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

129Xe Image Processing Pipeline: An open-source, graphical user interface application for the analysis of hyperpolarized 129Xe MRI

PURPOSE

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.

METHODS

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.

RESULTS

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

CONCLUSION

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.

Ultrasensitive 129Xe Magnetic Resonance Imaging: From Clinical Monitoring to Molecular Sensing

Magnetic resonance imaging (MRI) is a cornerstone technology in clinical diagnostics and in vivo research, offering unparalleled visualization capabilities. Despite significant advancements in the past century, traditional 1H MRI still faces sensitivity limitations that hinder its further development. To overcome this challenge, hyperpolarization methods have been introduced, disrupting the thermal equilibrium of nuclear spins and leading to an increased proportion of hyperpolarized spins, thereby enhancing sensitivity by hundreds to tens of thousands of times. Among these methods, hyperpolarized (HP) 129Xe MRI, also known as ultrasensitive 129Xe MRI, stands out for achieving the highest polarization enhancement and has recently received clinical approval. It effectively tackles the challenge of weak MRI signals from low proton density in the lungs. HP 129Xe MRI is valuable for assessing structural and functional changes in lung physiology during pulmonary disease progression, tracking cells, and detecting target molecules at pico-molar concentrations. This review summarizes recent developments in HP 129Xe MRI, including its physical principles, manufacturing methods, in vivo characteristics, and diverse applications in biomedical, chemical, and material sciences. In addition, it carefully discusses potential technical improvements and future prospects for enhancing its utility in these fields, further establishing HP 129Xe MRI's importance in advancing medical imaging and research.

Improved Diffusion-Weighted Hyperpolarized 129Xe Lung MRI with Patch-Based Higher-Order, Singular Value Decomposition Denoising

RATIONALE AND OBJECTIVES

Hyperpolarized xenon (129Xe) MRI is a noninvasive method to assess pulmonary structure and function. To measure lung microstructure, diffusion-weighted imaging-commonly the apparent diffusion coefficient (ADC)-can be employed to map changes in alveolar-airspace size resulting from normal aging and pulmonary disease. However, low signal-to-noise ratio (SNR) decreases ADC measurement certainty, and biases ADC to spuriously low values. Further, these challenges are most severe in regions of the lung where alveolar simplification or emphysematous remodeling generate abnormally high ADCs. Here, we apply Global Local Higher Order Singular Value Decomposition (GLHOSVD) denoising to enhance image SNR, thereby reducing uncertainty and bias in diffusion measurements.

MATERIALS AND METHODS

GLHOSVD denoising was employed in simulated images and gas phantoms with known diffusion coefficients to validate its effectiveness and optimize parameters for analysis of diffusion-weighted 129Xe MRI. GLHOSVD was applied to data from 120 subjects (34 control, 39 cystic fibrosis (CF), 27 lymphangioleiomyomatosis (LAM), and 20 asthma). Image SNR, ADC, and distributed diffusivity coefficient (DDC) were compared before and after denoising using Wilcoxon signed-rank analysis for all images.

RESULTS

Denoising significantly increased SNR in simulated, phantom, and in-vivo images, showing a greater than 2-fold increase (p < 0.001) across diffusion-weighted images. Although mean ADC and DDC remained unchanged (p > 0.05), ADC and DDC standard deviation decreased significantly in denoised images (p < 0.001).

CONCLUSION

When applied to diffusion-weighted 129Xe images, GLHOSVD improved image quality and allowed airspace size to be quantified in high-diffusion regions of the lungs that were previously inaccessible to measurement due to prohibitively low SNR, thus providing insights into disease pathology.

Assessment of pulmonary morphometry using hyperpolarized 129 Xe diffusion-weighted MRI with variable-sampling-ratio compressed sensing patterns

BACKGROUND

Hyperpolarized (HP) ¹²⁹ Xe multiple b-values diffusion-weighted magnetic resonance imaging (DW-MRI) has been widely used for quantifying pulmonary microstructural morphometry. However, the technique requires long acquisition times, making it hard to apply in patients with severe pulmonary diseases, who cannot sustain long breath holds.

PURPOSE

To develop and evaluate the technique of variable-sampling-ratio compressed sensing (VCS) patterns for accelerating HP ¹²⁹ Xe multiple b-values DW-MRI in humans.

METHODS

Optimal variable sampling ratios and corresponding k-space undersampling patterns for each b-value were obtained by retrospective simulations based on the fully sampled (FS) DW-MRI dataset acquired from six young healthy volunteers. Then, the FS datasets were retrospectively undersampled using both VCS patterns and conventional compressed sensing (CS) pattern with a similar average acceleration factor. The quality of reconstructed images with retrospective VCS (rVCS) and CS (rCS) datasets were quantified using mean absolute error (MAE) and structural similarity (SSIM). Pulmonary morphometric parameters were also evaluated between rVCS and FS datasets. In addition, prospective VCS multiple b-values ¹²⁹ Xe DW-MRI datasets were acquired from 14 cigarette smokers and 13 age-matched healthy volunteers. The differences of lung morphological parameters obtained with the proposed method were compared between the groups using independent samples t-test. Pearson correlation coefficient was also utilized for evaluating the correlation of the pulmonary physiological parameters obtained with VCS DW-MRI and pulmonary function tests.

RESULTS

Lower MAE and higher SSIM values were found in the reconstructed images with rVCS measurement when compared to those using conventional rCS measurement. The details and quality of the images obtained with rVCS and FS measurements were found to be comparable. The mean values of the morphological parameters derived from rVCS and FS datasets showed no significant differences (p > 0.05), and the mean differences of measured acinar duct radius, mean linear intercept, surface-to-volume ratio, and apparent diffusion coefficient with cylinder model were -0.87%, -2.42%, 2.04%, and -0.50%, respectively. By using the VCS technique, significant differences were delineated between the pulmonary morphometric parameters of healthy volunteers and cigarette smokers (p < 0.001), while the acquisition time was reduced by four times.

CONCLUSION

A fourfold reduction in acquisition time was achieved using the proposed VCS method while preserving good image quality. Our preliminary results demonstrated that the proposed method can be used for evaluating pulmonary injuries caused by cigarette smoking and may prove to be helpful in diagnosing lung diseases in clinical practice.

Feasibility of Dynamic Inhaled Gas MRI-Based Measurements Using Acceleration Combined with the Stretched Exponential Model

Dynamic inhaled gas (³He/¹²⁹Xe/¹⁹F) MRI permits the acquisition of regional fractional-ventilation which is useful for detecting gas-trapping in lung-diseases such as lung fibrosis and COPD. Deninger's approach used for analyzing the wash-out data can be substituted with the stretched-exponential-model (SEM) because signal-intensity is attenuated as a function of wash-out-breath in ¹⁹F lung imaging. Thirteen normal-rats were studied using ³He/¹²⁹Xe and ¹⁹F MRI and the ventilation measurements were performed using two 3T clinical-scanners. Two Cartesian-sampling-schemes (Fast-Gradient-Recalled-Echo/X-Centric) were used to test the proposed method. The fully sampled dynamic wash-out images were retrospectively under-sampled (acceleration-factors (AF) of 10/14) using a varying-sampling-pattern in the wash-out direction. Mean fractional-ventilation maps using Deninger's and SEM-based approaches were generated. The mean fractional-ventilation-values generated for the fully sampled k-space case using the Deninger method were not significantly different from other fractional-ventilation-values generated for the non-accelerated/accelerated data using both Deninger and SEM methods (p > 0.05 for all cases/gases). We demonstrated the feasibility of the SEM-based approach using retrospective under-sampling, mimicking AF = 10/14 in a small-animal-cohort from the previously reported dynamic-lung studies. A pixel-by-pixel comparison of the Deninger-derived and SEM-derived fractional-ventilation-estimates obtained for AF = 10/14 (≤16% difference) has confirmed that even at AF = 14, the accuracy of the estimates is high enough to consider this method for prospective measurements.

Artificial intelligence in functional imaging of the lung

Artificial intelligence (AI) is transforming the way we perform advanced imaging. From high-resolution image reconstruction to predicting functional response from clinically acquired data, AI is promising to revolutionize clinical evaluation of lung performance, pushing the boundary in pulmonary functional imaging for patients suffering from respiratory conditions. In this review, we overview the current developments and expound on some of the encouraging new frontiers. We focus on the recent advances in machine learning and deep learning that enable reconstructing images, quantitating, and predicting functional responses of the lung. Finally, we shed light on the potential opportunities and challenges ahead in adopting AI for functional lung imaging in clinical settings.

Hyperpolarized 129Xe Pulmonary MRI and Asymptomatic Atrial Septal Defect

In an asymptomatic 19-year-old who regularly underwent cardiopulmonary fitness testing for national lifeguard-accreditation, ¹²⁹Xe MRI unexpectedly revealed an abnormally augmented RBC signal and RBC-to-alveolar-capillary-tissue ratio with spatially homogeneous ventilation, tissue barrier, and RBC images. Pulmonary function was normal, but cardiopulmonary follow-up including transthoracic and transesophageal echocardiogram, heart catheterization, and contrast-enhanced cardiac CT imaging led to the diagnosis of a large (20 × 27 mm) secundum atrial septal defect (ASD) with a net right-to-left shunt (Qp:Qs = 0.5) and normal pulmonary pressures. This novel, unexpected case revealed that ¹²⁹Xe RBC signal intensity likely reflected erythrocytosis, compensatory to the abnormal cardiovascular hemodynamics that resulted from a large congenital ASD. Unlike ASD cases that present with dyspnea and exercise limitation, this ¹²⁹Xe MRI abnormality was detected in an asymptomatic teenager. This is the first report of asymptomatic adult congenital heart disease diagnosed subsequent to novel ¹²⁹Xe MRI that led to early intervention, avoiding long-term complications of cyanosis, including ventricular fibrosis and thromboembolic and bleeding risks.

Application of a 2D frequency encoding sectoral approach to hyperpolarized 129Xe MRI at low field

Inhaled hyperpolarized ¹²⁹Xe MRI is a non-invasive and radiation risk free lung imaging method, which can directly measure the business unit of the lung where gas exchange occurs: the alveoli and acinar ducts (lung function). Currently, three imaging approaches have been demonstrated to be useful for hyperpolarized ¹²⁹Xe MR in lungs: Fast Gradient Recalled Echo (FGRE), Radial Projection Reconstruction (PR), and spiral/cones. Typically, non-Cartesian acquisitions such as PR and spiral/cones require specific data post-processing, such as interpolating, regridding, and density-weighting procedures for image reconstruction, which often leads to smoothing effects and resolution degradation. On the other hand, Cartesian methods such as FGRE are not short-echo time (TE) methods; they suffer from imaging gradient-induced diffusion-weighting of the k-space center, and employ a significant number of radio-frequency (RF) pulses. Due to the non-renewable magnetization of the hyperpolarized media, the use of a large number of RF pulses (FGRE/PR) required for full k-space coverage is a significant limitation, especially for low field (<0.5 T) hyperpolarized gas MRI. We demonstrate an ultra-fast, purely frequency-encoded, Cartesian pulse sequence called Frequency-Encoding Sectoral (FES), which takes advantage of the long T₂* of hyperpolarized ¹²⁹Xe gas at low field strength (0.074 T). In contrast to PR/FGRE, it uses a much smaller number of RF pulses, and consequently maximizes image Signal-to-Noise Ratio (SNR) while shortening acquisition time. Additionally, FES does not suffer from non-uniform T₂* decay leading to image blurring; a common issue with interleaved spirals/cones. The Cartesian k-space coverage of the proposed FES method does not require specific k-space data post-processing, unlike PR/FGRE and spiral/cones methods. Proton scans were used to compare the FES sequence to both FGRE and Phase Encoding Sectoral, in terms of their SNR values and imaging efficiency estimates. Using FES, proton and hyperpolarized ¹²⁹Xe images were acquired from a custom hollow acrylic phantom (0.04L) and two normal rats (¹²⁹Xe only), utilizing both single-breath and multiple-breath schemes. For the ¹²⁹Xe phantom images, the apparent diffusion coefficient, T₁, and T₂* relaxation maps were acquired and generated. Blurring due to the T₂* decay and B₀ field variation were simulated to estimate dependence of the image resolution on the duration of the data acquisition windows (i.e. sector length), and temperature-induced resonance frequency shift from the low field magnet hardware.

Accelerate gas diffusion-weighted MRI for lung morphometry with deep learning

OBJECTIVES

Multiple b-value gas diffusion-weighted MRI (DW-MRI) enables non-invasive and quantitative assessment of lung morphometry, but its long acquisition time is not well-tolerated by patients. We aimed to accelerate multiple b-value gas DW-MRI for lung morphometry using deep learning.

METHODS

A deep cascade of residual dense network (DC-RDN) was developed to reconstruct high-quality DW images from highly undersampled k-space data. Hyperpolarized ¹²⁹Xe lung ventilation images were acquired from 101 participants and were retrospectively collected to generate synthetic DW-MRI data to train the DC-RDN. Afterwards, the performance of the DC-RDN was evaluated on retrospectively and prospectively undersampled multiple b-value ¹²⁹Xe MRI datasets.

RESULTS

Each slice with size of 64 × 64 × 5 could be reconstructed within 7.2 ms. For the retrospective test data, the DC-RDN showed significant improvement on all quantitative metrics compared with the conventional reconstruction methods (p < 0.05). The apparent diffusion coefficient (ADC) and morphometry parameters were not significantly different between the fully sampled and DC-RDN reconstructed images (p > 0.05). For the prospectively accelerated acquisition, the required breath-holding time was reduced from 17.8 to 4.7 s with an acceleration factor of 4. Meanwhile, the prospectively reconstructed results showed good agreement with the fully sampled images, with a mean difference of -0.72% and -0.74% regarding global mean ADC and mean linear intercept (L_m) values.

CONCLUSIONS

DC-RDN is effective in accelerating multiple b-value gas DW-MRI while maintaining accurate estimation of lung microstructural morphometry, facilitating the clinical potential of studying lung diseases with hyperpolarized DW-MRI.

KEY POINTS

• The deep cascade of residual dense network allowed fast and high-quality reconstruction of multiple b-value gas diffusion-weighted MRI at an acceleration factor of 4. • The apparent diffusion coefficient and morphometry parameters were not significantly different between the fully sampled images and the reconstructed results (p > 0.05). • The required breath-holding time was reduced from 17.8 to 4.7 s and each slice with size of 64 × 64 × 5 could be reconstructed within 7.2 ms.

Protocols for multi-site trials using hyperpolarized 129 Xe MRI for imaging of ventilation, alveolar-airspace size, and gas exchange: A position paper from the 129 Xe MRI clinical trials consortium

Hyperpolarized (HP) ¹²⁹ Xe MRI uniquely images pulmonary ventilation, gas exchange, and terminal airway morphology rapidly and safely, providing novel information not possible using conventional imaging modalities or pulmonary function tests. As such, there is mounting interest in expanding the use of biomarkers derived from HP ¹²⁹ Xe MRI as outcome measures in multi-site clinical trials across a range of pulmonary disorders. Until recently, HP ¹²⁹ Xe MRI techniques have been developed largely independently at a limited number of academic centers, without harmonizing acquisition strategies. To promote uniformity and adoption of HP ¹²⁹ Xe MRI more widely in translational research, multi-site trials, and ultimately clinical practice, this position paper from the ¹²⁹ Xe MRI Clinical Trials Consortium (https://cpir.cchmc.org/XeMRICTC) recommends standard protocols to harmonize methods for image acquisition in HP ¹²⁹ Xe MRI. Recommendations are described for the most common HP gas MRI techniques-calibration, ventilation, alveolar-airspace size, and gas exchange-across MRI scanner manufacturers most used for this application. Moreover, recommendations are described for ¹²⁹ Xe dose volumes and breath-hold standardization to further foster consistency of imaging studies. The intention is that sites with HP ¹²⁹ Xe MRI capabilities can readily implement these methods to obtain consistent high-quality images that provide regional insight into lung structure and function. While this document represents consensus at a snapshot in time, a roadmap for technical developments is provided that will further increase image quality and efficiency. These standardized dosing and imaging protocols will facilitate the wider adoption of HP ¹²⁹ Xe MRI for multi-site pulmonary research.

Application of a stretched-exponential model for morphometric analysis of accelerated diffusion-weighted 129Xe MRI of the rat lung

OBJECTIVE

Diffusion-weighted, hyperpolarized ¹²⁹Xe MRI is useful for the characterization of microstructural changes in the lung. A stretched exponential model was proposed for morphometric extraction of the mean chord length (L_m) from diffusion-weighted data. The stretched exponential model enables accelerated mapping of L_m in a single-breathhold using compressed sensing. Our purpose was to compare L_m maps obtained from stretched-exponential model analysis of accelerated versus unaccelerated diffusion-weighted ¹²⁹Xe MRI data obtained from healthy/injured rat lungs.

MATERIAL AND METHODS

L_m maps were generated using a stretched-exponential model analysis of previously acquired fully sampled diffusion-weighted ¹²⁹Xe rat data (b values = 0 … 110 s/cm²) and compared to L_m maps generated from retrospectively undersampled data simulating acceleration factors of 7/10. The data included four control rats and five rats receiving whole-lung irradiation to mimic radiation-induced lung injury. Mean L_m obtained from the accelerated/unaccelerated maps were compared to histological mean linear intercept.

RESULTS

Accelerated L_m estimates were similar to unaccelerated L_m estimates in all rats, and similar to those previously reported (< 12% different). L_m was significantly reduced (p < 0.001) in the irradiated rat cohort (90 ± 20 µm/90 ± 20 µm) compared to the control rats (110 ± 20 µm/100 ± 15 µm) and agreed well with histological mean linear intercept.

DISCUSSION

Accelerated mapping of L_m using a stretched-exponential model analysis is feasible, accurate and agrees with histological mean linear intercept. Acceleration reduces scan time, thus should be considered for the characterization of lung microstructural changes in humans where breath-hold duration is short.