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

Improving Xenon-129 lung ventilation image SNR with deep-learning based image reconstruction

PURPOSE

To evaluate the feasibility and utility of a deep learning (DL)-based reconstruction for improving the SNR of hyperpolarized 129Xe lung ventilation MRI.

METHODS

129Xe lung ventilation MRI data acquired from patients with asthma and/or chronic obstructive pulmonary disease (COPD) were retrospectively reconstructed with a commercial DL reconstruction pipeline at five different denoising levels. Quantitative imaging metrics of lung ventilation including ventilation defect percentage (VDP) and ventilation heterogeneity index (VHI) were compared between each set of DL-reconstructed images and alternative denoising strategies including: filtering, total variation denoising and higher-order singular value decomposition. Structural similarity between the denoised and original images was assessed. In a prospective study, the feasibility of using SNR gains from DL reconstruction to allow natural-abundance xenon MRI was evaluated in healthy volunteers.

RESULTS

129Xe ventilation image SNR was improved with DL reconstruction when compared with conventionally reconstructed images. In patients with asthma and/or COPD, DL-reconstructed images exhibited a slight positive bias in ventilation defect percentage (1.3% at 75% denoising) and ventilation heterogeneity index (˜1.4) when compared with conventionally reconstructed images. Additionally, DL-reconstructed images preserved structural similarity more effectively than data denoised using alternative approaches. DL reconstruction greatly improved image SNR (greater than threefold), to a level that 129Xe ventilation imaging using natural-abundance xenon appears feasible.

CONCLUSION

DL-based image reconstruction significantly improves 129Xe ventilation image SNR, preserves structural similarity, and leads to a minor bias in ventilation metrics that can be attributed to differences in the image sharpness. This tool should help facilitate cost-effective 129Xe ventilation imaging with natural-abundance xenon in the future.

A decay-modeled compressed sensing reconstruction approach for non-Cartesian hyperpolarized 129Xe MRI

PURPOSE

Hyperpolarized 129Xe MRI benefits from non-Cartesian acquisitions that sample k-space efficiently and rapidly. However, their reconstructions are complex and burdened by decay processes unique to hyperpolarized gas. Currently used gridded reconstructions are prone to artifacts caused by magnetization decay and are ill-suited for undersampling. We present a compressed sensing (CS) reconstruction approach that incorporates magnetization decay in the forward model, thereby producing images with increased sharpness and contrast, even in undersampled data.

METHODS

Radio-frequency, T1, andT 2 * $$ {\mathrm{T}}_2^{\ast } $$ decay processes were incorporated into the forward model and solved using iterative methods including CS. The decay-modeled reconstruction was validated in simulations and then tested in 2D/3D-spiral ventilation and 3D-radial gas-exchange MRI. Quantitative metrics including apparent-SNR and sharpness were compared between gridded, CS, and twofold undersampled CS reconstructions. Observations were validated in gas-exchange data collected from 15 healthy and 25 post-hematopoietic-stem-cell-transplant participants.

RESULTS

CS reconstructions in simulations yielded images with threefold increases in accuracy. CS increased sharpness and contrast for ventilation in vivo imaging and showed greater accuracy for undersampled acquisitions. CS improved gas-exchange imaging, particularly in the dissolved-phase where apparent-SNR improved, and structure was made discernable. Finally, CS showed repeatability in important global gas-exchange metrics including median dissolved-gas signal ratio and median angle between real/imaginary components.

CONCLUSION

A non-Cartesian CS reconstruction approach that incorporates hyperpolarized 129Xe decay processes is presented. This approach enables improved image sharpness, contrast, and overall image quality in addition to up-to threefold undersampling. This contribution benefits all hyperpolarized gas MRI through improved accuracy and decreased scan durations.

Analytical corrections for B1-inhomogeneity and signal decay in multi-slice 2D spiral hyperpolarized 129Xe MRI using keyhole reconstruction

PURPOSE

Hyperpolarized xenon MRI suffers from heterogeneous coil bias and magnetization decay that obscure pulmonary abnormalities. Non-physiological signal variability can be mitigated by measuring and mapping the nominal flip angle, and by rescaling the images to correct for signal bias and decay. While flip angle maps can be calculated from sequentially acquired images, scan time and breath-hold duration are doubled. Here, we exploit the low-frequency oversampling of 2D-spiral and keyhole reconstruction to measure flip angle maps from a single acquisition.

METHODS

Flip angle maps were calculated from two images generated from a single dataset using keyhole reconstructions and a Bloch-equation-based model suitable for hyperpolarized substances. Artifacts resulting from acquisition and reconstruction schemes (e.g., keyhole reconstruction radius, slice-selection profile, spiral-ordering, and oversampling) were assessed using point-spread functions. Simulated flip angle maps generated using keyhole reconstruction were compared against the paired-image approach using RMS error (RMSE). Finally, feasibility was demonstrated for in vivo xenon ventilation imaging.

RESULTS

Simulations demonstrated accurate flip angle maps and B1-inhomogeneity correction can be generated with only 1.25-fold central-oversampling and keyhole reconstruction radius = 5% (RMSE = 0.460°). These settings also generated accurate flip angle maps in a healthy control (RSME = 0.337°) and a person with cystic fibrosis (RMSE = 0.404°) in as little as 3.3 s.

CONCLUSION

Regional lung ventilation images with reduced impact of B1-inhomogeneity can be acquired rapidly by combining 2D-spiral acquisition, Bloch-equation-based modeling, and keyhole reconstruction. This approach will be especially useful for breath-hold studies where short scan durations are necessary, such as dynamic imaging and applications in children or people with severely compromised respiratory function.

Rapid pulmonary 129Xe ventilation MRI of discharged COVID-19 patients with zigzag sampling

PURPOSE

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

METHODS

Zigzag sampling in one direction was combined with gradient-recalled echo sequence (GRE-zigzag-Y) to acquire hyperpolarized 129Xe ventilation images. Image quality was compared with a balanced SSFP (bSSFP) sequence with the same spatial resolution for 12 healthy volunteers (HVs). For another 8 HVs and 9 discharged coronavirus disease 2019 subjects, isotropic resolution 129Xe ventilation images were acquired using zigzag sampling in two directions through GRE-zigzag-YZ. 129Xe ventilation defect percent (VDP) was quantified for GRE-zigzag-YZ and bSSFP acquisitions. Relationships and agreement between these VDP measurements were evaluated using Pearson correlation coefficient (r) and Bland-Altman analysis.

RESULTS

For 12 HVs, GRE-zigzag-Y and bSSFP required 2.2 s and 10.5 s, respectively, to acquire 129Xe images with a spatial resolution of 3.96 × 3.96 × 10.5 mm3. Structural similarity index, mean absolute error, and Dice similarity coefficient between the two sets of images and ventilated lung regions were 0.85 ± 0.03, 0.0015 ± 0.0001, and 0.91 ± 0.02, respectively. For another 8 HVs and 9 coronavirus disease 2019 subjects, 129Xe images with a nominal spatial resolution of 2.5 × 2.5 × 2.5 mm3 were acquired within 5.5 s per subject using GRE-zigzag-YZ. VDP provided by GRE-zigzag-YZ was strongly correlated (R2 = 0.93, p < 0.0001) with that generated by bSSFP with minimal biases (bias = -0.005%, 95% limit-of-agreement = [-0.414%, 0.424%]).

CONCLUSION

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

Combining neural networks and image synthesis to enable automatic thoracic cavity segmentation of hyperpolarized 129Xe MRI without proton scans

RATIONALE AND OBJECTIVES

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

MATERIALS AND METHODS

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

RESULTS

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

CONCLUSION

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

A single-breath-hold protocol for hyperpolarized 129 Xe ventilation and gas exchange imaging

Hyperpolarized 129 Xe MRI (Xe-MRI) is increasingly used to image the structure and function of the lungs. Because 129 Xe imaging can provide multiple contrasts (ventilation, alveolar airspace size, and gas exchange), imaging often occurs over several breath-holds, which increases the time, expense, and patient burden of scans. We propose an imaging sequence that can be used to acquire Xe-MRI gas exchange and high-quality ventilation images within a single, approximately 10 s, breath-hold. This method uses a radial one-point Dixon approach to sample dissolved 129 Xe signal, which is interleaved with a 3D spiral ("FLORET") encoding pattern for gaseous 129 Xe. Thus, ventilation images are obtained at higher nominal spatial resolution (4.2 × 4.2 × 4.2 mm3 ) compared with gas-exchange images (6.25 × 6.25 × 6.25 mm3 ), both competitive with current standards within the Xe-MRI field. Moreover, the short 10 s Xe-MRI acquisition time allows for 1 H "anatomic" images used for thoracic cavity masking to be acquired within the same breath-hold for a total scan time of about 14 s. Images were acquired using this single-breath method in 11 volunteers (N = 4 healthy, N = 7 post-acute COVID). For 11 of these participants, a separate breath-hold was used to acquire a "dedicated" ventilation scan and five had an additional "dedicated" gas exchange scan. The images acquired using the single-breath protocol were compared with those from dedicated scans using Bland-Altman analysis, intraclass correlation (ICC), structural similarity, peak signal-to-noise ratio, Dice coefficients, and average distance. Imaging markers from the single-breath protocol showed high correlation with dedicated scans (ventilation defect percent, ICC = 0.77, p = 0.01; membrane/gas, ICC = 0.97, p = 0.001; red blood cell/gas, ICC = 0.99, p < 0.001). Images showed good qualitative and quantitative regional agreement. This single-breath protocol enables the collection of essential Xe-MRI information within one breath-hold, simplifying scanning sessions and reducing costs associated with Xe-MRI.

B1 and magnetization decay correction for hyperpolarized 129 Xe lung imaging using sequential 2D spiral acquisitions

PURPOSE

To mitigate signal variations caused by inhomogeneous RF and magnetization decay in hyperpolarized 129 Xe ventilation images using flip-angle maps generated from sequential 2D spiral ventilation images acquired in a breath-hold. Images and correction maps were compared with those obtained using conventional, 2D gradient-recalled echo.

THEORY AND METHODS

Analytical expressions to predict signal intensity and uncertainty in flip-angle measurements were derived from the Bloch equations and validated by simulations and phantom experiments. Imaging in 129 Xe phantoms and human subjects (1 healthy, 1 cystic fibrosis) was performed using 2D gradient-recalled echo and spiral. For both sequences, consecutive images were acquired with the same slice position during a breath-hold (Cartesian scan time = 15 s; spiral scan time = 5 s). The ratio of these images was used to calculate flip-angle maps and correct intensity inhomogeneities in ventilation images.

RESULTS

Mean measured flip angle showed excellent agreement with the applied flip angle in simulations (R2  = 0.99) for both sequences. Mean measured flip angle agreed well with the globally applied flip angle (∼15% difference) in 129 Xe phantoms and in vivo imaging using both sequences. Corrected images displayed reduced coil-dependent signal nonuniformity relative to uncorrected images.

CONCLUSIONS

Flip-angle maps were obtained using sequentially acquired, 2D spiral, 129 Xe ventilation images. Signal intensity variations caused by RF-coil inhomogeneity can be corrected by acquiring sequential single-breath ventilation images in less than 5-s scan time. Thus, this method can be used to remove undesirable heterogeneity while preserving physiological effects on the signal distribution.

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.