPF underwent dissolved 129 Xe MRI at 1.5T. IPF subjects underwent same day pulmonary function tests to measure forced vital capacity and the diffusion capacity of the lung for carbon monoxide (DLCO ). A four-point echo time technique with k-space chemical-shift modeling of gas, dissolved 129 Xe in lung tissue/plasma (TP) and red blood cells (RBC) combined with a 3D radial trajectory was implemented within a 14-s breath-hold. RESULTS: Results show an excellent chemical shift separation of the dissolved 129 Xe compartments and gas contamination removal, confirmed by a strong agreement between average imaging and global spectroscopy RBC/TP ratio measurements. Subjects with IPF exhibited reduced imaging gas transfer when compared to HV. A significant increase of the amplitude of RBC signal cardiogenic oscillation was also observed. In IPF subjects, DLCO % predicted was significantly correlated with RBC/TP and RBC/GAS ratios and the correlations were stronger in the inferior and periphery sections of the lungs. CONCLUSION: Lung MRI of dissolved 129 Xe was performed with a four-echo spectroscopic imaging method. Subjects with IPF demonstrated reduced xenon imaging gas transfer and increased cardiogenic modulation of dissolved xenon signal in the RBCs when compared to HV.

The assessment of short and long term changes in lung function in CF using 129Xe MRI.

INTRODUCTION: 129Xe ventilation MRI is sensitive to detect early CF lung disease and response to treatment. 129Xe-MRI could play a significant role in clinical trials and patient management. Here we present data on the repeatability of imaging measurements and their sensitivity to longitudinal change. METHODS: 29 children and adults with CF and a range of disease severity were assessed twice, a median [IQR] of 16.0 [14.4,19.5] months apart. Patients performed 129Xe-MRI, lung clearance index (LCI), body plethysmography and spirometry at both visits. Eleven patients repeated 129Xe-MRI in the same session to assess the within-visit repeatability. The ventilation defect percentage (VDP) was the primary metric calculated from 129Xe-MRI. RESULTS: At baseline, mean (sd) age=23.0 (11.1) years and FEV1 z-score=-2.2 (2.0). Median [IQR] VDP=9.5 [3.4,31.6]%, LCI=9.0 [7.7,13.7]. Within-visit and inter-visit repeatability of VDP was high. At 16 months there was no single trend of 129Xe-MRI disease progression. Visible 129Xe-MRI ventilation changes were common, which reflected changes in VDP. Based on the within-visit repeatability, a significant short-term change in VDP is >±1.6%. For longer-term follow up, changes in VDP of up to ±7.7% can be expected, or ±4.1% for patients with normal FEV1. No patient had a significant change in FEV1, however 59% had change in VDP >±1.6%. In patients with normal FEV1, there were significant changes in ventilation and in VDP. CONCLUSIONS: 129Xe-MRI is a highly effective method for assessing longitudinal lung disease in patients with CF. VDP has great potential as a sensitive clinical outcome measure of lung function and endpoint for clinical trials.

Single breath-held acquisition of coregistered 3D 129 Xe lung ventilation and anatomical proton images of the human lung with compressed sensing.

PURPOSE: To develop and assess a method for acquiring coregistered proton anatomical and hyperpolarized 129 Xe ventilation MR images of the lungs with compressed sensing (CS) in a single breath hold. METHODS: Retrospective CS simulations were performed on fully sampled ventilation images acquired from one healthy smoker to optimize reconstruction parameters. Prospective same-breath anatomical and ventilation images were also acquired in five ex-smokers with an acceleration factor of 3 for hyperpolarized 129 Xe images, and were compared to fully sampled images acquired during the same session. The following metrics were used to assess data fidelity: mean absolute error (MAE), root mean square error, and linear regression of the signal intensity between fully sampled and undersampled images. The effect of CS reconstruction on two quantitative imaging metrics routinely reported [percentage ventilated volume (%VV) and heterogeneity score] was also investigated. RESULTS: Retrospective simulations showed good agreement between fully sampled and CS-reconstructed (acceleration factor of 3) images with MAE (root mean square error) of 3.9% (4.5%). The prospective same-breath images showed a good match in ventilation distribution with an average R2 of 0.76 from signal intensity linear regression and a negligible systematic bias of +0.1% in %VV calculation. A bias of -1.8% in the heterogeneity score was obtained. CONCLUSION: With CS, high-quality 3D images of hyperpolarized 129 Xe ventilation (resolution 4.2 × 4.2 × 7.5 mm3 ) can be acquired with coregistered 1 H anatomical MRI in a 15-s breath hold. The accelerated acquisition time dispenses with the need for registration between separate breath-hold 129 Xe and 1 H MRI, enabling more accurate %VV calculation.

Patterns of regional lung physiology in cystic fibrosis using ventilation magnetic resonance imaging and multiple-breath washout.

Hyperpolarised helium-3 (3He) ventilation magnetic resonance imaging (MRI) and multiple-breath washout (MBW) are sensitive methods for detecting lung disease in cystic fibrosis (CF). We aimed to explore their relationship across a broad range of CF disease severity and patient age, as well as assess the effect of inhaled lung volume on ventilation distribution.32 children and adults with CF underwent MBW and 3He-MRI at a lung volume of end-inspiratory tidal volume (EIV T). In addition, 28 patients performed 3He-MRI at total lung capacity. 3He-MRI scans were quantitatively analysed for ventilation defect percentage (VDP), ventilation heterogeneity index (VHI) and the number and size of individual contiguous ventilation defects. From MBW, the lung clearance index, convection-dependent ventilation heterogeneity (Scond) and convection-diffusion-dependent ventilation heterogeneity (Sacin) were calculated.VDP and VHI at EIV T strongly correlated with lung clearance index (r=0.89 and r=0.88, respectively), Sacin (r=0.84 and r=0.82, respectively) and forced expiratory volume in 1 s (FEV1) (r=-0.79 and r=-0.78, respectively). Two distinct 3He-MRI patterns were highlighted: patients with abnormal FEV1 had significantly (p<0.001) larger, but fewer, contiguous defects than those with normal FEV1, who tended to have numerous small volume defects. These two MRI patterns were delineated by a VDP of ∼10%. At total lung capacity, when compared to EIV T, VDP and VHI reduced in all subjects (p<0.001), demonstrating improved ventilation distribution and regions of volume-reversible and nonreversible ventilation abnormalities.

Spatial fuzzy c-means thresholding for semiautomated calculation of percentage lung ventilated volume from hyperpolarized gas and 1 H MRI.

PURPOSE: To develop an image-processing pipeline for semiautomated (SA) and reproducible analysis of hyperpolarized gas lung ventilation and proton anatomical magnetic resonance imaging (MRI) scan pairs. To compare results from the software for total lung volume (TLV), ventilated volume (VV), and percentage lung ventilated volume (%VV) calculation to the current manual "basic" method and a K-means segmentation method. MATERIALS AND METHODS: Six patients were imaged with hyperpolarized 3 He and same-breath lung 1 H MRI at 1.5T and six other patients were scanned with hyperpolarized 129 Xe and separate-breath 1 H MRI. One expert observer and two users with experience in lung image segmentation carried out the image analysis. Spearman (R), Intraclass (ICC) correlations, Bland-Altman limits of agreement (LOA), and Dice Similarity Coefficients (DSC) between output lung volumes were calculated. RESULTS: When comparing values of %VV, agreement between observers improved using the SA method (mean; R = 0.984, ICC = 0.980, LOA = 7.5%) when compared to the basic method (mean; R = 0.863, ICC = 0.873, LOA = 14.2%) nonsignificantly (pR = 0.25, pICC = 0.25, and pLOA = 0.50 respectively). DSC of VV and TLV masks significantly improved (P < 0.01) using the SA method (mean; DSCVV = 0.973, DSCTLV = 0.980) when compared to the basic method (mean; DSCVV = 0.947, DSCTLV = 0.957). K-means systematically overestimated %VV when compared to both basic (mean overestimation = 5.0%) and SA methods (mean overestimation = 9.7%), and had poor agreement with the other methods (mean ICC; K-means vs. basic = 0.685, K-means vs. SA = 0.740). CONCLUSION: A semiautomated image processing software was developed that improves interobserver agreement and correlation of lung ventilation volume percentage when compared to the currently used basic method and provides more consistent segmentations than the K-means method. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:640-646.

Comparison of 3 He and 129 Xe MRI for evaluation of lung microstructure and ventilation at 1.5T.

BACKGROUND: To support translational lung MRI research with hyperpolarized 129 Xe gas, comprehensive evaluation of derived quantitative lung function measures against established measures from 3 He MRI is required. Few comparative studies have been performed to date, only at 3T, and multisession repeatability of 129 Xe functional metrics have not been reported. PURPOSE/HYPOTHESIS: To compare hyperpolarized 129 Xe and 3 He MRI-derived quantitative metrics of lung ventilation and microstructure, and their repeatability, at 1.5T. STUDY TYPE: Retrospective. POPULATION: Fourteen healthy nonsmokers (HN), five exsmokers (ES), five patients with chronic obstructive pulmonary disease (COPD), and 16 patients with nonsmall-cell lung cancer (NSCLC). FIELD STRENGTH/SEQUENCE: 1.5T. NSCLC, COPD patients and selected HN subjects underwent 3D balanced steady-state free-precession lung ventilation MRI using both 3 He and 129 Xe. Selected HN, all ES, and COPD patients underwent 2D multislice spoiled gradient-echo diffusion-weighted lung MRI using both hyperpolarized gas nuclei. ASSESSMENT: Ventilated volume percentages (VV%) and mean apparent diffusion coefficients (ADC) were derived from imaging. COPD patients performed the whole MR protocol in four separate scan sessions to assess repeatability. Same-day pulmonary function tests were performed. STATISTICAL TESTS: Intermetric correlations: Spearman's coefficient. Intergroup/internuclei differences: analysis of variance / Wilcoxon's signed rank. Repeatability: coefficient of variation (CV), intraclass correlation (ICC) coefficient. RESULTS: A significant positive correlation between 3 He and 129 Xe VV% was observed (r = 0.860, P < 0.001). VV% was larger for 3 He than 129 Xe (P = 0.001); average bias, 8.79%. A strong correlation between mean 3 He and 129 Xe ADC was obtained (r = 0.922, P < 0.001). MR parameters exhibited good correlations with pulmonary function tests. In COPD patients, mean CV of 3 He and 129 Xe VV% was 4.08% and 13.01%, respectively, with ICC coefficients of 0.541 (P = 0.061) and 0.458 (P = 0.095). Mean 3 He and 129 Xe ADC values were highly repeatable (mean CV: 2.98%, 2.77%, respectively; ICC: 0.995, P < 0.001; 0.936, P < 0.001). DATA CONCLUSION: 129 Xe lung MRI provides near-equivalent information to 3 He for quantitative lung ventilation and microstructural MRI at 1.5T. LEVEL OF EVIDENCE: 3 Technical Efficacy Stage 2 J. Magn. Reson. Imaging 2018.

Lung perfusion: MRI vs. SPECT for screening in suspected chronic thromboembolic pulmonary hypertension.

PURPOSE: To assess the diagnostic accuracy of magnetic resonance imaging (MRI) perfusion against perfusion single photon emission tomography (SPECT) screening for chronic thromboembolic pulmonary hypertension (CTEPH). Ventilation/perfusion (V/Q) scintigraphy is recommended to screen for suspected CTEPH. It has previously been shown that 3D dynamic contrast-enhanced (DCE) lung perfusion MRI has a similar sensitivity for diagnosing CTEPH in comparison to planar perfusion scintigraphy; however, planar scintigraphy has now been largely replaced by SPECT, due to higher spatial resolution and sensitivity. MATERIALS AND METHODS: Consecutive patients with suspected CTEPH or unexplained pulmonary hypertension attending a referral center, who underwent lung DCE perfusion MRI at 1.5T, perfusion SPECT, and computed tomography pulmonary angiography (CTPA) within 14 days of right heart catheterization, from April 2013 to April 2014, were included. DCE-MR, SPECT, and CTPA were independently analyzed by two blinded radiologists. Disagreements were corrected by consensus. The gold standard reference for the diagnosis of chronic thromboemboli was based on a review of multimodality imaging and clinical findings. RESULTS: In all, 74 patients with suspected CTEPH underwent all three modalities. Forty-six were diagnosed with CTEPH (36) or chronic thromboembolic disease (CTED) (10). 3D DCE perfusion MRI correctly identified all patients (sensitivity of 100%), compared with a 97% sensitivity for SPECT. CONCLUSION: DCE lung perfusion MRI has increased sensitivity when compared with perfusion scintigraphy in screening for CTEPH. As MRI does not use ionizing radiation, it should be considered as a first-line imaging modality in suspected CTEPH. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2017;46:1693-1697.

A hybrid model- and deep learning-based framework for functional lung image synthesis from multi-inflation CT and hyperpolarized gas MRI.

BACKGROUND: Hyperpolarized gas MRI is a functional lung imaging modality capable of visualizing regional lung ventilation with exceptional detail within a single breath. However, this modality requires specialized equipment and exogenous contrast, which limits widespread clinical adoption. CT ventilation imaging employs various metrics to model regional ventilation from non-contrast CT scans acquired at multiple inflation levels and has demonstrated moderate spatial correlation with hyperpolarized gas MRI. Recently, deep learning (DL)-based methods, utilizing convolutional neural networks (CNNs), have been leveraged for image synthesis applications. Hybrid approaches integrating computational modeling and data-driven methods have been utilized in cases where datasets are limited with the added benefit of maintaining physiological plausibility. PURPOSE: To develop and evaluate a multi-channel DL-based method that combines modeling and data-driven approaches to synthesize hyperpolarized gas MRI lung ventilation scans from multi-inflation, non-contrast CT and quantitatively compare these synthetic ventilation scans to conventional CT ventilation modeling. METHODS: In this study, we propose a hybrid DL configuration that integrates model- and data-driven methods to synthesize hyperpolarized gas MRI lung ventilation scans from a combination of non-contrast, multi-inflation CT and CT ventilation modeling. We used a diverse dataset comprising paired inspiratory and expiratory CT and helium-3 hyperpolarized gas MRI for 47 participants with a range of pulmonary pathologies. We performed six-fold cross-validation on the dataset and evaluated the spatial correlation between the synthetic ventilation and real hyperpolarized gas MRI scans; the proposed hybrid framework was compared to conventional CT ventilation modeling and other non-hybrid DL configurations. Synthetic ventilation scans were evaluated using voxel-wise evaluation metrics such as Spearman's correlation and mean square error (MSE), in addition to clinical biomarkers of lung function such as the ventilated lung percentage (VLP). Furthermore, regional localization of ventilated and defect lung regions was assessed via the Dice similarity coefficient (DSC). RESULTS: We showed that the proposed hybrid framework is capable of accurately replicating ventilation defects seen in the real hyperpolarized gas MRI scans, achieving a voxel-wise Spearman's correlation of 0.57 ± 0.17 and an MSE of 0.017 ± 0.01. The hybrid framework significantly outperformed CT ventilation modeling alone and all other DL configurations using Spearman's correlation. The proposed framework was capable of generating clinically relevant metrics such as the VLP without manual intervention, resulting in a Bland-Altman bias of 3.04%, significantly outperforming CT ventilation modeling. Relative to CT ventilation modeling, the hybrid framework yielded significantly more accurate delineations of ventilated and defect lung regions, achieving a DSC of 0.95 and 0.48 for ventilated and defect regions, respectively. CONCLUSION: The ability to generate realistic synthetic ventilation scans from CT has implications for several clinical applications, including functional lung avoidance radiotherapy and treatment response mapping. CT is an integral part of almost every clinical lung imaging workflow and hence is readily available for most patients; therefore, synthetic ventilation from non-contrast CT can provide patients with wider access to ventilation imaging worldwide.

PhysVENeT: a physiologically-informed deep learning-based framework for the synthesis of 3D hyperpolarized gas MRI ventilation.

Functional lung imaging modalities such as hyperpolarized gas MRI ventilation enable visualization and quantification of regional lung ventilation; however, these techniques require specialized equipment and exogenous contrast, limiting clinical adoption. Physiologically-informed techniques to map proton (1H)-MRI ventilation have been proposed. These approaches have demonstrated moderate correlation with hyperpolarized gas MRI. Recently, deep learning (DL) has been used for image synthesis applications, including functional lung image synthesis. Here, we propose a 3D multi-channel convolutional neural network that employs physiologically-informed ventilation mapping and multi-inflation structural 1H-MRI to synthesize 3D ventilation surrogates (PhysVENeT). The dataset comprised paired inspiratory and expiratory 1H-MRI scans and corresponding hyperpolarized gas MRI scans from 170 participants with various pulmonary pathologies. We performed fivefold cross-validation on 150 of these participants and used 20 participants with a previously unseen pathology (post COVID-19) for external validation. Synthetic ventilation surrogates were evaluated using voxel-wise correlation and structural similarity metrics; the proposed PhysVENeT framework significantly outperformed conventional 1H-MRI ventilation mapping and other DL approaches which did not utilize structural imaging and ventilation mapping. PhysVENeT can accurately reflect ventilation defects and exhibits minimal overfitting on external validation data compared to DL approaches that do not integrate physiologically-informed mapping.

Imaging biomarkers of lung ventilation in interstitial lung disease from 129Xe and oxygen enhanced 1H MRI.

PURPOSE: To compare imaging biomarkers from hyperpolarised 129Xe ventilation MRI and dynamic oxygen-enhanced MRI (OE-MRI) with standard pulmonary function tests (PFT) in interstitial lung disease (ILD) patients. To evaluate if biomarkers can separate ILD subtypes and detect early signs of disease resolution or progression. STUDY TYPE: Prospective longitudinal. POPULATION: Forty-one ILD (fourteen idiopathic pulmonary fibrosis (IPF), eleven hypersensitivity pneumonitis (HP), eleven drug-induced ILD (DI-ILD), five connective tissue disease related-ILD (CTD-ILD)) patients and ten healthy volunteers imaged at visit 1. Thirty-four ILD patients completed visit 2 (eleven IPF, eight HP, ten DIILD, five CTD-ILD) after 6 or 26 weeks. FIELD STRENGTH/SEQUENCE: MRI was performed at 1.5 T, including inversion recovery T1 mapping, dynamic MRI acquisition with varying oxygen levels, and hyperpolarised 129Xe ventilation MRI. Subjects underwent standard spirometry and gas transfer testing. ASSESSMENT: Five 1H MRI and two 129Xe MRI ventilation metrics were compared with spirometry and gas transfer measurements. STATISTICAL TEST: To evaluate differences at visit 1 among subgroups: ANOVA or Kruskal-Wallis rank tests with correction for multiple comparisons. To assess the relationships between imaging biomarkers, PFT, age and gender, at visit 1 and for the change between visit 1 and 2: Pearson correlations and multilinear regression models. RESULTS: The global PFT tests could not distinguish ILD subtypes. Percentage ventilated volumes were lower in ILD patients than in HVs when measured with 129Xe MRI (HV 97.4 ± 2.6, CTD-ILD: 91.0 ± 4.8 p = 0.017, DI-ILD 90.1 ± 7.4 p = 0.003, HP 92.6 ± 4.0 p = 0.013, IPF 88.1 ± 6.5 p < 0.001), but not with OE-MRI. 129Xe reported more heterogeneous ventilation in DI-ILD and IPF than in HV, and OE-MRI reported more heterogeneous ventilation in DI-ILD and IPF than in HP or CTD-ILD. The longitudinal changes reported by the imaging biomarkers did not correlate with the PFT changes between visits. DATA CONCLUSION: Neither 129Xe ventilation nor OE-MRI biomarkers investigated in this study were able to differentiate between ILD subtypes, suggesting that ventilation-only biomarkers are not indicated for this task. Limited but progressive loss of ventilated volume as measured by 129Xe-MRI may be present as the biomarker of focal disease progresses. OE-MRI biomarkers are feasible in ILD patients and do not correlate strongly with PFT. Both OE-MRI and 129Xe MRI revealed more spatially heterogeneous ventilation in DI-ILD and IPF.

Longitudinal Lung Function Assessment of Patients Hospitalized With COVID-19 Using 1H and 129Xe Lung MRI.

BACKGROUND: Microvascular abnormalities and impaired gas transfer have been observed in patients with COVID-19. The progression of pulmonary changes in these patients remains unclear. RESEARCH QUESTION: Do patients hospitalized with COVID-19 without evidence of architectural distortion on structural imaging exhibit longitudinal improvements in lung function measured by using 1H and 129Xe MRI between 6 and 52 weeks following hospitalization? STUDY DESIGN AND METHODS: Patients who were hospitalized with COVID-19 pneumonia underwent a pulmonary 1H and 129Xe MRI protocol at 6, 12, 25, and 51 weeks following hospital admission in a prospective cohort study between November 2020 and February 2022. The imaging protocol was as follows: 1H ultra-short echo time, contrast-enhanced lung perfusion, 129Xe ventilation, 129Xe diffusion-weighted, and 129Xe spectroscopic imaging of gas exchange. RESULTS: Nine patients were recruited (age 57 ± 14 [median ± interquartile range] years; six of nine patients were male). Patients underwent MRI at 6 (n = 9), 12 (n = 9), 25 (n = 6), and 51 (n = 8) weeks following hospital admission. Patients with signs of interstitial lung damage were excluded. At 6 weeks, patients exhibited impaired 129Xe gas transfer (RBC to membrane fraction), but lung microstructure was not increased (apparent diffusion coefficient and mean acinar airway dimensions). Minor ventilation abnormalities present in four patients were largely resolved in the 6- to 25-week period. At 12 weeks, all patients with lung perfusion data (n = 6) showed an increase in both pulmonary blood volume and flow compared with 6 weeks, although this was not statistically significant. At 12 weeks, significant improvements in 129Xe gas transfer were observed compared with 6-week examinations; however, 129Xe gas transfer remained abnormally low at weeks 12, 25, and 51. INTERPRETATION: 129Xe gas transfer was impaired up to 1 year following hospitalization in patients who were hospitalized with COVID-19 pneumonia, without evidence of architectural distortion on structural imaging, whereas lung ventilation was normal at 52 weeks.

Large-scale investigation of deep learning approaches for ventilated lung segmentation using multi-nuclear hyperpolarized gas MRI.

Respiratory diseases are leading causes of mortality and morbidity worldwide. Pulmonary imaging is an essential component of the diagnosis, treatment planning, monitoring, and treatment assessment of respiratory diseases. Insights into numerous pulmonary pathologies can be gleaned from functional lung MRI techniques. These include hyperpolarized gas ventilation MRI, which enables visualization and quantification of regional lung ventilation with high spatial resolution. Segmentation of the ventilated lung is required to calculate clinically relevant biomarkers. Recent research in deep learning (DL) has shown promising results for numerous segmentation problems. Here, we evaluate several 3D convolutional neural networks to segment ventilated lung regions on hyperpolarized gas MRI scans. The dataset consists of 759 helium-3 (3He) or xenon-129 (129Xe) volumetric scans and corresponding expert segmentations from 341 healthy subjects and patients with a wide range of pathologies. We evaluated segmentation performance for several DL experimental methods via overlap, distance and error metrics and compared them to conventional segmentation methods, namely, spatial fuzzy c-means (SFCM) and K-means clustering. We observed that training on combined 3He and 129Xe MRI scans using a 3D nn-UNet outperformed other DL methods, achieving a mean ± SD Dice coefficient of 0.963 ± 0.018, average boundary Hausdorff distance of 1.505 ± 0.969 mm, Hausdorff 95th percentile of 5.754 ± 6.621 mm and relative error of 0.075 ± 0.039. Moreover, limited differences in performance were observed between 129Xe and 3He scans in the testing set. Combined training on 129Xe and 3He yielded statistically significant improvements over the conventional methods (p < 0.0001). In addition, we observed very strong correlation and agreement between DL and expert segmentations, with Pearson correlation of 0.99 (p < 0.0001) and Bland-Altman bias of - 0.8%. The DL approach evaluated provides accurate, robust and rapid segmentations of ventilated lung regions and successfully excludes non-lung regions such as the airways and artefacts. This approach is expected to eliminate the need for, or significantly reduce, subsequent time-consuming manual editing.