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

Background: Hyperpolarized (HP) 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 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 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.