Airspace Dimension Assessment (AiDA) by inhaled nanoparticles: benchmarking with hyperpolarised 129Xe diffusion-weighted lung MRI

Abstract

Enlargements of distal airspaces can indicate pathological changes in the lung, but accessible and precise techniques able to measure these regions are lacking. Airspace Dimension Assessment with inhaled nanoparticles (AiDA) is a new method developed for in vivo measurement of distal airspace dimensions. The aim of this study was to benchmark the AiDA method against quantitative measurements of distal airspaces from hyperpolarised ¹²⁹Xe diffusion-weighted (DW)-lung magnetic resonance imaging (MRI). AiDA and ¹²⁹Xe DW-MRI measurements were performed in 23 healthy volunteers who spanned an age range of 23-70 years. The relationship between the ¹²⁹Xe DW-MRI and AiDA metrics was tested using Spearman's rank correlation coefficient. Significant correlations were observed between AiDA distal airspace radius (r_AiDA) and mean ¹²⁹Xe apparent diffusion coefficient (ADC) (p < 0.005), distributed diffusivity coefficient (DDC) (p < 0.001) and distal airspace dimension (Lm_D) (p < 0.001). A mean bias of - 1.2 µm towards r_AiDA was observed between ¹²⁹Xe Lm_D and r_AiDA, indicating that r_AiDA is a measure of distal airspace dimension. The AiDA R₀ intercept correlated with MRI ¹²⁹Xe α (p = 0.02), a marker of distal airspace heterogeneity. This study demonstrates that AiDA has potential to characterize the distal airspace microstructures and may serve as an alternative method for clinical examination of the lungs.

Figure 1

Linear regressions (with 95% confidence intervals in color) and Spearman’s correlation (ρ) of r_AiDA as a function of ¹²⁹Xe DW-MRI metrics: (a) ADC, (b) DDC, (c) Lm_D and of R₀ as a function of α heterogeneity index (d).

Figure 2

Bland–Altman plot of distal airspace radius r_AiDA and mean diffusive length scale Lm_D. A mean bias of 1.2 µm towards r_AiDA and 95% agreement limits − 36.0 to 33.6 µm were found.

Figure 3

Linear regressions (with 95% confidence intervals in blue) and Spearman’s correlation (ρ) of (a) ADC, (b) Lm_D, (c) DDC and (d) r_AiDA as a function of age.

Figure 4

(Left) Schematic illustration of the AiDA method. The aerosol particles from the distal airspaces were sampled to obtain the distal airspace radius r_AiDA and the zero-second recovery R_0. (Right) Representative recovery data as a function of residence time in the lungs and AiDA fit obtained for one volunteer.

Figure 5

Example hyperpolarised ¹²⁹Xe diffusion-weighted (DW) MRI data from one volunteer. (Left) Maps of ADC and Lm_D values calculated from ¹²⁹Xe DW-MRI. (Right) Representative global ¹²⁹Xe signal as a function of b-value, ADC- and SEM-fits obtained for the same volunteer.