Complementation-reinforced network for integrated reconstruction and segmentation of pulmonary gas MRI with high acceleration.

BACKGROUND: 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. PURPOSE: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.

Deep Learning Approaches for Quantifying Ventilation Defects in Hyperpolarized Gas Magnetic Resonance Imaging of the Lung: A Review.

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.

Chest MRI and CT Predictors of 10-Year All-Cause Mortality in COPD.

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 argument for utilising magnetic resonance imaging as a tool for monitoring lung structure and function in pediatric patients.

INTRODUCTION: Although historically challenging to perform in the lung, technological advancements have made Magnetic Resonance Imaging (MRI) increasingly applicable for pediatric pulmonary imaging. Furthermore, a wide array of functional imaging techniques has become available that may be leveraged alongside structural imaging for increasingly sensitive biomarkers, or as outcome measures in the evaluation of novel therapies. AREAS COVERED: In this review, recent technical advancements and modern methodologies for structural and functional lung MRI are described. These include ultrashort echo time (UTE) MRI, free-breathing contrast agent-free, functional lung MRI, and hyperpolarized gas MRI, amongst other techniques. Specific examples of the application of these methods in children are provided, principally drawn from recent research in asthma, bronchopulmonary dysplasia, and cystic fibrosis. EXPERT OPINION: Pediatric lung MRI is rapidly growing, and is well poised for clinical utilization, as well as continued research into early disease detection, disease processes, and novel treatments. Structure/function complementarity makes MRI especially attractive as a tool for increased adoption in the evaluation of pediatric lung disease. Looking toward the future, novel technologies, such as low-field MRI and artificial intelligence, mitigate some of the traditional drawbacks of lung MRI and will aid in improving access to MRI in general, potentially spurring increased adoption and demand for pulmonary MRI in children.

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.

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.

Quantification of pulmonary functional MRI: state-of-the-art and emerging image processing methods and measurements.

Pulmonary functional magnetic resonance imaging (PfMRI) provides a way to non-invasively map and measure the spatial distribution of pulmonary ventilation, perfusion and gas-exchange abnormalities with unprecedented detail of functional processes at the level of airways, alveoli and the alveolar-capillary membrane. Current PfMRI approaches are dominated by hyperpolarized helium-3 (3He) and xenon-129 (129Xe) gases, which both provide rapid (8-15 s) and well-tolerated imaging examinations in patients with severe pulmonary diseases and pediatric populations, whilst employing no ionizing radiation. While a number of review papers summarize the required image acquisition hardware and software requirements needed to enable PfMRI, here we focus on the image analysis and processing methods required for reproducible measurements using hyperpolarized gas ventilation MRI. We start with the transition in the literature from qualitative and subjective scoring systems to quantitative and objective measurements which enable precise quantification of the lung's critical structure-function relationship. We provide an overview of quantitative biomarkers and the relevant respiratory system parameters that may be measured using PfMRI methods, outlining the history of developments in the field, current methods and then knowledge gaps and typical limitations. We focus on hyperpolarized noble gas MR image processing methods used for quantifying ventilation and gas distribution in the lungs, and discuss the utility and applications of imaging biomarkers generated through these techniques. We conclude with a summary of the current and future directions to further the development of image processing methods, and discuss the remaining challenges for potential clinical translation of these approaches and their integration into standard clinical workflows.

Ventilation heterogeneity imaged by multibreath wash-ins of hyperpolarized 3 He and 129 Xe in healthy rabbits.

KEY POINTS: Multibreath imaging to estimate regional gas mixing efficiency is superior to intensity-based single-breath ventilation markers, as it is capable of revealing minute but essential measures of ventilation heterogeneity which may be sensitive to subclinical alterations in the early stages of both obstructive and restrictive respiratory disorders. Large-scale convective stratification of ventilation in central-to-peripheral directions is the dominant feature of observed ventilation heterogeneity when imaging a heavy/less diffusive xenon gas mixture; smaller-scale patchiness, probably originating from asymmetric lung function at bronchial airway branching due to the interaction of convective and diffusive flows, is the dominant feature when imaging a lighter/more diffusive helium gas mixture. Since detecting low regional ventilation is crucial for characterizing diseased lungs, our results suggest that dilution with natural abundance helium and imaging at higher lung volumes seem advisable when imaging with hyperpolarized 129 Xe; this will allow the imaging gas to reach slow-filling and/or non-dependent lung regions, which might otherwise be impossible to distinguish from total ventilation shunt regions. The ability to differentiate these regions from those of total shunt is worse with typical single-breath imaging techniques. ABSTRACT: The mixing of freshly inhaled gas with gas already present in the lung can be directly assessed with heretofore unachievable precision via magnetic resonance imaging of signal build-up resulting from multiple wash-ins of a hyperpolarized (HP) gas. Here, we used normoxic HP 3 He and 129 Xe mixtures to study regional ventilation at different spatial scales in five healthy mechanically ventilated supine rabbits at two different inspired volumes. To decouple the respective effects of density and diffusion rates on ventilation heterogeneity, two additional studies were performed: one in which 3 He was diluted with an equal fraction of natural abundance xenon, and one in which 129 Xe was diluted with an equal fraction of 4 He. We observed systematic differences in the spatial scale of specific ventilation heterogeneity between HP 3 He and 129 Xe. We found that large-scale, central-to-peripheral convective ventilation inhomogeneity is the dominant cause of observed heterogeneity when breathing a normoxic xenon gas mixture. In contrast, small-scale ventilation heterogeneity in the form of patchiness, probably originating from asymmetric lung function at bronchial airway branching due to interactions between convective and diffusive flows, is the dominant feature when breathing a normoxic helium gas mixture, for which the critical zone occurs more proximally and at an imageable spatial scale. We also showed that the existence of particular underventilated non-dependent lung regions when breathing a heavy gas mixture is the result of the density of that mixture - rather than, for example, its diffusion rate or viscosity. Finally, we showed that gravity-dependent ventilation heterogeneity becomes substantially more uniform at higher inspired volumes for xenon gas mixtures compared to helium mixtures.

Peripheral and proximal lung ventilation in asthma: Short-term variation and response to bronchodilator inhalation.

BACKGROUND: The relative involvement of the large and small airways in asthma is not clear. Hyperpolarized gas magnetic resonance imaging (MRI) provides high-resolution 3-dimensional images of ventilation distribution that can be quantified by the ventilated volume percentage (VV%) of the lungs. OBJECTIVE: Our aims were to (1) quantify the baseline reproducibility of VV%, (2) assess the ventilation distribution between the proximal and peripheral lungs, and (3) investigate regional ventilation response to bronchodilator inhalation in a cohort of patients with asthma. METHODS: A total of 33 patients with poorly controlled, moderate-to-severe asthma were scanned with hyperpolarized 3He MRI. Two image data sets were acquired at baseline, and 1 image data set was acquired after bronchodilator inhalation. Images were divided into proximal and peripheral regions for analysis. RESULTS: Bland-Altman analysis showed strong reproducibility of VV% (bias = 0.12%; LOA = -1.86% to 2.10%). VV% variation at baseline was greater in the periphery than in the proximal lung. The proximal lung was better ventilated than the peripheral lung. Ventilation increased significantly in response to bronchodilator inhalation, globally and regionally, and the ventilation increase in response to bronchodilator inhalation was greater in the peripheral lung than in the proximal lung. Hyperpolarized gas MRI was more sensitive to changes in response to bronchodilator inhalation (58%) than spirometry (33%). CONCLUSION: The peripheral lung showed reduced ventilation and a greater response to bronchodilator inhalation than the proximal lung. The high level of baseline reproducibility and sensitivity of hyperpolarized gas MRI to bronchodilator reversibility suggests that it is suitable for low subject number studies of therapy response.

Inhaled Xenon Washout as a Biomarker of Alzheimer's Disease.

Biomarkers have the potential to aid in the study of Alzheimer’s disease (AD); unfortunately, AD biomarker values often have a high degree of overlap between healthy and AD individuals. This study investigates the potential utility of a series of novel AD biomarkers, the sixty second 129Xe retention time, and the xenon washout parameter, based on the washout of hyperpolarized 129Xe from the brain of AD participants following inhalation. The xenon washout parameter is influenced by cerebral perfusion, T1 relaxation of xenon, and the xenon partition coefficient, all factors influenced by AD. Participants with AD (n = 4) and healthy volunteers (n = 4) were imaged using hyperpolarized 129Xe magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) to determine the amount of retained xenon in the brain. At 60 s after the breath hold, AD patients retained significantly higher amounts of 129Xe compared to healthy controls. Data was fit to a pharmacokinetic model and the xenon washout parameter was extracted. Xenon washout in white and grey matter occurs at a slower rate in Alzheimer’s participants (129Xe half-life time of 42 s and 43 s, respectively) relative to controls (20 s and 16 s, respectively). Following larger scale clinical trials for validation, the xenon washout parameter has the potential to become a useful biomarker for the support of AD diagnosis.

Mild loss of lung aeration augments stretch in healthy lung regions.

Inspiratory stretch by mechanical ventilation worsens lung injury. However, it is not clear whether and how the ventilator damages lungs in the absence of preexisting injury. We hypothesized that subtle loss of lung aeration during general anesthesia regionally augments ventilation and distension of ventilated air spaces. In eight supine anesthetized and intubated rats, hyperpolarized gas MRI was performed after a recruitment maneuver following 1 h of volume-controlled ventilation with zero positive end-expiratory pressure (ZEEP), FiO2 0.5, and tidal volume 10 ml/kg, and after a second recruitment maneuver. Regional fractional ventilation (FV), apparent diffusion coefficient (ADC) of (3)He (a measurement of ventilated peripheral air space dimensions), and gas volume were measured in lung quadrants of ventral and dorsal regions of the lungs. In six additional rats, computed tomography (CT) images were obtained at each time point. Ventilation with ZEEP decreased total lung gas volume and increased both FV and ADC in all studied regions. Increases in FV were more evident in the dorsal slices. In each lung quadrant, higher ADC was predicted by lower gas volume and by increased mean values (and heterogeneity) of FV distribution. CT scans documented 10% loss of whole-lung aeration and increased density in the dorsal lung, but no macroscopic atelectasis. Loss of pulmonary gas at ZEEP increased fractional ventilation and inspiratory dimensions of ventilated peripheral air spaces. Such regional changes could help explain a propensity for mechanical ventilation to contribute to lung injury in previously uninjured lungs.

Redistribution of inhaled hyperpolarized 3He gas during breath-hold differs by asthma severity.

The purpose of this work was to quantify the redistribution of ventilation-weighted signal in the lungs of asthmatic subjects during a breath-hold using high temporal-spatial resolution hyperpolarized (HP) He-3 MRI. HP He-3 MRI was used to obtain time-resolved, volumetric images of lung ventilation during breath-hold in 39 human subjects classified as either healthy/nondiseased (n = 14), mild-to-moderate asthmatic (n = 17), or severely asthmatic (n = 8). Signals were normalized to a standard lung volume, so that voxels within the lung from all 39 subjects could be analyzed as a group to increase statistical power and enable semiautomated classification of voxels into 1 of 5 ventilation level categories (ranging from defect to hyperintense). End-inspiratory ventilation distribution and temporal rates of mean signal change for each of the five ventilation categories were compared using ANOVA. Time rates of signal change were hypothesized to represent underlying gas redistribution processes, potentially influenced by disease. We found that mild-to-moderate asthmatic subjects showed the greatest rate of signal change, even though those with severe asthma had the greatest end-inspiration ventilation heterogeneity. The observed results support the existence of local differences in airway resistances associated with the different obstructive patterns in the lungs for severe vs. mild-to-moderate asthmatic subjects.

Ventilation heterogeneity and the benefits and challenges of multiple breath washout testing in patients with cystic fibrosis.

Multiple breath inert gas washout tests offer a simple and non-invasive method of measuring the effects of peripheral airway disease on ventilation efficiency, and have been the subject of intense research and commercial interest in the last decade. Although highly sensitive, these tests are not specific to disease in the small airways and are not suited to all patients with cystic fibrosis. They are harder to perform in subjects with more severe lung disease, with longer test time and poorer reproducibility in this population. The clinical advantages are more obvious in those with milder airways disease, where they are more sensitive to early changes than spirometry. In this population they are also quicker to complete and reproducible. A particular advantage in children is a stable range of normal which does not change during adolescence. Different inert gases give different washout results, and the technology to measure MBW is itself still evolving. Newer technologies such as hyperpolarised gas MRI are now helping us to understand how disease-related changes in lung ventilation influences inert gas washout.

In vivo lung morphometry with accelerated hyperpolarized (3) He diffusion MRI: a preliminary study.

PURPOSE: Parallel imaging can be used to reduce imaging time and to increase the spatial coverage in hyperpolarized gas magnetic resonance imaging of the lung. In this proof-of-concept study, we investigate the effects of parallel imaging on the morphometric measurement of lung microstructure using diffusion magnetic resonance imaging with hyperpolarized (3) He. METHODS: Fully sampled and under-sampled multi-b diffusion data were acquired from human subjects using an 8-channel (3) He receive coil. A parallel imaging reconstruction technique (generalized autocalibrating partially parallel acquisitions [GRAPPA]) was used to reconstruct under-sampled k-space data. The morphometric results of the generalized autocalibrating partially parallel acquisitions-reconstructed data were compared with the results of fully sampled data for three types of subjects: healthy volunteers, mild, and moderate chronic obstructive pulmonary disease patients. RESULTS: Morphometric measurements varied only slightly at mild acceleration factors. The results were largely well preserved compared to fully sampled data for different lung conditions. CONCLUSION: Parallel imaging, given sufficient signal-to-noise ratio, provides a reliable means to accelerate hyperpolarized-gas magnetic resonance imaging with no significant difference in the measurement of lung morphometry from the fully sampled images. GRAPPA is a promising technique to significantly reduce imaging time and/or to improve the spatial coverage for the morphometric measurement with hyperpolarized gases.

Assessment of repeatability of hyperpolarized gas MR ventilation functional imaging in cystic fibrosis.

RATIONALE AND OBJECTIVES: Hyperpolarized (HP) gas magnetic resonance imaging (MRI) is an advanced imaging technique that provides high-resolution regional information on lung function without using ionizing radiation. Before this modality can be considered for assessing clinical or investigational interventions, baseline repeatability needs to be established. We assessed repeatability of lung function measurement using HP helium-3 MRI (HP (3)He MRI) in a small cohort of patients with cystic fibrosis (CF). MATERIALS AND METHODS: We examined repeatability of HP (3)He MR images of five patients with CF in four scanning sessions over a 4-week period. We acquired images on a Philips 3.0 Tesla Achieva MRI scanner using a quadrature, flexible, wrap-around, (3)He radiofrequency coil with a fast gradient-echo pulse sequence. We determined ventilation volume and ventilation defect volume using an advanced semiautomatic segmentation algorithm and also quantified ventilation heterogeneity. RESULTS: There were no significant differences in total ventilation volume, ventilation defect volume, ventilation defect percentage, or mean ventilation heterogeneity (repeated-measures analysis of variance, P = .2116, P = .2825, P = .2871, and P = .7265, respectively) in the patients across the four scanning sessions. CONCLUSIONS: Our results indicate that total ventilation volume, ventilation defect volume, ventilation defect percentage, and mean ventilation heterogeneity as assessed by HP gas MRI in CF patients with stable health are reproducible over time. This repeatability and the technique's capability to provide noninvasive high-resolution data on regional lung function without ionizing radiation make (3)He MRI a potentially useful outcome measure for CF-related clinical trials.

Inert fluorinated gas MRI: a new pulmonary imaging modality.

Fluorine-19 ((19)F) MRI of the lungs using inhaled inert fluorinated gases can potentially provide high quality images of the lungs that are similar in quality to those from hyperpolarized (HP) noble gas MRI. Inert fluorinated gases have the advantages of being nontoxic, abundant, and inexpensive compared with HP gases. Due to the high gyromagnetic ratio of (19)F, there is sufficient thermally polarized signal for imaging, and averaging within a single breath-hold is possible due to short longitudinal relaxation times. Therefore, the gases do not need to be hyperpolarized prior to their use in MRI. This eliminates the need for an expensive polarizer and expensive isotopes. Inert fluorinated gas MRI of the lungs has been previously demonstrated in animals, and more recently in healthy volunteers and patients with lung diseases. The ongoing improvements in image quality demonstrate the potential of (19)F MRI for visualizing the distribution of ventilation in human lungs and detecting functional biomarkers. In this brief review, the development of inert fluorinated gas MRI, current progress, and future prospects are discussed. The current state of HP noble gas MRI is also briefly discussed in order to provide context to the development of this new imaging modality. Overall, this may be a viable clinical imaging modality that can provide useful information for the diagnosis and management of chronic respiratory diseases.

Hyperpolarized (3)He diffusion MRI and histology of secreted frizzled related protein-1 (SFRP1) deficient lungs in a Murine model.

Secreted frizzled related protein-1 (SFRP1) plays a key role in many diverse processes, including embryogenesis, tissue repair, bone formation, and tumor genesis. Previous studies have shown the effects of the SFRP1 gene on lung development using the SFRP1 knockout mouse model via histological and physiological studies. In this study, the feasibility of ADC (acquired via HP (3)He) to detect altered lung structure in the SFRP1 knockout (SFRP1(-/-)) mice was investigated, and compared to analysis by histology. This study consisted of two groups, the wild-type (WT) mice and the knockout (KO) mice with n=6 mice for each group. (3)He ADC MRI and histology were performed on all of the animals. The global Lm values of WT and KO mice were 35.0±0.8µm and 38.4±3.8µm, respectively, which translated to an increase of 9.58% in the Lm of KO mice. The mean global ADCs for the WT and KO mice were 0.12±0.01cm(2)/s and 0.13±0.01cm(2)/s, respectively, which equated to a relative increase of 8.0% in the KO mice compared to the WT mice. In the sub-analysis of the anterior, medial and posterior lung regions, Lm increased by 10.50%, 6.66% and 11.84% in the KO mice, respectively, whereas the differences in ADC between the two groups in the anterior, medial, and posterior regions were 7.3%, 8.3%, and 4.6%, respectively. These results suggest that HP MRI measurements can be used as a suitable substitute for histology to obtain valuable information about lung geometry non-invasively. This technique is also advantageous as regional measurements can be performed, which can identify lung destruction more precisely. Most importantly, this approach extends far beyond the specific pathology analyzed in this study, as it can be applied to many other pathological conditions in the lung tissue, as well to many other embryonic studies.

Accelerated fractional ventilation imaging with hyperpolarized Gas MRI.

PURPOSE: To investigate the utility of accelerated imaging to enhance multibreath fractional ventilation (r) measurement accuracy using hyperpolarized gas MRI. Undersampling shortens the breath-hold time, thereby reducing the O2 -induced signal decay and allows subjects to maintain a more physiologically relevant breathing pattern. Additionally, it may improve r estimation accuracy by reducing radiofrequency destruction of hyperpolarized gas. METHODS: Image acceleration was achieved using an eight-channel phased array coil. Undersampled image acquisition was simulated in a series of ventilation images and data was reconstructed for various matrix sizes (48-128) using generalized auto-calibrating partially parallel acquisition. Parallel accelerated r imaging was also performed on five mechanically ventilated pigs. RESULTS: Optimal acceleration factor was fairly invariable (2.0-2.2×) over the range of simulated resolutions. Estimation accuracy progressively improved with higher resolutions (39-51% error reduction). In vivo r values were not significantly different between the two methods: 0.27 ± 0.09, 0.35 ± 0.06, 0.40 ± 0.04 (standard) versus 0.23 ± 0.05, 0.34 ± 0.03, 0.37 ± 0.02 (accelerated); for anterior, medial, and posterior slices, respectively, whereas the corresponding vertical r gradients were significant (P < 0.001): 0.021 ± 0.007 (standard) versus 0.019 ± 0.005 (accelerated) (cm(-1) ). CONCLUSION: Quadruple phased array coil simulations resulted in an optimal acceleration factor of ∼2× independent of imaging resolution. Results advocate undersampled image acceleration to improve accuracy of fractional ventilation measurement with hyperpolarized gas MRI.