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.

Introduction

Detection of microstructural changes in the distal airspaces can be crucial for clinical evaluation of early stage lung disease and longitudinal monitoring of pulmonary diseases. The standard procedures for detection of disease in the distal airspaces include spirometry, test of diffusing capacity of the lung for carbon monoxide (D_LCO), and lung density analysis from computed tomography (CT). However, all the mentioned techniques can fail to reveal early indications of pathological changes^1,2. Therefore, considerable disease can be present with minimal effect on standard pulmonary function tests (PFTs).

Diffusion-weighted (DW) magnetic resonance imaging (MRI) with inhaled hyperpolarised noble gases helium-3 (³He) or xenon-129 (¹²⁹Xe) is an in vivo imaging method that is sensitive to changes in the distal airspaces^3–5. The method is based on measurement of the Brownian diffusional restriction of the inhaled hyperpolarised gas atoms within the distal airspace walls. This property is used to derive the apparent diffusion coefficient (ADC), which provides 3D in vivo information on the distal airspace microstructure. In addition to ADC, theoretical models of hyperpolarised gas diffusion within the lungs, such as the stretched exponential model (SEM)^6,7 and the cylinder airway model (CM)^8,9, can be used to derive distal airspace dimensions, analogous to those obtained through histological analysis. These in vivo distal airspace measurements from hyperpolarised gas DW-MRI have shown good agreement with direct morphometric measurements in validation studies with lung specimens^8,10,11. Numerous studies have used hyperpolarised gas DW-MRI to elucidate changes in distal airspaces related to smoking^12–14, ageing¹⁵, lung inflation¹⁶, and diseases such as COPD^17,18, asthma¹⁹, and idiopathic pulmonary fibrosis (IPF)²⁰. However, due to the high cost and complex infrastructure required for hyperpolarised gas MRI, the technique is not typically employed in standard pulmonary function testing or screening for pulmonary disease.

Airspace Dimension Assessment (AiDA) with inhaled nanoparticles, could potentially be more cost efficient and more widely accessible than MRI with hyperpolarised gases. The method is based on measurement of the exhaled recovery of inhaled nanoparticles which deposit in the distal airspaces due to Brownian diffusion²¹. The fraction of deposited particles is directly related to the size of the airspaces, and analysis of the nanoparticle recovery yields two metrics. The first metric is an effective airspace radius (r_AiDA), which is a root mean square measure of a collection of airspaces, and the second metric is the recovery at an imaginary zero-seconds breath-hold (R₀). r_AiDA has been found to correlate with the extent of emphysema²² and proton lung tissue density as quantified by standard pulmonary structural MRI²³. R₀ significantly correlates with the carbon monoxide transfer coefficient (K_CO) and age²⁴. Further benchmarking of the technique with established methods of in vivo distal airspace assessment is required to evaluate the clinical potential of the AiDA technique.

The aim of this study was to benchmark inhaled nanoparticle measurements with AiDA against ¹²⁹Xe DW-MRI derived ADC and distal airspace dimensions. Since both AiDA²¹ and ¹²⁹Xe DW-MRI ⁶ use diffusion of nanoparticles and gas molecules, we hypothesise that measures from the two different techniques will correlate.

Results

Volunteer demographics, pulmonary function test data, DW-MRI metrics and AiDA variables

Table 1 summarises volunteer demographics, pulmonary function test results, hyperpolarised ¹²⁹Xe DW-MRI metrics and AiDA values for the 23 volunteers.

Table 1.

Summary of volunteer demographics and global measurements.

Characteristic	Mean ± SD	Median (IQR)	Min	Max
Age (years)	48 ± 17	54 (34)	23	70
Height (cm)	173.7 ± 9.8	175 (15)	155	191
Weight (kg)	75.5 ± 14.4	77 (24)	55	103
FEV1 (% pred)	96.0 ± 9.7	95.8 (10.9)	80.1	115.4
VC (% pred)	96.5 ± 12.4	94.8 (10.3)	74.7	132.4
TLC (% pred)	97.9 ± 11.6	96.8 (14.5)	80.3	128.2
RV (% pred)	102.5 ± 24.8	102.2 (27.6)	57	182
FRC (% pred)	93.3 ± 16.8	93.4 (25.0)	57.6	116.5
DLCO (% pred)	102.8 ± 18.8	101.3 (23.7)	67.5	153.7
KCO (% pred)	113.2 ± 16.2	112 (21.4)	85	150.3
ADC (cm2/s)	0.034 ± 0.004	0.034 (0.0041)	0.0263	0.0434
α (a.u.)	0.862 ± 0.015	0.863 (0.023)	0.826	0.889
DDC (cm2/s)	0.030 ± 0.004	0.030 (0.005)	0.022	0.030
LmD (µm)	281 ± 19	277 (20)	244	322
rAiDA (µm)	279 ± 25	284 (42)	240	325
R0 (a.u.)	0.484 ± 0.132	0.51 (0.21)	0.183	0.675

Data are presented as mean ± SD, median (IQR), minimum values and maximum values.

IQR interquartile range, FEV₁ forced expiratory volume in 1 s, VC vital capacity, TLC total lung capacity, RV residual volume, FRC forced respiratory capacity; D_LCO diffusing capacity of lung for carbon monoxide, K_CO carbon monoxide transfer coefficient, ADC apparent diffusion coefficient, α heterogeneity index, DDC distributed diffusivity coefficient, Lm_D mean diffusive length scale, r_AiDA distal airspace radius, R₀ zero-second recovery.

Benchmarking of AiDA using the DW-MRI measurements

Statistically significant linear correlations were observed between r_AiDA and ¹²⁹Xe DW-MRI metrics ADC (p < 0.005), DDC (p < 0.001) and Lm_D (p < 0.001) (Fig. 1a–c). The R₀ intercept measurement from AiDA was significantly correlated with ¹²⁹Xe α heterogeneity index (p = 0.02) (Fig. 1d), but not with any of the other ¹²⁹Xe DW-MRI metrics. Bland–Altman analysis of r_AiDA and ¹²⁹Xe Lm_D showed a mean bias of − 1.2 µm (95% agreement limits − 36.0 to 33.6 µm) towards r_AiDA (Fig. 2). r_AiDA and Lm_D deviated less than 0.05% for the whole group and on average 8% between subjects.

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.

For the comparison in the Bland–Altman plot, a linear relationship of strength ρ = 0.48 was observed (p = 0.02) suggesting r_AiDA increased more than Lm_D with increasing airspace size. No difference was seen between men and women for the AiDA and ¹²⁹Xe DW-MRI metric distributions.

Significant correlations were observed between volunteer age ¹²⁹Xe DW-MRI metrics ADC (p < 0.005), DDC (p < 0.001) and Lm_D (p < 0.001) (Fig. 3a–c). A trend towards increased r_AiDA with age was observed (p = 0.07) (Fig. 3d). A positive trend between R₀ and FEV₁ was observed (p = 0.07). AiDA and ¹²⁹Xe DW-MRI variables did not correlate significantly with any of the PFT measurements.

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.

Discussion

Measurements of distal airspace dimensions were acquired with AiDA and ¹²⁹Xe DW-MRI in 23 healthy volunteers. AiDA metrics, r_AiDA and R₀, corresponded to previously reported values from healthy volunteers^22–24. ¹²⁹Xe DW-MRI metrics of ADC, DDC and distal airspace dimensions (Lm_D) were larger than the values previously reported for younger healthy volunteers (29 ± 4 years), but smaller than values in ex-smoker volunteers⁶.

Distal airspace radius r_AiDA, significantly correlated with ¹²⁹Xe DW-MRI metrics ADC, DDC and Lm_D, which confirm to us that r_AiDA is a measure of distal airspace dimensions. Agreement between r_AiDA and ¹²⁹Xe DW-MRI Lm_D was confirmed with Bland–Altman analysis where a mean bias of − 1.2 µm towards r_AiDA, corresponding to 0.43% deviation from the mean measured r_AiDA, was observed. Both AiDA and ¹²⁹Xe DW-MRI measure the Brownian motion, presumably within the same distal airspace. The breath-hold times and diffusion times were optimised specifically for diffusion across small distances but assume that the particles stay within the same airway duct. The statistically significant correlation between R₀ and α heterogeneity index (p = 0.02) could be indicating that R₀ represents heterogeneity in the acini, which both metrics are hypothesized to measure^6,24.

The ADC, DDC and Lm_D dependency on age suggests a prevalence of age-related distal airspace changes in the older healthy volunteers²⁵. The significant correlations between volunteer age and ¹²⁹Xe ADC and Lm_D further demonstrate the sensitivity of DW-MRI to age-related airspace changes, and matches trends previously observed with ³He DW-MRI^15,26. A trend towards increasing r_AiDA with age was observed. α heterogeneity correlated with volunteer height (p < 0.001). AiDA and ¹²⁹Xe DW-MRI metrics did not correlate with any other volunteer demographic or PFT data.

The bias and relatively wide 95% agreement limits (− 36.0 to 33.6 µm) observed in the Bland–Altman plot can be explained by the difference in acquisition method of the two techniques. AiDA is acquired over multiple breath-holds with diffusion times ranging between 5 and 15 s. In contrast, ¹²⁹Xe DW-MRI is a single breath-hold acquisition with an 8.5 ms diffusion time. The linear trend in bias visible in the Bland Altman plot (Fig. 2) suggests that the agreement between r_AiDA and Lm_D changes with increasing mean airspace dimension size. This implies that the measurements from the two methods might diverge for increasing distal airspaces with an increasing bias between r_AiDA and Lm_D in larger airspaces. The increasing bias may be attributed to the difference between the diffusion coefficients for xenon (0.14 cm²/s when mixed with air in the lungs²⁷ and the 50 nm nanoparticles (2 × 10^–5 cm²/s²⁸). Due to the much smaller mass and size of ¹²⁹Xe-atoms they diffuse much faster than the nanoparticles. Hence, the exponential decay, from which the diffusion distance in the lung is calculated, is different for the two techniques. For ¹²⁹Xe DW-MRI the theoretical 1D free diffusion length ($L_{1D}$) is approximately 500 µm ($L_{1D}=\sqrt{2\mathrm{\Delta }{D}_0};\mathrm{\Delta }=$ 8.5 ms, $D_0=0.14$ cm²/s) while for 50 nm nanoparticles the corresponding displacement in one direction is approximately 200 µm ($\mathrm{\Delta }=$ 10 s, $D_0=$ 2 × 10⁻⁵ cm²/s).

While the ¹²⁹Xe DW-MRI method provides voxel-wise regional information about the lung structure the AiDA method provides one measure of the distal airspace dimensions. Therefore, the ¹²⁹Xe DW-MRI method can give more detailed and regional information about the structure of the distal airspaces compared to AiDA. However, as indicated by this study, the AiDA method has the potential to give a faster and more accessible, but still precise, measurement of the distal airspace dimensions, which can be of great importance when hyperpolarised lung MRI is not available. To further compare the relative sensitivity between the two methods more measurements, in particular including a large variation in distal space sizes, are needed.

Although the two methods obtained approximately similar airspace dimensions (average r_AiDA and Lm_D deviated less than 0.5%), there were also several significant differences in the measurement procedures for the subjects. ¹²⁹Xe DW-MRI measurements were made at FRC + 1 L while AiDA measurements were made at TLC. Hence, the lung inflation was larger for all AiDA measurements as compared to ¹²⁹Xe DW-MRI measurements. Previous studies have shown that measured ADC in hyperpolarised ³He DW-MRI increases with increasing lung inflation volumes²⁹. Which means that, AiDA could be expected to have measured larger dimensions r_AiDA when compared to Lm_D.

Distal airspace size also depends on posture and ADC decreases from the non-dependent region of the lung down to the dependent region. For hyperpolarised ³He DW-MRI measurements, ADC has been found to vary significantly depending on posture, and this was attributed to the compression of parenchyma, due to the lungs own weight, and the mass of the heart³⁰. In this study AiDA measurements were performed in upright sitting position while the ¹²⁹Xe DW-MRI measurements were performed in supine position. Therefore, a postural variation between whole lungs AiDA measurement and the regionally averaged ¹²⁹Xe DW-MRI metrics is expected. For a more elaborate comparison of the two methods the breath-hold volumes could be set to be equal and breath-hold times set to correspond to the same diffusion distances. In addition, AiDA could potentially be measured with subjects in a supine posture enabling an even more efficient comparison with minimized systematic errors.

AiDA measurements have been shown to be repeatable to approximately < 2.4% when measured at different times over a period of 18 months³¹. Previous studies have shown that ³He and ¹²⁹Xe ADC is highly repeatable in COPD patients with a coefficient of variation of 2.98% and 2.77% respectively, over 5 visits³². Similarly it has been showed that ³He Lm_D in patients with idiopathic pulmonary fibrosis is highly repeatable with a 0.6% difference between same-day visits²⁰.

This is the first study that compares AiDA with an independent and validated non-invasive method for assessment of distal airspaces. The study includes a limited number of healthy subjects, which mainly was due to the logistics related to travel between Sweden and the UK. The subject group was thus homogenous with small variations in PFT results. However, even with the small group of volunteers, significant correlations were found between AiDA and ¹²⁹Xe DW-MRI metrics, and interestingly these increased with age indicating age dependent changes in alveolar size or ‘aging emphysema’²⁵. Ideally, a future extension of this study would include subjects with a greater range of distal airspace sizes, such as patients with emphysematous lung disease.

In conclusion, this work has compared estimates of airspace radii from inhaled nanoparticles by the AiDA method with ¹²⁹Xe DW-MRI in a healthy volunteer cohort. The significant correlations show that the distal airspace radius of the lungs measured by AiDA (r_AiDA) can be related to distal airspace microstructure dimensions as quantified by ¹²⁹Xe DW-MRI. Quantitavely the two methods are in close agreement, with mean r_AiDA and Lm_D deviating < 0.5% for the whole group and on average 8% on an individual level. Further benchmarking in selected groups of patients could be used to evaluate the relative sensitivity of AiDA and ¹²⁹Xe DW-MRI in detecting early emphysematous changes to the distal airspace microstructure.

Methods

Study subjects and study design

The study enrolled 23 healthy adult volunteers (14M, 9F) in the age range 23–70 years with no history of pulmonary disease, in Sweden in the spring 2019. The study was approved by the Regional Ethical Review Board in Lund, Sweden (application number 2018/659), and performed in accordance with the Declaration of Helsinki, including obtaining informed written consent from all volunteers. All volunteers underwent spirometry, body plethysmography, carbon monoxide gas transfer, and AiDA measurements at Skåne University Hospital in Malmö, Sweden. Pulmonary function tests were performed according to the European Respiratory Society guidelines³³. All ¹²⁹Xe DW-MRI measurements were performed at University of Sheffield, Sheffield, UK.

AiDA measurements

The AiDA method and instrumentation has been described in detail elsewhere³¹. Figure 4 displays a schematic illustration of the AiDA instrument²³ and example data from one subject.

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.

In short, each subject initially inhaled particle-free air to remove background particles from their lungs. The subject was then instructed to exhale to residual volume (RV) prior to inhaling 50 nm polystyrene latex (PSL) aerosol particles to total lung capacity (TLC), hold their breath for a predefined time and finally exhale. For each measurement, the subject sat upright and a nose clip prevented them from breathing through the nose. Inhaled and exhaled nanoparticle number concentrations were registered for 8 consecutive measurements, with breath-hold times between 5 and 15 s. The inhaled nanoparticle concentration employed for AiDA was less than 10,000 cm⁻³, which is lower than the concentration of ambient nanoparticles in an urban environment³⁴.

The probability of particle deposition in the distal airspaces depends on residence time and airspace size. Enlarged airspaces yield a lower deposited fraction, corresponding to a higher particle recovery ($R$) in exhaled gas. The estimation of airspace dimensions using AiDA is based on the solution of the diffusion equation in circular tubes randomly distributed with axisymmetric boundary conditions²¹. The solution shows that the recovery R decays exponentially with residence time $\left(t\right)$ in the lung³⁵ according to:

$$R\left(t\right)=R_0{e}^{\frac{ln\left(2\right)}{t_{1/2}}t},$$

where $R_0$ is the recovery at zero-second breath-hold and and t_1/2 is the deposition half-life time. Residence time t was established and linear least-squares regression was fitted to the data. From the fit, R₀ and $t_{1/2}$ were determined. R₀ is presumably related to the dynamic phase of breathing and small conducting airways (generation 10–15³⁶), but remains to be evaluated further^21,24. The airspace radius r_AiDA, was calculated from $t_{1/2}$ and the diffusion coefficient ($D$) for 50 nm particles according to:

$$r_{\text{AiDA}}=2.89\sqrt{D{t}_{1/2}}$$

¹²⁹Xe DW-MRI measurements

Hyperpolarised ¹²⁹Xe DW-MRI was performed on a GE HDx 1.5T scanner with a flexible transmit/receive quadrature vest coil (Clinical MR Solutions, Brookfield, WI, USA) after the inhalation of a 1 L gas mixture containing 550 mL ¹²⁹Xe (> 25% polarization³⁷) and 450 mL N₂ from the level of function residual capacity. A 3D multiple b-value spoiled gradient echo (SPGR) sequence with compressed sensing was used with a 16 s breath-hold⁶. Specific DW-MRI acquisition parameters were: TE/TR = 14.0/17.3 ms, ¹²⁹Xe diffusion time = 8.5 ms, b = [0, 12, 20, 30 s/cm²]. ¹²⁹Xe ADC was calculated using a mono-exponential fit between the signal of the b = 0 (S₀) and 12 (S_b=12) s/cm² interleaves:

$$ADC=ln\left(\frac{S_0/S_{b=12}}{12}\right).$$

The mean diffusive length scale (Lm_D), a measure of mean distal airspace dimension from the SEM was derived by fitting the ¹²⁹Xe diffusion signal from all four b-values to a stretched exponential function⁶:

$$\frac{S\left(b\right)}{S_0}=\underset{0}{\overset{\infty }{\int }}p\left(D\right)e^{-bD}dD=e^{{\left[-b·DDC\right]}^{\alpha }},$$

where $p\left(D\right)$ is the probability distribution of different apparent diffusivities within each image voxel, DDC is the distributed diffusivity coefficient, and α is the heterogeneity index that describes the deviation from a mono-exponential decay (α = 1). α is thought to be a measure of the underlying complexity or heterogeneity of the geometry of the restricting distal boundaries³⁸. From α and DDC a numerical expression for $p\left(D\right)$ is estimated³⁹. Further, $p\left(D\right)$ is related to diffusion length scales associated with different apparent diffusivities (D) by the characteristic diffusion length ($L_D=\sqrt{2\mathrm{\Delta }D};\mathrm{\Delta }=$ diffusion time) which represent a measure of the distribution of microscopic dimensions. Lm_D, a measure of mean distal airspace dimensions, is defined as the expectation value of the probability distribution of diffusion length and is related to $p\left(D\right)$ by:

$$L{m}_D=\underset{0}{\overset{\infty }{\int }}\sqrt{2\mathrm{\Delta }D}p\left(D\right)dD.$$

Both ADC and SEM metrics were calculated on a voxel-by-voxel basis, and averaged across the entire lung volume to derive global means. Figure 5 displays examples of ADC and SEM-derived Lm_D maps from the hyperpolarised ¹²⁹Xe DW-MRI.

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.

Benchmarking of AiDA versus DW-MRI measurements

All of the AiDA analysis, ¹²⁹Xe DW-MRI lung morphometry calculations and comparisons of metrics were implemented using MATLAB Version 2020a (The MathWorks, Inc., Natick, Massachusetts, United States). Correlations between AiDA variables, ¹²⁹Xe DW-MRI metrics and standard PFT measurements were assessed using Spearman’s rank correlation test. The significance threshold was set at 0.05. Bland–Altman analysis was used to assess the agreement between the r_AiDA and Lm_D.

Author contributions

The study was conceived, planned and designed by M.P.S., H.F.C., J.L., P.W., L.E.O. and J.M.W. Experiments were planned, designed and performed by M.P.S., H.F.C., G.J.C. and G.N. with substantial support from J.L., P.W. and J.M.W. Data analysis was performed by M.P.S. and H.F.C. M.P.S., H.F.C., G.J.C., G.N., J.L., P.W., L.E.O. and J.M.W. interpreted and analysed the results. M.P.S. and H.F.C. prepared the figures and wrote the manuscript with substantial input from all other authors. All authors were involved in several rounds of critically revising the manuscript and approved the submitted version.

Funding

Open access funding provided by Lund University. This work was supported by the Swedish Heart and Lung foundation (Grant No 2017-0644, Grant No 2018-0483 and Grant No 2020-0855) and the Swedish Research Council for Health, Working Life and Welfare (Grant No 2017-00690) and NanoLund. This work was supported by National Institute for Health Research grant (NIHR-RP-R3-12-027) and Medical Research Council Grant (MR/M008894/1). The views expressed in this publication are those of the authors and not necessarily those of the NHS, the National Institute for Health Research or the Department of Health.

Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Competing interests

Prof. P. Wollmer received personal fees from Chiesi Pharmaceuticals during the conduct of the study. In addition, Prof. P Wollmer and Assoc. Prof. J. Löndahl have a patent for “Device and Method for pulmonary function measurements” issued. The other authors declare no competing interests.