A Comparison of Two Hyperpolarized 129Xe MRI Ventilation Quantification Pipelines: The Effect of Signal to Noise Ratio

Encoding Enhanced Complex CNN for Accurate and Highly Accelerated MRI

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

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.

Comparison of hyperpolarized 3He-MRI, CT based parametric response mapping, and mucus scores in asthmatics

Purpose: The purpose of this study was to anatomically correlate ventilation defects with regions of air trapping by whole lung, lung lobe, and airway segment in the context of airway mucus plugging in asthma. Methods: A total of 34 asthmatics [13M:21F, 13 mild/moderate, median age (range) of 49.5 (36.8-53.3) years and 21 severe, 56.1 (47.1-62.6) years] and 4 healthy subjects [1M:3F, 38.5 (26.6-52.2) years] underwent HP 3He MRI and CT imaging. HP 3He MRI was assessed for ventilation defects using a semi-automated k-means clustering algorithm. Inspiratory and expiratory CTs were analyzed using parametric response mapping (PRM) to quantify markers of emphysema and functional small airways disease (fSAD). Segmental and lobar lung masks were obtained from CT and registered to HP 3He MRI in order to localize ventilation defect percent (VDP), at the lobar and segmental level, to regions of fSAD and mucus plugging. Spearman's correlation was utilized to compare biomarkers on a global and lobar level, and a multivariate analysis was conducted to predict segmental fSAD given segmental VDP (sVDP) and mucus score as variables in order to further understand the functional relationships between regional measures of obstruction. Results: On a global level, fSAD was correlated with whole lung VDP (r = 0.65, p < 0.001), mucus score (r = 0.55, p < 0.01), and moderately correlated (-0.60 ≤ r ≤ -0.56, p < 0.001) to percent predicted (%p) FEV1, FEF25-75 and FEV1/FVC, and more weakly correlated to FVC%p (-0.38 ≤ r ≤ -0.35, p < 0.001) as expected from previous work. On a regional level, lobar VDP, mucus scores, and fSAD were also moderately correlated (r from 0.45-0.66, p < 0.01). For segmental colocalization, the model of best fit was a piecewise quadratic model, which suggests that sVDP may be increasing due to local airway obstruction that does not manifest as fSAD until more extensive disease is present. sVDP was more sensitive to the presence of a mucus plugs overall, but the prediction of fSAD using multivariate regression showed an interaction in the presence of a mucus plugs when sVDP was between 4% and 10% (p < 0.001). Conclusion: This multi-modality study in asthma confirmed that areas of ventilation defects are spatially correlated with air trapping at the level of the airway segment and suggests VDP and fSAD are sensitive to specific sources of airway obstruction in asthma, including mucus plugs.

Diffusion weighted hyperpolarized 129 Xe MRI of the lung with 2D and 3D (FLORET) spiral

PURPOSE

To enable efficient hyperpolarized ¹²⁹ Xe diffusion imaging using 2D and 3D (Fermat Looped, ORthogonally Encoded Trajectories, FLORET) spiral sequences and demonstrate that ¹²⁹ Xe ADCs obtained using these sequences are comparable to those obtained using a conventional, 2D gradient-recalled echo (GRE) sequence.

THEORY AND METHODS

Diffusion-weighted ¹²⁹ Xe MRI (b-values = 0, 7.5, 15 s/cm² ) was performed in four healthy volunteers and one subject with lymphangioleiomyomatosis using slice-selective 2D-GRE (scan time = 15 s), slice-selective 2D-Spiral (4 s), and 3D-FLORET (16 s) sequences. Experimental SNRs from b-value = 0 images ( SNR 0 EX $$ SNR{0}_{EX} $$ ) and mean ADC values were compared across sequences. In two healthy subjects, a second b = 0 image was acquired using the 2D-Spiral sequence to map flip angle and correct RF-induced, hyperpolarized signal decay at the voxel level, thus improving regional ADC estimates.

RESULTS

Diffusion-weighted images from spiral sequences displayed image quality comparable to 2D-GRE and produced sufficient SNR 0 EX $$ SNR{0}_{EX} $$ (16.8 ± 3.8 for 2D-GRE, 21.2 ± 3.5 for 2D-Spiral, 20.4 ± 3.5 for FLORET) to accurately calculate ADC. Whole-lung means and SDs of ADC obtained via spiral were not significantly different (P > 0.54) from those obtained via 2D-GRE. Finally, 2D-Spiral images were corrected for signal decay, which resulted in a whole-lung mean ADC decrease of ˜15%, relative to uncorrected images.

CONCLUSIONS

Relative to GRE, efficient spiral sequences allow ¹²⁹ Xe diffusion images to be acquired with isotropic lung coverage (3D), higher SNR $$ SNR $$ (2D and 3D), and three-fold faster (2D) within a single breath-hold. In turn, shortened breath-holds enable flip-angle mapping, and thus, allow RF-induced signal decay to be corrected, increasing ADC accuracy.

A 3D stack-of-spirals approach for rapid hyperpolarized 129 Xe ventilation mapping in pediatric cystic fibrosis lung disease

PURPOSE

To demonstrate the feasibility of a rapid 3D stack-of-spirals (3D-SoS) imaging acquisition for hyperpolarized ¹²⁹ Xe ventilation mapping in healthy pediatric participants and pediatric cystic fibrosis (CF) participants, in comparison to conventional Cartesian multislice (2D) gradient-recalled echo (GRE) imaging.

METHODS

The 2D-GRE and 3D-SoS acquisitions were performed in 13 pediatric participants (5 healthy, 8 CF) during separate breath-holds. Images from both sequences were compared on the basis of ventilation defect percent (VDP) and other measures of image similarity. The nadir of transient oxygen saturation (SpO₂ ) decline due to xenon breath-holding was measured with pulse oximetry, and expressed as a percent change relative to baseline.

RESULTS

¹²⁹ Xe ventilation images were acquired in a breath-hold of 1.2-1.8 s with the 3D-SoS sequence, compared to 6.2-8.8 s for 2D-GRE. Mean ± SD VDP measures for 2D-GRE and 3D-SoS sequences were 5.02 ± 1.06% and 5.28 ± 1.08% in healthy participants, and 18.05 ± 8.26% and 18.75 ± 6.74% in CF participants, respectively. Across all participants, the intraclass correlation coefficient of VDP measures for both sequences was 0.98 (95% confidence interval: 0.94-0.99). The percent change in SpO₂ was reduced to -2.1 ± 2.7% from -5.2 ± 3.5% with the shorter 3D-SoS breath-hold.

CONCLUSION

Hyperpolarized ¹²⁹ Xe ventilation imaging with 3D-SoS yielded images approximately five times faster than conventional 2D-GRE, reducing SpO₂ desaturation and improving tolerability of the xenon administration. Analysis of VDP and other measures of image similarity demonstrate excellent agreement between images obtained with both sequences. 3D-SoS holds significant potential for reducing the acquisition time of hyperpolarized ¹²⁹ Xe MRI, and/or increasing spatial resolution while adhering to clinical breath-hold constraints.

Quantitative Imaging Metrics for the Assessment of Pulmonary Pathophysiology: An Official American Thoracic Society and Fleischner Society Joint Workshop Report

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.

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

PURPOSE

To compare imaging biomarkers from hyperpolarised ¹²⁹Xe ventilation MRI and dynamic oxygen-enhanced MRI (OE-MRI) with standard pulmonary function tests (PFT) in interstitial lung disease (ILD) patients. To evaluate if biomarkers can separate ILD subtypes and detect early signs of disease resolution or progression.

STUDY TYPE

Prospective longitudinal.

POPULATION

Forty-one ILD (fourteen idiopathic pulmonary fibrosis (IPF), eleven hypersensitivity pneumonitis (HP), eleven drug-induced ILD (DI-ILD), five connective tissue disease related-ILD (CTD-ILD)) patients and ten healthy volunteers imaged at visit 1. Thirty-four ILD patients completed visit 2 (eleven IPF, eight HP, ten DIILD, five CTD-ILD) after 6 or 26 weeks.

FIELD STRENGTH/SEQUENCE

MRI was performed at 1.5 T, including inversion recovery T₁ mapping, dynamic MRI acquisition with varying oxygen levels, and hyperpolarised ¹²⁹Xe ventilation MRI. Subjects underwent standard spirometry and gas transfer testing.

ASSESSMENT

Five ¹H MRI and two ¹²⁹Xe MRI ventilation metrics were compared with spirometry and gas transfer measurements.

STATISTICAL TEST

To evaluate differences at visit 1 among subgroups: ANOVA or Kruskal-Wallis rank tests with correction for multiple comparisons. To assess the relationships between imaging biomarkers, PFT, age and gender, at visit 1 and for the change between visit 1 and 2: Pearson correlations and multilinear regression models.

RESULTS

The global PFT tests could not distinguish ILD subtypes. Percentage ventilated volumes were lower in ILD patients than in HVs when measured with ¹²⁹Xe MRI (HV 97.4 ± 2.6, CTD-ILD: 91.0 ± 4.8 p = 0.017, DI-ILD 90.1 ± 7.4 p = 0.003, HP 92.6 ± 4.0 p = 0.013, IPF 88.1 ± 6.5 p < 0.001), but not with OE-MRI. ¹²⁹Xe reported more heterogeneous ventilation in DI-ILD and IPF than in HV, and OE-MRI reported more heterogeneous ventilation in DI-ILD and IPF than in HP or CTD-ILD. The longitudinal changes reported by the imaging biomarkers did not correlate with the PFT changes between visits.

DATA CONCLUSION

Neither ¹²⁹Xe ventilation nor OE-MRI biomarkers investigated in this study were able to differentiate between ILD subtypes, suggesting that ventilation-only biomarkers are not indicated for this task. Limited but progressive loss of ventilated volume as measured by ¹²⁹Xe-MRI may be present as the biomarker of focal disease progresses. OE-MRI biomarkers are feasible in ILD patients and do not correlate strongly with PFT. Both OE-MRI and ¹²⁹Xe MRI revealed more spatially heterogeneous ventilation in DI-ILD and IPF.

Bias field correction in hyperpolarized 129 Xe ventilation MRI using templates derived by RF-depolarization mapping

PURPOSE

To correct for RF inhomogeneity for in vivo 129 Xe ventilation MRI using flip-angle mapping enabled by randomized 3D radial acquisitions. To extend this RF-depolarization mapping approach to create a flip-angle map template applicable to arbitrary acquisition strategies, and to compare these approaches to conventional bias field correction.

METHODS

RF-depolarization mapping was evaluated first in digital simulations and then in 51 subjects who had undergone radial 129 Xe ventilation MRI in the supine position at 3T (views = 3600; samples/view = 128; TR/TE = 4.5/0.45 ms; flip angle = 1.5; FOV = 40 cm). The images were corrected using newly developed RF-depolarization and templated-based methods and the resulting quantitative ventilation metrics (mean, coefficient of variation, and gradient) were compared to those resulting from N4ITK correction.

RESULTS

RF-depolarization and template-based mapping methods yielded a pattern of RF-inhomogeneity consistent with the expected variation based on coil architecture. The resulting corrected images were visually similar, but meaningfully distinct from those generated using standard N4ITK correction. The N4ITK algorithm eliminated the physiologically expected anterior-posterior gradient (-0.04 ± 1.56%/cm, P < 0.001). These 2 newly introduced methods of RF-depolarization and template correction retained the physiologically expected anterior-posterior ventilation gradient in healthy subjects (2.77 ± 2.09%/cm and 2.01 ± 2.73%/cm, respectively).

CONCLUSIONS

Randomized 3D 129 Xe MRI ventilation acquisitions can inherently be corrected for bias field, and this technique can be extended to create flip angle templates capable of correcting images from a given coil regardless of acquisition strategy. These methods may be more favorable than the de facto standard N4ITK because they can remove undesirable heterogeneity caused by RF effects while retaining results from known physiology.

Hyperpolarized 129Xenon MRI Ventilation Defect Quantification via Thresholding and Linear Binning in Multiple Pulmonary Diseases

RATIONALE

There is no agreed upon method for quantifying ventilation defect percentage (VDP) with high sensitivity and specificity from hyperpolarized (HP) gas ventilation MR images in multiple pulmonary diseases for both pediatrics and adults, yet identifying such methods will be necessary for future multi-site trials. Most HP gas MRI ventilation research focuses on a specific pulmonary disease and utilizes one quantification scheme for determining VDP. Here we sought to determine the potential of different methods for quantifying VDP from HP 129Xe images in multiple pulmonary diseases through comparison of the most utilized quantification schemes: linear binning and thresholding.

MATERIALS AND METHODS

HP 129Xe MRI was performed in a total of 176 subjects (125 pediatrics and 51 adults, age 20.98±16.48 years) who were either healthy controls (n = 23) or clinically diagnosed with cystic fibrosis (CF) (n = 37), lymphangioleiomyomatosis (LAM) (n = 29), asthma (n = 22), systemic juvenile idiopathic arthritis (sJIA) (n = 11), interstitial lung disease (ILD) (n = 7), or were bone marrow transplant (BMT) recipients (n = 47). HP 129Xe ventilation images were acquired during a ≤16 second breath-hold using a 2D multi-slice gradient echo sequence on a 3T Philips scanner (TR/TE 8.0/4.0ms, FA 10-12°, FOV 300 × 300mm, voxel size≈3 × 3 × 15mm). Images were analyzed using 5 different methods to quantify VDPs: linear binning (histogram normalization with binning into 6 clusters) following either linear or a variant of a nonparametric nonuniform intensity normalization algorithm (N4ITK) bias-field correction, thresholding ≤60% of the mean signal intensity with linear bias-field correction, and thresholding ≤60% and ≤75% of the mean signal intensity following N4ITK bias-field correction. Spirometry was successfully obtained in 84% of subjects.

RESULTS

All quantification schemes were able to label visually identifiable ventilation defects in similar regions within all subjects. The VDPs of control subjects were significantly lower (p<0.05) compared to BMT, CF, LAM, and ILD subjects for most of the quantification methods. No one quantification scheme was better able to differentiate individual disease groups from the control group. Advanced statistical modeling of the VDP quantification schemes revealed that in comparing controls to the combined disease group, N4ITK bias-field corrected 60% thresholding had the highest predictive efficacy, sensitivity, and specificity at the VDP cut-point of 2.3%. However, compared to the thresholding quantification schemes, linear binning was able to capture and label subtle low-ventilation regions in subjects with milder obstruction, such as subjects with asthma.

CONCLUSION

The difference in VDP between healthy controls and patients varied between the different disease states for all quantification methods. Although N4ITK bias-field corrected 60% thresholding was superior in separating the combined diseased group from controls, linear binning is able to better label low-ventilation regions unlike the current, 60% thresholding scheme. For future clinical trials, a consensus will need to be reached on which VDP scheme to utilize, as there are subtle advantages for each for specific disease.

Comparison of Hyperpolarized 3He and 129Xe MR Imaging in Cystic Fibrosis Patients

PURPOSE

In this study, we compared hyperpolarized ³He and ¹²⁹Xe images from patients with cystic fibrosis using two commonly applied magnetic resonance sequences, standard gradient echo (GRE) and balanced steady-state free precession (TrueFISP) to quantify regional similarities and differences in signal distribution and defect analysis.

MATERIALS AND METHODS

Ten patients (7M/3F) with cystic fibrosis underwent hyperpolarized gas MR imaging with both ³He and ¹²⁹Xe. Six had MRI with both GRE, and TrueFISP sequences and four patients had only GRE sequence but not TrueFISP. Ventilation defect percentages (VDPs) were calculated as lung voxels with <60% of the whole-lung hyperpolarized gas signal mean and was measured in all datasets. The voxel signal distributions of both ¹²⁹Xe and ³He gases were visualized and compared using violin plots. VDPs of hyperpolarized 3 He and 129 Xe were compared in Bland-Altman plots; Pearson correlation coefficients were used to evaluate the relationships between inter-gas and inter-scan to assess the reproducibility.

RESULTS

A significant correlation was demonstrated between ¹²⁹Xe VDP and ³He VDP for both GRE and TrueFISP sequences (ρ = 0.78, p<0.0004). The correlation between the GRE and TrueFISP VDP for ³He was ρ = 0.98 and was ρ = 0.91 for ¹²⁹Xe. Overall, ¹²⁹Xe (27.2±9.4) VDP was higher than ³He (24.3±6.9) VDP on average on cystic fibrosis patients.

CONCLUSION

In patients with cystic fibrosis, the selection of hyperpolarized ¹²⁹Xe or ³He gas is most likely inconsequential when it comes to measure the overall lung function by VDP although ¹²⁹Xe may be more sensitive to starker lung defects, particularly when using a TrueFISP sequence.

Image- versus histogram-based considerations in semantic segmentation of pulmonary hyperpolarized gas images

PURPOSE

To characterize the differences between histogram-based and image-based algorithms for segmentation of hyperpolarized gas lung images.

METHODS

Four previously published histogram-based segmentation algorithms (ie, linear binning, hierarchical k-means, fuzzy spatial c-means, and a Gaussian mixture model with a Markov random field prior) and an image-based convolutional neural network were used to segment 2 simulated data sets derived from a public (n = 29 subjects) and a retrospective collection (n = 51 subjects) of hyperpolarized 129Xe gas lung images transformed by common MRI artifacts (noise and nonlinear intensity distortion). The resulting ventilation-based segmentations were used to assess algorithmic performance and characterize optimization domain differences in terms of measurement bias and precision.

RESULTS

Although facilitating computational processing and providing discriminating clinically relevant measures of interest, histogram-based segmentation methods discard important contextual spatial information and are consequently less robust in terms of measurement precision in the presence of common MRI artifacts relative to the image-based convolutional neural network.

CONCLUSIONS

Direct optimization within the image domain using convolutional neural networks leverages spatial information, which mitigates problematic issues associated with histogram-based approaches and suggests a preferred future research direction. Further, the entire processing and evaluation framework, including the newly reported deep learning functionality, is available as open source through the well-known Advanced Normalization Tools ecosystem.

Early prediction of lung lesion progression in COVID-19 patients with extended CT ventilation imaging

PURPOSE

In the prediction of COVID-19 disease progression, a clear illustration and early determination of an area that will be affected by pneumonia remain great challenges. In this study, we aimed to predict and visualize the progression of lung lesions in COVID-19 patients in the early stage of illness by using chest CT.

METHODS

COVID-19 patients who underwent three chest CT scans in the progressive phase were retrospectively enrolled. An extended CT ventilation imaging (CTVI) method was proposed in this work that was adapted to use two chest CT scans acquired on different days, and then lung ventilation maps were generated. The prediction maps were obtained according to the fractional ventilation values, which were related to pulmonary regional function and tissue property changes. The third CT scan was used to validate whether the prediction maps could be used to distinguish healthy regions and potential lesions.

RESULTS

A total of 30 patients (mean age ± SD, 43 ± 10 years, 19 females, and 2-12 days between the second and third CT scans) were included in this study. The predicted lesion locations and sizes were almost the same as the true ones visualized in third CT scan. Quantitatively, the predicted lesion volumes and true lesion volumes showed both a good Pearson correlation (R² = 0.80; P < 0.001) and good consistency in the Bland-Altman plot (mean bias = 0.04 cm³). Regarding the enlargements of the existing lesions, prediction results also exhibited a good Pearson correlation (R² = 0.76; P < 0.001) with true lesion enlargements.

CONCLUSION

The present findings demonstrated that the extended CTVI method could accurately predict and visualize the progression of lung lesions in COVID-19 patients in the early stage of illness, which is helpful for physicians to predetermine the severity of COVID-19 pneumonia and make effective treatment plans in advance.

Reproducibility of Hyperpolarized 129Xe MRI Ventilation Defect Percent in Severe Asthma to Evaluate Clinical Trial Feasibility

RATIONALE AND OBJECTIVES

¹²⁹Xe MRI has been developed to noninvasively visualize and quantify the functional consequence of airway obstruction in asthma. Its widespread application requires evidence of intersite reproducibility and agreement. Our objective was to evaluate reproducibility and agreement of ¹²⁹Xe ventilation MRI measurements in severe asthmatics at two sites.

MATERIALS AND METHODS

In seven adults with severe asthma, ¹²⁹Xe ventilation MRI was acquired pre- and post-bronchodilator at two geographic sites within 24-hours. ¹²⁹Xe MRI signal-to-noise ratio (SNR) was calculated and ventilation abnormalities were quantified as the whole-lung and slice-by-slice ventilation defect percent (VDP). Intraclass correlation coefficients (ICC) and Bland-Altman analysis were used to determine intersite ¹²⁹Xe VDP reproducibility and agreement.

RESULTS

Whole-lung and slice-by-slice ¹²⁹Xe VDP measured at both sites were correlated and reproducible (pre-bronchodilator: whole-lung ICC = 0.90, p = 0.005, slice-by-slice ICC = 0.78, p < 0.0001; post-bronchodilator: whole-lung ICC = 0.94, p < 0.0001, slice-by-slice ICC = 0.83, p < 0.0001) notwithstanding intersite differences in the ¹²⁹Xe-dose-equivalent-volume (101 ± 15 mL site 1, 49 ± 6 mL site 2, p < 0.0001), gas-mixture (¹²⁹Xe/⁴He site 1; ¹²⁹Xe/N₂ site 2) and SNR (40 ± 19 site 1, 23 ± 5 site 2, p = 0.02). Qualitative ¹²⁹Xe gas distribution differences were observed between sites and slice-by-slice ¹²⁹Xe VDP, but not whole-lung ¹²⁹Xe VDP, was significantly lower at site 1 (pre-bronchodilator VDP: whole-lung bias = -3%, p > 0.99, slice-by-slice bias = -3%, p = 0.0001; post-bronchodilator VDP: whole-lung bias = -2%, p = 0.59, slice-by-slice-bias = -2%, p = 0.0003).

CONCLUSION

¹²⁹Xe MRI VDP at two different sites measured within 24-hours in the same severe asthmatics were correlated. Qualitative and quantitative intersite differences in ¹²⁹Xe regional gas distribution and VDP point to site-specific variability that may be due to differences in gas-mixture composition or SNR.

Regional Gas Exchange Measured by 129 Xe Magnetic Resonance Imaging Before and After Combination Bronchodilators Treatment in Chronic Obstructive Pulmonary Disease

Background

Hyperpolarized ¹²⁹Xe magnetic resonance imaging (MRI) provides a non‐invasive assessment of regional pulmonary gas exchange function. This technique has demonstrated that chronic obstructive pulmonary disease (COPD) patients exhibit ventilation defects, reduced interstitial barrier tissue uptake, and poor transfer to capillary red blood cells (RBCs). However, the behavior of these measurements following therapeutic intervention is unknown.

Purpose

To characterize changes in ¹²⁹Xe gas transfer function following administration of an inhaled long‐acting beta‐agonist/long‐acting muscarinic receptor antagonist (LABA/LAMA) bronchodilator.

Study Type

Prospective.

Population

Seventeen COPD subjects (GOLD II/III classification per Global Initiative for Chronic Obstructive Lung Disease criteria) were imaged before and after 2 weeks of LABA/LAMA therapy.

Field Strength/Sequences

Dedicated ventilation imaging used a multi‐slice 2D gradient echo sequence. Three‐dimensional images of ventilation, barrier uptake, and RBC transfer used an interleaved, radial, 1‐point Dixon sequence. Imaging was acquired at 3 T.

Assessment

¹²⁹Xe measurements were quantified before and after LABA/LAMA treatment by ventilation defect + low percent (ven_{def + low}) and by barrier uptake and RBC transfer relative to a healthy reference population (bar_%ref and RBC_%ref). Pulmonary function tests, including diffusing capacity of the lung for carbon monoxide (DL_CO), were also performed before and after treatment.

Statistical Tests

Paired t‐test, Pearson correlation coefficient (r).

Results

Baseline ven_{def + low} was 57.8 ± 8.4%, bar_%ref was 73.2 ± 19.6%, and RBC_%ref was 36.5 ± 13.6%. Following treatment, ven_{def + low} decreased to 52.5 ± 10.6% (P < 0.05), and improved in 14/17 (82.4%) of subjects. However, RBC_%ref decreased in 10/17 (58.8%) of subjects. Baseline measurements of bar_%ref and DL_CO were correlated with the degree of post‐treatment change in ven_{def + low} (r = −0.49, P < 0.05 and r = −0.52, P < 0.05, respectively).

Conclusion

LABA/LAMA therapy tended to preferentially improve ventilation in subjects whose ¹²⁹Xe barrier uptake and DL_CO were relatively preserved. However, newly ventilated regions often revealed RBC transfer defects, an aspect of lung function opaque to spirometry. These microvasculature abnormalities must be accounted for when assessing the effects of LABA/LAMA therapy.

Level of Evidence

1

Technical Efficacy Stage

4

In vivo methods and applications of xenon-129 magnetic resonance

Hyperpolarised gas lung MRI using xenon-129 can provide detailed 3D images of the ventilated lung airspaces, and can be applied to quantify lung microstructure and detailed aspects of lung function such as gas exchange. It is sensitive to functional and structural changes in early lung disease and can be used in longitudinal studies of disease progression and therapy response. The ability of ¹²⁹Xe to dissolve into the blood stream and its chemical shift sensitivity to its local environment allow monitoring of gas exchange in the lungs, perfusion of the brain and kidneys, and blood oxygenation. This article reviews the methods and applications of in vivo¹²⁹Xe MR in humans, with a focus on the physics of polarisation by optical pumping, radiofrequency coil and pulse sequence design, and the in vivo applications of ¹²⁹Xe MRI and MRS to examine lung ventilation, microstructure and gas exchange, blood oxygenation, and perfusion of the brain and kidneys.

Generalized Linear Binning to Compare Hyperpolarized 129Xe Ventilation Maps Derived from 3D Radial Gas Exchange Versus Dedicated Multislice Gradient Echo MRI

RATIONALE

Hyperpolarized ¹²⁹Xe ventilation MRI is typically acquired using multislice fast gradient recalled echo (GRE), but interleaved 3D radial ¹²⁹Xe gas transfer MRI now provides dissolved-phase and ventilation images from a single breath. To investigate whether these ventilation images provide equivalent quantitative metrics, we introduce generalized linear binning analysis.

METHODS

This study included 36 patients who had undergone both multislice GRE ventilation and 3D radial gas exchange imaging. Images were then quantified by linear binning to classify voxels into one of four clusters: ventilation defect percentage (VDP), Low-, Medium- or High-ventilation percentage (LVP, MVP, HVP). For 3D radial images, linear binning thresholds were generalized using a Box-Cox rescaled reference histogram. We compared the cluster populations from the two ventilation acquisitions both numerically and spatially.

RESULTS

Interacquisition Bland-Altman limits of agreement for the clusters between 3D radial vs GRE were (-7% to 5%) for VDP, (-10% to 14%) for LVP, and (-8% to 8%) for HVP. While binning maps were qualitatively similar between acquisitions, their spatial overlap was modest for VDP (Dice = 0.5 ± 0.2), and relatively poor for LVP (0.3 ± 0.1) and HVP (0.2 ± 0.1).

CONCLUSION

Both acquisitions yield reasonably concordant VDP and qualitatively similar maps. However, poor regional agreement (Dice) suggests that the two acquisitions cannot yet be used interchangeably. However, further improvements in 3D radial resolution and reconciliation of bias field correction may well obviate the need for a dedicated ventilation scan in many cases.

Improved pulmonary 129 Xe ventilation imaging via 3D-spiral UTE MRI

PURPOSE

Hyperpolarized ¹²⁹ Xe MRI characterizes regional lung ventilation in a variety of disease populations, with high sensitivity to airway obstruction in early disease. However, ventilation images are usually limited to a single breath-hold and most-often acquired using gradient-recalled echo sequences with thick slices (~10-15 mm), which increases partial-volume effects, limits ability to observe small defects, and suffers from imperfect slice selection. We demonstrate higher-resolution ventilation images, in shorter breath-holds, using FLORET (Fermat Looped ORthogonally Encoded Trajectories), a center-out 3D-spiral UTE sequence.

METHODS

In vivo human adult (N = 4; 2 healthy, 2 with cystic fibrosis) ¹²⁹ Xe images were acquired using 2D gradient-recalled echo, 3D radial, and FLORET. Each sequence was acquired at its highest possible resolution within a 16-second breath-hold with a minimum voxel dimension of 3 mm. Images were compared using ¹²⁹ Xe ventilation defect percentage, SNR, similarity coefficients, and vasculature cross-sections.

RESULTS

The FLORET sequence obtained relative normalized SNR, 40% greater than 2D gradient-recalled echo (P = .012) and 26% greater than 3D radial (P = .067). Moreover, the FLORET images were acquired with 3-fold-higher nominal resolution in a 15% shorter breath-hold. Finally, vasculature was less prominent in FLORET, likely due to diminished susceptibility-induced dephasing at shorter TEs afforded by UTE sequences.

CONCLUSION

The FLORET sequence yields higher SNR for a given resolution with a shorter breath-hold than traditional ventilation imaging techniques. This sequence more accurately measures ventilation abnormalities and enables reduced scan times in patients with poor compliance and severe lung disease.

Fast and accurate reconstruction of human lung gas MRI with deep learning

PURPOSE

To fast and accurately reconstruct human lung gas MRI from highly undersampled k-space using deep learning.

METHODS

The scheme was comprised of coarse-to-fine nets (C-net and F-net). Zero-filling images from retrospectively undersampled k-space at an acceleration factor of 4 were used as input for C-net, and then output intermediate results which were fed into F-net. During training, a L2 loss function was adopted in C-net, while a function that united L2 loss with proton prior knowledge was used in F-net. The 871 hyperpolarized ¹²⁹ Xe pulmonary ventilation images from 72 volunteers were randomly arranged as training (90%) and testing (10%) data. Ventilation defect percentage comparisons were implemented using a paired 2-tailed Student's t-test and correlation analysis. Furthermore, prospective acquisitions were demonstrated in 5 healthy subjects and 5 asymptomatic smokers.

RESULTS

Each image with size of 96 × 84 could be reconstructed within 31 ms (mean absolute error was 4.35% and structural similarity was 0.7558). Compared with conventional compressed sensing MRI, the mean absolute error decreased by 17.92%, but the structural similarity increased by 6.33%. For ventilation defect percentage, there were no significant differences between the fully sampled and reconstructed images through the proposed algorithm (P = 0.932), but had significant correlations (r = 0.975; P < 0.001). The prospectively undersampled results validated a good agreement with fully sampled images, with no significant differences in ventilation defect percentage but significantly higher signal-to-noise ratio values.

CONCLUSION

The proposed algorithm outperformed classical undersampling methods, paving the way for future use of deep learning in real-time and accurate reconstruction of gas MRI.