Improving hyperpolarized 129 Xe ADC mapping in pediatric and adult lungs with uncertainty propagation

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.

Hyperpolarized Gas Imaging in Lung Diseases: Functional and Artificial Intelligence Perspective

Pathophysiologic changes in lung diseases are often accompanied by changes in ventilation and gas exchange. Comprehensive evaluation of lung function cannot be obtained through chest X-ray and computed tomography. Proton-based lung MRI is particularly challenging due to low proton density within the lung tissue. In this review, we discuss an emerging technology--hyperpolarized gas MRI with inhaled 129Xe, which provides functional and microstructural information and has the potential as a clinical tool for detecting the early stage and progression of certain lung diseases. We review the hyperpolarized 129Xe MRI studies in patients with a range of pulmonary diseases, including chronic obstructive pulmonary disease, asthma, cystic fibrosis, pulmonary hypertension, radiation-induced lung injury and interstitial lung disease, and the applications of artificial intelligence were reviewed as well.

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.