MRI can provide localized assessment of lung function for monitoring people with lung disease. Hyperpolarized 129Xe MRI directly images pulmonary gas distribution but requires specialized hardware. Conventional 1H MRI acquisitions can also provide functional maps using free-breathing approaches. The purpose of this study is to evaluate regional ventilation derived from 3D ultrashort echo-time (UTE) 1H MRI using Motion-Compensated Low-Rank constrained reconstruction (MoCoLoR), by comparing against 129Xe MRI and pulmonary function testing as reference-standard. The study is retrospective in design. The study included 57 participants (25.4 ± 15.8 years, 35 males and 22 females): 12 healthy volunteers, 20 pediatric, and 25 adult people with cystic fibrosis (CF) scanned between January 2022 and February 2023. Field strength/sequence: 3T; 129Xe: 2D multislice spoiled gradient-recalled sequence; UTE 1H: variable-density 3D radial sequence. K-means-based 129Xe ventilation defect percent (VDP), forced expiratory volume in 1 s (FEV1), and lung clearance index (LCI) were evaluated against UTE 1H VDP from a modified k-means method. The correspondence of ventilation defect maps from 129Xe and UTE 1H was also evaluated. Statistical tests included the Pearson correlation coefficient (r) and t tests, with p < 0.05 considered significant. 129Xe and UTE 1H VDP were significantly correlated (r = 0.64, p = 9.1 × 10 - 8 $$ 9.1\times {10}^{-8} $$ ). Bland-Altman analysis showed a bias of -0.05 (p = 7.2 × 10 - 5 $$ 7.2\times {10}^{-5} $$ ) and limits of agreement of (0.07, -0.17). The Dice spatial accuracy of the UTE-based ventilation defect regions using 129Xe as reference was 0.64 ± 0.05. UTE 1H VDP was significantly correlated with FEV1 (r = -0.54, p = 2.9 × 10 - 4 $$ 2.9\times {10}^{-4} $$ ) and LCI (r = 0.48, p = 5.9 × 10 - 3 $$ 5.9\times {10}^{-3} $$ ) and was significantly different between healthy and CF participants (p = 0.017), although the correlations and differences were stronger for 129Xe VDP. UTE 1H VDP correlated with 129Xe VDP, FEV1, and LCI, and demonstrated high, consistent Dice spatial accuracy against 129Xe VDP. UTE 1H VDP captured variations in lung ventilation and has the advantage that it can be widely implemented on any MR system for evaluation and monitoring of patients with lung disease.

Hyperpolarized 129Xe magnetic resonance imaging (MRI) provides a way to quantify ventilation heterogeneity as ventilation defect percent (VDP), calculated as the volume of unventilated lung volume normalized to the thoracic cavity volume. Currently used methods for quantifying VDP include (1) binary signal-intensity thresholds (Binary-threshold, BT), (2) Gaussian transformation of signal-intensity histogram with standard deviation thresholds or Gaussian-linear-binning (GLB), and (3) iterative centroid-based clustering of the signal-intensity histogram (k-means). These methods have not been directly compared in patients with asthma and chronic obstructive pulmonary disease (COPD), in whom ventilation defects are hallmark findings. Our objective was to quantify and compare VDP using these four different methods.

Data from 175 participants (n=42 healthy, n=43 COPD, n=90 asthma) were retrospectively evaluated using a CNN co-registration and segmentation pipeline and GLB, GLBslice, (slice-wise evaluation of GLB) BT and k-means VDP quantification methods. Linear-regression and Bland-Altman plots were used to quantify inter-method correlations and agreement.

VDP was significantly different using GLB (Asthma: 6±9%, COPD: 7±7%, p<.001) and BT (Asthma: 6±7%, COPD: 10±8%, p<.001) methods compared to GLBslice (Asthma: 12±13%, COPD: 16±15%, p<.001) and k-means (Asthma: 12±12%, COPD: 25±17%, p<.001). VDP calculated using GLB (R2=.64, p<.001), GLBslice (R2=.84, p<.001) and BT (R2=.84, p<.001) was significantly correlated with k-means VDP. Bland-Altman plots revealed wide 95% confidence intervals of agreement for k-means with GLB/GLBslice (COPD -6%/-1%: 42%/23%; asthma -5%/-10%:16%/10%) and BT (COPD -4%:36%; asthma -6%:19%).

VDP differences in patients with asthma and COPD calculated using four methods are important to consider for multi-center studies.

To describe the recent advances in the pathobiology and treatment of mucus hypersecretion in asthma, a critical factor contributing to airway obstruction, inflammation, and impaired lung function.

Significant progress has been made in understanding how mucin protein regulation, mucus viscosity, and adhesion are affected by cytokine-driven inflammation, especially interleukin-13, and defects in ion transport mechanisms. Advances in imaging techniques, such as multidetector computed tomography (MDCT) and hyperpolarized gas MRI, allow for a more precise assessment of mucus plugging and associated ventilation defects. Emerging therapies, including biologicals targeting type-2 (T2) inflammation, and novel mucolytics aimed at modifying mucus properties and secretion, offer promising effects in reducing mucus in severe asthmatics.

The growing understanding of mucus biology and the development of advanced imaging and therapeutic strategies could significantly improve the management of mucus-related complications in asthma. By targeting mucus characteristics, these findings support future approaches to reduce airway obstruction, enhance lung function, and improve clinical outcomes in patients with severe asthma. A deeper understanding of the glycobiology of mucus is critical to develop new therapies.

MR imaging holds the potential to enhance drug development efficiency by de-risking early phase studies and increasing confidence in results. It can improve patient selection, increase repeatability, and provide greater sensitivity to change, thereby enabling smaller, faster clinical trials. For trials in the pulmonary space, hyperpolarized 129Xe MRI is appealing because it provides 3-dimensional imaging of pulmonary ventilation and gas exchange in a brief, non-invasive exam. Metrics derived from 129Xe MRI may be more sensitive to disease progression than conventional lung function assessments and may thus provide a valuable means to evaluate numerous novel pharmacologic and biologic therapies now in development. However, despite the acute need for better patient selection and for prognostic and monitoring biomarkers, 129Xe MR imaging is not yet widely utilized in pulmonary drug development, partly because such trials must be conducted at multiple centers to enroll enough participants. Thus, incorporating 129Xe MRI requires broader dissemination, harmonized image acquisition protocols, standardized dose delivery, visualization, and quantification. Multi-site trials must also be able to operate across all major MRI vendor platforms and diverse software/hardware revisions. To this end, the 129Xe MRI Clinical trials consortium has published a harmonized protocol describing four recommended acquisitions. Here we report on the first industry-sponsored study to deploy this 129Xe MRI/MRS protocol in a multi-center, multi-platform, multi-national study to evaluate longitudinal progression of chronic obstructive pulmonary disease (COPD). We demonstrate the steps necessary to implement standardized 129Xe-MRI acquisition techniques across multiple sites and discuss the practices implemented, quality control approaches, and lessons learned for facilitating and accelerating the implementation of future trials that incorporate this technology. Level of Evidence: 5.

Hyperpolarized 129Xe MRI assesses lung ventilation, often using the ventilation defect percentage (VDP). Unlike VDP, defect distribution index (DDI) quantifies spatial clustering of defects.

To quantify spatial distribution of 129Xe ventilation defects using DDI across pulmonary diseases.

Retrospective.

Four hundred twenty-one subjects (age = 23.1 ± 17.1, female = 230), comprising healthy controls (N = 60) and subjects with obstructive conditions (asthma [N = 25], bronchiolitis obliterans syndrome [BOS, N = 18], cystic fibrosis [CF, N = 90], lymphangioleiomyomatosis [LAM, N = 50]), restrictive conditions (bleomycin-treated cancer survivors [BLEO, N = 14]; fibrotic lung diseases [FLD, N = 92]), bone marrow transplantation (BMT, N = 53), and bronchopulmonary dysplasia (BPD, N = 19).

3 T, two-dimensional multi-slice gradient echo.

Whole-lung mean DDI was extracted from DDI maps; correlated with VDP (percent of pixels <60% of whole-lung mean signal intensity) and pulmonary function tests (PFTs) including FEV1, FVC, and FEV1/FVC. DDI and DDI/VDP, a marker of defect clustering, were compared across diseases.

Pearson correlation analysis and Kruskal-Wallis tests. P < 0.0056 for disease groups, P < 0.0125 for categories.

DDI was significantly elevated in BMT (8.3 ± 11.5), BOS (30.1 ± 57.5), BPD (16.0 ± 46.8), CF (15.4 ± 27.2), and LAM (12.6 ± 34.2) compared to controls (1.8 ± 3.1). DDI correlated significantly with VDP in all groups (r ≥ 0.56) except BLEO, and with PFTs in CF, FLD, and LAM (r ≥ 0.56). Obstructive groups had significantly higher mean DDI (14.0 ± 32.0) than controls (1.8 ± 3.0) and restrictive groups (4.0 ± 12.0). DDI/VDP was significantly lower in the restrictive group (0.6 ± 0.6) than controls (0.8 ± 0.6) and obstructive group (1.0 ± 1.0).

DDI may provide insights into the distribution of ventilation defects across diseases.

3 TECHNICAL EFFICACY: Stage 2.

Hyperpolarized 129Xe MRI presents opportunities to assess regional pulmonary microstructure and function. Ongoing advancements in hardware, sequences, and image processing have helped it become increasingly adopted for both research and clinical use. As the number of applications and users increase, standardization becomes crucial. To that end, this study developed an executable, open-source 129Xe image processing pipeline (XIPline) to provide a user-friendly, graphical user interface-based analysis pipeline to analyze and visualize 129Xe MR data, including scanner calibration, ventilation, diffusion-weighted, and gas exchange images.

The customizable XIPline is designed in MATLAB to analyze data from all three major scanner platforms. Calibration data is processed to calculate optimal flip angle and determine129Xe frequency offset. Data processing includes loading, reconstructing, registering, segmenting, and post-processing images. Ventilation analysis incorporates three common algorithms to calculate ventilation defect percentage and novel techniques to assess defect distribution and ventilation texture. Diffusion analysis features ADC mapping, modified linear binning to account for ADC age-dependence, and common diffusion morphometry methods. Gas exchange processing uses a generalized linear binning for data acquired using 1-point Dixon imaging.

The XIPline workflow is demonstrated using analysis from representative calibration, ventilation, diffusion, and gas exchange data.

The application will reduce redundant effort when implementing new techniques across research sites by providing an open-source framework for developers. In its current form, it offers a robust and adaptable platform for 129Xe MRI analysis to ensure methodological consistency, transparency, and support for collaborative research across multiple sites and MRI manufacturers.

Hyperpolarized 129Xe MRI quantifies ventilation-defect-percent (VDP), the ratio of 129Xe signal-void to the anatomic 1H MRI thoracic-cavity-volume. VDP is associated with airway inflammation and disease control and serves as a treatable trait in therapy studies. Semi-automated VDP pipelines require time-intensive observer interactions. Current convolutional neural network (CNN) approaches for quantifying VDP lack external validation, which limits multicenter utilization. Our objective was to develop an automated and externally validated deep-learning pipeline to quantify pulmonary 129Xe MRI VDP.

1H and 129Xe MRI data from the primary site (Site1) were used to train and test a CNN segmentation and registration pipeline, while two independent sites (Site2 and Site3) provided external validation. Semi-automated and CNN-based registration error was measured using mean-absolute-error (MAE) while segmentation error was measured using generalized-Dice-similarity coefficient (gDSC). CNN and semi-automated VDP were compared using linear regression and Bland-Altman analysis.

Training/testing used data from 205 participants (healthy volunteers, asthma, COPD, long-COVID; mean age=54 ± 16y; 119 females) from Site1. External validation used data from 71 participants. CNN and semi-automated 1H and 129Xe registrations agreed (MAE=0.3°, R2 =0.95 rotation; 1.1%, R2 =0.79 scaling; 0.2/0.5px, R2 =0.96/0.95, x/y-translation; all p < .001). Thoracic-cavity and ventilation segmentations were also spatially corresponding (gDSC=0.92 and 0.88, respectively). CNN VDP correlated with semi-automated VDP (Site1 R2/ρ = .97/.95, bias=-0.5%; Site2 R2/ρ = .85/.93, bias=-0.9%; Site3 R2/ρ = .95/.89, bias=-0.8%, all p < .001).

An externally validated CNN registration/segmentation model demonstrated strong agreement with low error compared to the semi-automated method. CNN and semi-automated registrations, thoracic-cavity-volume and ventilation-volume segmentations were highly correlated with high gDSC for the datasets.

To compare phase-resolved functional lung (PREFUL) regional ventilation derived from a free breathing 3D UTE radial MRI acquisition to hyperpolarized 129Xe-MRI (Xe-MRI), conventional 2D multi-slice PREFUL MRI, and pulmonary function tests in pediatric cystic fibrosis (CF) lung disease.

Free-breathing 3D UTE and 2D multi-slice 1H MRI as well as Xe-MRI were acquired in 12 stable pediatric CF patients. Using PREFUL, regional ventilation (RVent) maps were calculated from the free-breathing data. Ventilation defect percentage (VDP) was determined from 3D and 2D RVent maps (2D VDPRVent and 3D VDPRVent, respectively) and Xe-MRI ventilation (VDPXe). VDP was calculated for the whole lung and for eight regions based on left/right, anterior/posterior, and superior/inferior divisions of the lung. Global and regional VDP was compared between the three methods using Bland-Altman analysis, linear mixed model-based correlation, and one-way analysis of variance and multiple comparisons tests.

Global 3D VDPRVent, VDPXe, and 2D VDPRVent were all strongly correlated (all R2 > 0.62, p < 0.0001) and showed minimal, non-significant bias (all <2%, p > 0.05). Three dimensional and 2D VDPRVent significantly correlated to VDPXe in most of the separate lung regions (R2 = 0.18-0.74, p < 0.04), but showed lower inter-agreement. The superior/anterior lung regions showed the least agreement between all three methods (all p > 0.12).

Absolute VDP assessed by 3D UTE PREFUL MRI showed good global agreement with Xe-MRI and 2D multi-slice PREFUL MRI in pediatric CF lung disease. Therefore, 3D UTE PREFUL MRI offers a sensitive and potentially more accessible alternative to Xe-MRI for regional volumetric evaluation of ventilation.

In respiratory medicine, there is a need for sensitive measures of regional lung function that can be performed using standard imaging technology, without the need for inhaled or intravenous contrast agents.

To describe VOxel-wise Lung VEntilation (VOLVE), a new method for quantifying regional lung ventilation (V) and perfusion (Q) using free-breathing proton MRI, and to evaluate VOLVE in healthy never-smokers, healthy people with smoking history, and people with chronic obstructive pulmonary disease (COPD).

Prospective pilot.

Twelve healthy never-smoker participants (age 30.3 ± 12.5 years, five male), four healthy participants with smoking history (>10 pack-years) (age 42.5 ± 18.3 years, one male), and 12 participants with COPD (age 62.8 ± 11.1 years, seven male).

Single-slice free-breathing two-dimensional fast field echo sequence at 3 T.

A novel postprocessing was developed to evaluate the MR signal changes in the lung parenchyma using a linear regression-based approach, which makes use of all the data in the time series for maximum sensitivity. V/Q-weighted maps were produced by computing the cross-correlation, lag and gradient between the respiratory/cardiac phase time course and lung parenchyma signal time courses. A comparison of histogram median and skewness values and spirometry was performed.

Kruskal-Wallis tests with Dunn's multiple comparison tests to compare VOLVE metrics between groups; Spearman correlation to assess the correlation between MRI and spirometry-derived parameters; and Bland-Altman analysis and coefficient of variation to evaluate repeatability were used. A P-value <0.05 was considered significant.

Significant differences between the groups were found for ventilation between healthy never-smoker and COPD groups (median XCCV, LagV, and GradV) and perfusion (median XCCQ, LagQ, and GradQ). Minimal bias and no significant differences between intravisit scans were found (P range = 0.12-0.97).

This preliminary study showed that VOLVE has potential to provide metrics of function quantification.

2 TECHNICAL EFFICACY: Stage 1.

Modern pulmonary imaging can reveal underlying pathologic and pathophysiologic changes in the lungs of people with asthma, with important clinical implications. A multitude of imaging modalities, including computed tomography, magnetic resonance imaging, optical coherence tomography, and endobronchial ultrasound, are now being used to examine underlying structure-function relationships. Imaging-based biomarkers from these techniques, including airway dimensions, blood vessel volumes, mucus scores, extent of ventilation defect, and extent of air trapping, often have increased sensitivity compared with that of traditional lung function measurements and are increasingly being used as end points in clinical trials. Imaging has been crucial to recent improvements in our understanding of the relationships between type 2 inflammation, eosinophilia, and mucus extent. With the advent of effective anti-type 2 biologic therapies, computed tomography and magnetic resonance imaging techniques can identify not just which patients benefit from therapy but why they benefit. Clinical trials have begun to assess the utility of imaging to prospectively plan airway therapy targets in bronchial thermoplasty and have potential to direct future bronchoscopic therapies. Together, imaging techniques provide a diverse set of tools to investigate how spatially distributed airway, blood, and parenchymal abnormalities shape disease heterogeneity in patients with asthma.

Transaxial CT imaging is the main clinical imaging modality for the assessment of COVID-induced lung damage. However, this type of data does not quantify the functional properties of the lung. The objective is to provide non-invasive personalized cartographies of lung stiffness for long-COVID patients using MR elastography (MRE) and follow-up the evolution of this quantitative mapping over time.

Seven healthy and seven long-COVID participants underwent CT and MRE imaging at total lung capacity. After CT test, a senior radiologist visually analyzed the lung structure. Less than one month later, a first MRI (1.5 T, GRE sequence) lung density test followed by a first MRE (SE-EPI sequence) test were performed. Gadolinium-doped water phantom and a pneumatic driver (vibration frequency: 50 Hz), placed on the sternum, were used for MRI and MRE tests, respectively. Personalized cartographies of the stiffness were obtained, by two medical imaging engineers, using a specific post processing (MMDI algorithm). The monitoring (lung density, stiffness) was carried out no later than 11 months for each COVID patient. Wilcoxon's tests and an intra-class correlation coefficient (ICC) were used for statistical analysis.

The density for long-COVID patients was significantly (P = 0.047) greater (170 kg.m-3) compared to healthy (125 kg.m-3) subjects. After the first MRE test, the stiffness measured for the healthy subjects was in the same range (median value (interquartile range, IQR): 0.93 (0.09) kPa), while the long-COVID patients showed a larger stiffness range (from 1.39 kPa to 2.05 kPa). After a minimum delay of 5 months, the second MRE test showed a decrease of stiffness (from 22 % to 40 %) for every long-COVID patient. The inter-operator agreement was excellent (intra-class correlation coefficient: 0.93 [0.78-0.97]).

The MRE test is sensitive enough to monitor disease-induced change in lung stiffness (increase with COVID symptoms and decrease with recovery). This non-invasive modality could yield complementary information as a new imaging biomarker to follow up long-COVID patients.

To comprehensively assess the impact of aging, cigarette smoking, and chronic obstructive pulmonary disease (COPD) on pulmonary physiology using 129Xe MR.

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).

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.

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.

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.

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.

It remains difficult to predict longitudinal outcomes in long-COVID, even with chest CT and functional MRI. 129Xe MRI reflects airway dysfunction, measured using ventilation defect percent (VDP) and in long-COVID patients, MRI VDP was abnormal, suggestive of airways disease. While MRI VDP and quality-of-life improved 15-month post-COVID infection, both remained abnormal. To better understand the relationship of airways disease and quality-of-life improvements in patients with long-COVID, we extracted 129Xe ventilation MRI textures and generated machine-learning models in an effort to predict improved quality-of-life, 15-month post-infection.

Long-COVID patients provided written-informed consent to 3-month and 15-month post-infection visits. Pyradiomics was used to extract 129Xe ventilation MRI texture features, which were ranked using a Random-Forest classifier. Top-ranking features were used in classification models to dichotomize patients based on St. George's Respiratory Questionnaire (SGRQ) score improvement greater than the minimal-clinically-important-difference (MCID). Classification performance was evaluated using the area under the receiver-operator-characteristic-curve (AUC), sensitivity, and specificity.

120 texture features were extracted from 129Xe ventilation MRI in 44 long-COVID participants (54 ± 14 years), including 30 (52 ± 12 years) with ΔSGRQ≥MCID and 14 (58 ± 18 years) with ΔSGRQ

CONCLUSION

A machine learning model exclusively trained on 129Xe MRI ventilation textures explained improved SGRQ-scores 12 months later, and outperformed clinical models. Their unique spatial-intensity information helps build our understanding about long-COVID airway dysfunction.

Collapse

To demonstrate the feasibility of zigzag sampling for 3D rapid hyperpolarized 129Xe ventilation MRI in human.

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.

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%]).

Zigzag sampling combined with GRE sequence provides a way for rapid 129Xe ventilation imaging.

Emergent evidence in several respiratory diseases supports translational potential for Phase-Resolved Functional Lung (PREFUL) MRI to spatially quantify ventilation but its feasibility and physiological relevance have not been demonstrated in patients with asthma. This study compares PREFUL-derived ventilation defect percent (VDP) in severe asthma patients to healthy controls and measures its responsiveness to bronchodilator therapy and relation to established measures of airways disease.

Forty-one adults with severe asthma and seven healthy controls performed same-day free-breathing 1H MRI, 129Xe MRI, spirometry, and oscillometry. A subset of participants (n = 23) performed chest CT and another subset of participants with asthma (n = 19) repeated 1H MRI following the administration of a bronchodilator. VDP was calculated for both PREFUL and 129Xe MRI. Additionally, the percent of functional small airways disease was determined from CT parametric response maps (PRMfSAD).

PREFUL VDP measured pre-bronchodilator (19.1% [7.4-43.3], p = 0.0002) and post-bronchodilator (16.9% [6.1-38.4], p = 0.0007) were significantly greater than that of healthy controls (7.5% [3.7-15.5]) and was significantly decreased post-bronchodilator (from 21.9% [10.1-36.9] to 16.9% [6.1-38.4], p = 0.0053). PREFUL VDP was correlated with spirometry (FEV1%pred: r = -0.46, p = 0.0023; FVC%pred: r = -0.35, p = 0.024, FEV1/FVC: r = -0.46, p = 0.0028), 129Xe MRI VDP (r = 0.39, p = 0.013), and metrics of small airway disease (CT PRMfSAD: r = 0.55, p = 0.021; Xrs5 Hz: r = -0.44, p = 0.0046, and AX: r = 0.32, p = 0.044).

PREFUL-derived VDP is responsive to bronchodilator therapy in asthma and is associated with measures of airflow obstruction and small airway dysfunction. These findings validate PREFUL VDP as a physiologically relevant and accessible ventilation imaging outcome measure in asthma.

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.

The respiratory consequences of acute COVID-19 infection and related symptoms tend to resolve 4 weeks post-infection. However, for some patients, new, recurrent, or persisting symptoms remain beyond the acute phase and persist for months, post-infection. The symptoms that remain have been referred to as long-COVID. A number of research sites employed 129 Xe magnetic resonance imaging (MRI) during the pandemic and evaluated patients post-infection, months after hospitalization or home-based care as a way to better understand the consequences of infection on 129 Xe MR gas-exchange and ventilation imaging. A systematic review and comprehensive search were employed using MEDLINE via PubMed (April 2023) using the National Library of Medicine's Medical Subject Headings and key words: post-COVID-19, MRI, 129 Xe, long-COVID, COVID pneumonia, and post-acute COVID-19 syndrome. Fifteen peer-reviewed manuscripts were identified including four editorials, a single letter to the editor, one review article, and nine original research manuscripts (2020-2023). MRI and MR spectroscopy results are summarized from these prospective, controlled studies, which involved small sample sizes ranging from 9 to 76 participants. Key findings included: 1) 129 Xe MRI gas-exchange and ventilation abnormalities, 3 months post-COVID-19 infection, and 2) a combination of MRI gas-exchange and ventilation abnormalities alongside persistent symptoms in patients hospitalized and not hospitalized for COVID-19, 1-year post-infection. The persistence of respiratory symptoms and 129 Xe MRI abnormalities in the context of normal or nearly normal pulmonary function test results and chest computed tomography (CT) was consistent. Longitudinal improvements were observed in long-term follow-up of long-COVID patients but mean 129 Xe gas-exchange, ventilation heterogeneity values and symptoms remained abnormal, 1-year post-infection. Pulmonary functional MRI using inhaled hyperpolarized 129 Xe gas has played a role in detecting gas-exchange and ventilation abnormalities providing complementary information that may help develop our understanding of the root causes of long-COVID. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 5.

Hyperpolarized (HP) gas MRI enables the clear visualization of lung structure and function. Clinically relevant biomarkers, such as ventilated defect percentage (VDP) derived from this modality can quantify lung ventilation function. However, long imaging time leads to image quality degradation and causes discomfort to the patients. Although accelerating MRI by undersampling k-space data is available, accurate reconstruction and segmentation of lung images are quite challenging at high acceleration factors.

To simultaneously improve the performance of reconstruction and segmentation of pulmonary gas MRI at high acceleration factors by effectively utilizing the complementary information in different tasks.

A complementation-reinforced network is proposed, which takes the undersampled images as input and outputs both the reconstructed images and the segmentation results of lung ventilation defects. The proposed network comprises a reconstruction branch and a segmentation branch. To effectively exploit the complementary information, several strategies are designed in the proposed network. Firstly, both branches adopt the encoder-decoder architecture, and their encoders are designed to share convolutional weights for facilitating knowledge transfer. Secondly, a designed feature-selecting block discriminately feeds shared features into decoders of both branches, which can adaptively pick suitable features for each task. Thirdly, the segmentation branch incorporates the lung mask obtained from the reconstructed images to enhance the accuracy of the segmentation results. Lastly, the proposed network is optimized by a tailored loss function that efficiently combines and balances these two tasks, in order to achieve mutual benefits.

Experimental results on the pulmonary HP 129 Xe MRI dataset (including 43 healthy subjects and 42 patients) show that the proposed network outperforms state-of-the-art methods at high acceleration factors (4, 5, and 6). The peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and Dice score of the proposed network are enhanced to 30.89, 0.875, and 0.892, respectively. Additionally, the VDP obtained from the proposed network has good correlations with that obtained from fully sampled images (r = 0.984). At the highest acceleration factor of 6, the proposed network promotes PSNR, SSIM, and Dice score by 7.79%, 5.39%, and 9.52%, respectively, in comparison to the single-task models.

The proposed method effectively enhances the reconstruction and segmentation performance at high acceleration factors up to 6. It facilitates fast and high-quality lung imaging and segmentation, and provides valuable support in the clinical diagnosis of lung diseases.

Purpose To determine if proton (1H) MRI-derived specific ventilation is responsive to bronchodilator (BD) therapy and associated with clinical biomarkers of type 2 airway inflammation and airways dysfunction in severe asthma. Materials and Methods In this prospective study, 27 participants with severe asthma (mean age, 52 years ± 9 [SD]; 17 female, 10 male) and seven healthy controls (mean age, 47 years ± 16; five female, two male), recruited between 2018 and 2021, underwent same-day spirometry, respiratory oscillometry, and tidal breathing 1H MRI. Participants with severe asthma underwent all assessments before and after BD therapy, and type 2 airway inflammatory biomarkers were determined (blood eosinophil count, sputum eosinophil percentage, sputum eosinophil-free granules, and fraction of exhaled nitric oxide) to generate a cumulative type 2 biomarker score. Specific ventilation was derived from tidal breathing 1H MRI and its response to BD therapy, and relationships with biomarkers of type 2 airway inflammation and airway dysfunction were evaluated. Results Mean MRI specific ventilation improved with BD inhalation (from 0.07 ± 0.04 to 0.11 ± 0.04, P < .001). Post-BD MRI specific ventilation (P = .046) and post-BD change in MRI specific ventilation (P = .006) were greater in participants with asthma with type 2 low biomarkers compared with participants with type 2 high biomarkers of airway inflammation. Post-BD change in MRI specific ventilation was correlated with change in forced expiratory volume in 1 second (r = 0.40, P = .04), resistance at 5 Hz (r = -0.50, P = .01), resistance at 19 Hz (r = -0.42, P = .01), reactance area (r = -0.54, P < .01), and reactance at 5 Hz (r = 0.48, P = .01). Conclusion Specific ventilation evaluated with tidal breathing 1H MRI was responsive to BD therapy and was associated with clinical biomarkers of airways disease in participants with severe asthma. Keywords: MRI, Severe Asthma, Ventilation, Type 2 Inflammation Supplemental material is available for this article. © RSNA, 2023 See also the commentary by Moore and Chandarana in this issue.

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

The minimal clinically important difference (MCID) and upper limit of normal (ULN) for MRI ventilation defect percent (VDP) were previously reported for hyperpolarized 3He gas MRI. Hyperpolarized 129Xe VDP is more sensitive to airway dysfunction than 3He, therefore the objective of this study was to determine the ULN and MCID for 129Xe MRI VDP in healthy and asthma participants.

We retrospectively evaluated healthy and asthma participants who underwent spirometry and 129XeMRI on a single visit; participants with asthma completed the asthma control questionnaire (ACQ-7). The MCID was estimated using distribution- (smallest detectable difference [SDD]) and anchor-based (ACQ-7) methods. Two observers measured VDP (semiautomated k-means-cluster segmentation algorithm) in 10 participants with asthma, five-times each in random order, to determine SDD. The ULN was estimated based on the 95% confidence interval of the relationships between VDP and age.

Mean VDP was 1.6 ± 1.2% for healthy (n = 27) and 13.7 ± 12.9% for asthma participants (n = 55). ACQ-7 and VDP were correlated (r = .37, p = .006; VDP = 3.5·ACQ + 4.9). The anchor-based MCID was 1.75% while the mean SDD and distribution-based MCID was 2.25%. VDP was correlated with age for healthy participants (p = .56, p =.003; VDP = .04·Age-.01). The ULN for all healthy participants was 2.0%. By age tertiles, the ULN was 1.3% ages 18-39 years, 2.5% for 40-59 years and 3.8% for 60-79 years.

The 129Xe MRI VDP MCID was estimated in participants with asthma; the ULN was estimated in healthy participants across a range of ages, both of which provide a way to interpret VDP measurements in clinical investigations.

This paper provides an in-depth overview of Deep Neural Networks and their application in the segmentation and analysis of lung Magnetic Resonance Imaging (MRI) scans, specifically focusing on hyperpolarized gas MRI and the quantification of lung ventilation defects. An in-depth understanding of Deep Neural Networks is presented, laying the groundwork for the exploration of their use in hyperpolarized gas MRI and the quantification of lung ventilation defects. Five distinct studies are examined, each leveraging unique deep learning architectures and data augmentation techniques to optimize model performance. These studies encompass a range of approaches, including the use of 3D Convolutional Neural Networks, cascaded U-Net models, Generative Adversarial Networks, and nnU-net for hyperpolarized gas MRI segmentation. The findings highlight the potential of deep learning methods in the segmentation and analysis of lung MRI scans, emphasizing the need for consensus on lung ventilation segmentation methods.

Quantification of 129Xe MRI relies on accurate segmentation of the thoracic cavity, typically performed manually using a combination of 1H and 129Xe scans. This can be accelerated by using Convolutional Neural Networks (CNNs) that segment only the 129Xe scan. However, this task is complicated by peripheral ventilation defects, which requires training CNNs with large, diverse datasets. Here, we accelerate the creation of training data by synthesizing 129Xe images with a variety of defects. We use this to train a 3D model to provide thoracic cavity segmentation from 129Xe ventilation MRI alone.

Training and testing data consisted of 22 and 33 3D 129Xe ventilation images. Training data were expanded to 484 using Template-based augmentation while an additional 298 images were synthesized using the Pix2Pix model. This data was used to train both a 2D U-net and 3D V-net-based segmentation model using a combination of Dice-Focal and Anatomical Constraint loss functions. Segmentation performance was compared using Dice coefficients calculated over the entire lung and within ventilation defects.

Performance of both U-net and 3D segmentation was improved by including synthetic training data. The 3D models performed significantly better than U-net, and the 3D model trained with synthetic 129Xe images exhibited the highest overall Dice score of 0.929. Moreover, addition of synthetic training data improved the Dice score in ventilation defect regions from 0.545 to 0.588 for U-net and 0.739 to 0.765 for the 3D model.

It is feasible to obtain high-quality segmentations from 129Xe scan alone using 3D models trained with additional synthetic images.

The purpose of this study is to assess the intra- and interscan repeatability of free-breathing phase-resolved functional lung (PREFUL) MRI in stable pediatric cystic fibrosis (CF) lung disease in comparison to static breath-hold hyperpolarized 129-xenon MRI (Xe-MRI) and pulmonary function tests.

Free-breathing 1-hydrogen MRI and Xe-MRI were acquired from 15 stable pediatric CF patients and seven healthy age-matched participants on two visits, 1 month apart. Same-visit MRI scans were also performed on a subgroup of the CF patients. Following the PREFUL algorithm, regional ventilation (RVent) and regional flow volume loop cross-correlation maps were determined from the free-breathing data. Ventilation defect percentage (VDP) was determined from RVent maps (VDP_RVent ), regional flow volume loop cross-correlation maps (VDP_CC ), VDP_RVent ∪ VDP_CC , and multi-slice Xe-MRI. Repeatability was evaluated using Bland-Altman analysis, coefficient of repeatability (CR), and intraclass correlation.

Minimal bias and no significant differences were reported for all PREFUL MRI and Xe-MRI VDP parameters between intra- and intervisits (all P > 0.05). Repeatability of VDP_RVent , VDP_CC , VDP_RVent ∪ VDP_CC , and multi-slice Xe-MRI were lower between the two-visit scans (CR = 14.81%, 15.36%, 16.19%, and 9.32%, respectively) in comparison to the same-day scans (CR = 3.38%, 2.90%, 1.90%, and 3.92%, respectively). pulmonary function tests showed high interscan repeatability relative to PREFUL MRI and Xe-MRI.

PREFUL MRI, similar to Xe-MRI, showed high intravisit repeatability but moderate intervisit repeatability in CF, which may be due to inherent disease instability, even in stable patients. Thus, PREFUL MRI may be considered a suitable outcome measure for future treatment response studies.

Respiratory diseases are leading causes of death and disability in the world. While early diagnosis is key, this has proven difficult due to the lack of sensitive and non-invasive tools. Computed tomography is regarded as the gold standard for structural lung imaging but lacks functional information and involves significant radiation exposure. Lung magnetic resonance imaging (MRI) has historically been challenging due to its short T2 and low proton density. Hyperpolarised gas MRI is an emerging technique that is able to overcome these difficulties, permitting the functional and microstructural evaluation of the lung. Other novel imaging techniques such as fluorinated gas MRI, oxygen-enhanced MRI, Fourier decomposition MRI and phase-resolved functional lung imaging can also be used to interrogate lung function though they are currently at varying stages of development. This article provides a clinically focused review of these contrast and non-contrast MR imaging techniques and their current applications in lung disease.

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

We previously showed in patients with poorly controlled eosinophilic asthma that a single dose of benralizumab resulted in significantly improved asthma-control-questionnaire (ACQ-6) score and ¹²⁹Xe MRI ventilation defect percent (VDP), 28 days post-injection, and ¹²⁹Xe MRI VDP and CT airway mucus occlusions were shown to independently predict this early ACQ-6 response to benralizumab.

Do early VDP responses at 28 days persist, and do FEV₁, fractional exhaled nitric oxide (Feno), and mucus plug score improve during a 2.5 year treatment period?

Participants with poorly controlled eosinophilic asthma completed spirometry, ACQ-6, and MRI, 28 days, 1, and 2.5 years after benralizumab; chest CT was acquired at enrollment and 2.5 years later.

Of 29 participants evaluated at 28 days post-benralizumab, 16 participants returned for follow-up while on therapy at 1 year, and 13 participants were evaluable while on therapy at 2.5 years, post-benralizumab initiation. As compared with 28 days post-benralizumab, ACQ-6 score (2.0 ± 1.4) significantly improved after 1 year (0.5 ± 0.6, P = .02; 95% CI, 0.1-1.1) and 2.5 years (0.5 ± 0.5, P = .03; 95% CI, 0.1-1.1). The mean VDP change at 2.5 years (-4% ± 3%) was greater than the minimal clinically important difference, but not significantly different from VDP measured 28 days post-benralizumab. Mucus score (3 ± 4) was significantly improved at 2.5 years (1 ± 1, P = .03; 95% CI, 0.3-5.5). In six of eight participants with previous occlusions, mucus plugs vanished or substantially diminished 2.5 years later. VDP (P < .001) and mucus score (P < .001) measured at baseline, but not Feno or FEV₁, independently predicted ACQ score after 2.5 years.

In poorly controlled eosinophilic asthma, early MRI VDP responses at 28 days post-benralizumab persisted 2.5 years later, alongside significantly improved mucus score and asthma control.

Multiple thoracic imaging modalities have been developed to link structure to function in the diagnosis and monitoring of lung disease. Volumetric computed tomography (CT) renders three-dimensional maps of lung structures and may be combined with positron emission tomography (PET) to obtain dynamic physiological data. Magnetic resonance imaging (MRI) using ultrashort-echo time (UTE) sequences has improved signal detection from lung parenchyma; contrast agents are used to deduce airway function, ventilation-perfusion-diffusion, and mechanics. Proton MRI can measure regional ventilation-perfusion ratio. Quantitative imaging (QI)-derived endpoints have been developed to identify structure-function phenotypes, including air-blood-tissue volume partition, bronchovascular remodeling, emphysema, fibrosis, and textural patterns indicating architectural alteration. Coregistered landmarks on paired images obtained at different lung volumes are used to infer airway caliber, air trapping, gas and blood transport, compliance, and deformation. This document summarizes fundamental "good practice" stereological principles in QI study design and analysis; evaluates technical capabilities and limitations of common imaging modalities; and assesses major QI endpoints regarding underlying assumptions and limitations, ability to detect and stratify heterogeneous, overlapping pathophysiology, and monitor disease progression and therapeutic response, correlated with and complementary to, functional indices. The goal is to promote unbiased quantification and interpretation of in vivo imaging data, compare metrics obtained using different QI modalities to ensure accurate and reproducible metric derivation, and avoid misrepresentation of inferred physiological processes. The role of imaging-based computational modeling in advancing these goals is emphasized. Fundamental principles outlined herein are critical for all forms of QI irrespective of acquisition modality or disease entity.

Outside eosinophilia, current clinical asthma phenotypes do not show strong relationships with disease pathogenesis or treatment responses. While chest x-ray computed tomography (CT) phenotypes have previously been explored, functional MRI measurements provide complementary phenotypic information.

To derive novel data-driven asthma phenotypic clusters using functional MRI airway biomarkers that better describe airway pathologies in patients.

Retrospective.

A total of 45 patients with asthma who underwent post-bronchodilator ¹²⁹ Xe MRI, volume-matched CT, spirometry and plethysmography within a 90-minute visit.

Three-dimensional gradient-recalled echo ¹²⁹ Xe ventilation sequence at 3 T.

We measured MRI ventilation defect percent (VDP), CT airway wall-area percent (WA%), wall-thickness (WT, WT* [*normalized for age/sex/height]), lumen-area (LA), lumen-diameter (D, D*) and total airway count (TAC). Univariate relationships were utilized to select variables for k-means cluster analysis and phenotypic subgroup generation. Spirometry and plethysmography measurements were compared across imaging-based clusters.

Spearman correlation (ρ), one-way analysis of variance (ANOVA) or Kruskal-Wallis tests with post hoc Bonferroni correction for multiple comparisons, significance level 0.05.

Based on limited common variance (Kaiser-Meyer-Olkin-measure = 0.44), four unique clusters were generated using MRI VDP, TAC, WT* and D* (52 ± 14 years, 27 female). Imaging measurements were significantly different across clusters as was the forced expiratory volume in 1-second (FEV₁ %_pred ), residual volume/total lung capacity and airways resistance. Asthma-control (P = 0.9), quality-of-life scores (P = 0.7) and the proportions of severe-asthma (P = 0.4) were not significantly different. Cluster1 (n = 15/8 female) reflected mildly abnormal CT airway measurements and FEV₁ with moderately abnormal VDP. Cluster2 (n = 12/12 female) reflected moderately abnormal TAC, WT and FEV₁ . In Cluster3 and Cluster4 (n = 14/6 female, n = 4/1 female, respectively), there was severely reduced TAC, D and FEV₁ , but Cluster4 also had significantly worse, severely abnormal VDP (7 ± 5% vs. 41 ± 12%).

We generated four proof-of-concept MRI-derived clusters of asthma with distinct structure-function pathologies. Cluster analysis of asthma using ¹²⁹ Xe MRI in combination with CT biomarkers is feasible and may challenge currently used paradigms for asthma phenotyping and treatment decisions.

3 TECHNICAL EFFICACY: Stage.

To develop an end-to-end deep learning (DL) framework to segment ventilation defects on pulmonary hyperpolarized MRI.

The Multi-Ethnic Study of Atherosclerosis Chronic Obstructive Pulmonary Disease (COPD) study is a nested longitudinal case-control study in older smokers. Between February 2016 and July 2017, 56 participants (age, mean ± SD, 74 ± 8 years; 34 men) underwent same breath-hold proton (1H) and helium (3He) MRI, which were annotated for non-ventilated, hypo-ventilated, and normal-ventilated lungs. In this retrospective DL study, 820 1H and 3He slices from 42/56 (75%) participants were randomly selected for training, with the remaining 14/56 (25%) for test. Full lung masks were segmented using a traditional U-Net on 1H MRI and were imported into a cascaded U-Net, which were used to segment ventilation defects on 3He MRI. Models were trained with conventional data augmentation (DA) and generative adversarial networks (GAN)-DA.

Conventional-DA improved 1H and 3He MRI segmentation over the non-DA model (P = 0.007 to 0.03) but GAN-DA did not yield further improvement. The cascaded U-Net improved non-ventilated lung segmentation (P < 0.005). Dice similarity coefficients (DSC) between manually and DL-segmented full lung, non-ventilated, hypo-ventilated, and normal-ventilated regions were 0.965 ± 0.010, 0.840 ± 0.057, 0.715 ± 0.175, and 0.883 ± 0.060, respectively. We observed no statistically significant difference in DCSs between participants with and without COPD (P = 0.41, 0.06, and 0.18 for non-ventilated, hypo-ventilated, and normal-ventilated regions, respectively).

The proposed cascaded U-Net framework generated fully-automated segmentation of ventilation defects on 3He MRI among older smokers with and without COPD that is consistent with our reference method.

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.

Patients often report persistent symptoms beyond the acute infectious phase of COVID-19. Hyperpolarised ¹²⁹Xe MRI provides a way to directly measure airway functional abnormalities; the clinical relevance of ¹²⁹Xe MRI ventilation defects in ever-hospitalised and never-hospitalised patients who had COVID-19 has not been ascertained. It remains unclear if persistent symptoms beyond the infectious phase are related to small airways disease and ventilation heterogeneity. Hence, we measured ¹²⁹Xe MRI ventilation defects, pulmonary function and symptoms in ever-hospitalised and never-hospitalised patients who had COVID-19 with persistent symptoms consistent with post-acute COVID-19 syndrome (PACS).

Consenting participants with a confirmed diagnosis of PACS completed ¹²⁹Xe MRI, CT, spirometry, multi-breath inert-gas washout, 6-minute walk test, St. George's Respiratory Questionnaire (SGRQ), modified Medical Research Council (mMRC) dyspnoea scale, modified Borg scale and International Physical Activity Questionnaire. Consenting ever-COVID volunteers completed ¹²⁹Xe MRI and pulmonary function tests only.

Seventy-six post-COVID and nine never-COVID participants were evaluated. Ventilation defect per cent (VDP) was abnormal and significantly greater in ever-COVID as compared with never-COVID participants (p<0.001) and significantly greater in ever-hospitalised compared with never-hospitalised participants who had COVID-19 (p=0.048), in whom diffusing capacity of the lung for carbon-monoxide (p=0.009) and 6-minute walk distance (6MWD) (p=0.005) were also significantly different. ¹²⁹Xe MRI VDP was also related to the 6MWD (p=0.02) and post-exertional SpO₂ (p=0.002). Participants with abnormal VDP (≥4.3%) had significantly worse 6MWD (p=0.003) and post-exertional SpO₂ (p=0.03).

¹²⁹Xe MRI VDP was significantly worse in ever-hospitalised as compared with never-hospitalised participants and was related to 6MWD and exertional SpO₂ but not SGRQ or mMRC scores.

NCT05014516.

Evaluation of structural lung abnormalities with magnetic resonance imaging (MRI) has previously been shown to be predictive of clinical neonatal outcomes in preterm birth. MRI during free-breathing with phase-resolved functional lung (PREFUL) may allow for complimentary functional information without exogenous contrast.

To investigate the feasibility of structural and functional pulmonary MRI in a cohort of neonates and infants with no cardiorespiratory disease. Macrovascular pulmonary blood flows were also evaluated.

Prospective.

Ten term infants with no clinically defined cardiorespiratory disease were imaged. Infants recruited from the general population and neonatal intensive care unit (NICU) were studied.

T₁ -weighted VIBE, T₂ -weighted BLADE uncorrected for motion. Ultrashort echo time (UTE) and 3D-flow data were acquired during free-breathing with self-navigation and retrospective reconstruction. Single slice 2D-gradient echo (GRE) images were acquired during free-breathing for PREFUL analysis. Imaging was performed at 3 T.

T₁ , T₂ , and UTE images were scored according to the modified Ochiai scheme by three pediatric body radiologists. Ventilation/perfusion-weighted maps were extracted from free-breathing GRE images using PREFUL analysis. Ventilation and perfusion defect percent (VDP, QDP) were calculated from the segmented ventilation and perfusion-weighted maps. Time-averaged cardiac blood velocities from three-dimensional-flow were evaluated in major pulmonary arteries and veins.

Intraclass correlation coefficient (ICC).

The ICC of replicate structural scores was 0.81 (95% CI: 0.45-0.95) across three observers. Elevated Ochiai scores, VDP, and QDP were observed in two NICU participants. Excluding these participants, mean ± standard deviation structural scores were 1.2 ± 0.8, while VDP and QDP were 1.0% ± 1.1% and 0.4% ± 0.5%, respectively. Main pulmonary arterial blood flows normalized to body surface area were 3.15 ± 0.78 L/min/m² .

Structural and functional pulmonary imaging is feasible using standard clinical MRI hardware (commercial whole-body 3 T scanner, table spine array, and flexible thoracic array) in free-breathing infants.

2 TECHNICAL EFFICACY: Stage 1.