Comparison of CT-derived ventilation maps with deposition patterns of inhaled microspheres in rats

Abstract

Purpose: Computer models for inhalation toxicology and drug-aerosol delivery studies rely on ventilation pattern inputs for predictions of particle deposition and vapor uptake. However, changes in lung mechanics due to disease can impact airflow dynamics and model results. It has been demonstrated that non-invasive, in vivo, 4DCT imaging (3D imaging at multiple time points in the breathing cycle) can be used to map heterogeneities in ventilation patterns under healthy and disease conditions. The purpose of this study was to validate ventilation patterns measured from CT imaging by exposing the same rats to an aerosol of fluorescent microspheres (FMS) and examining particle deposition patterns using cryomicrotome imaging.

Materials and methods: Six male Sprague-Dawley rats were intratracheally instilled with elastase to a single lobe to induce a heterogeneous disease. After four weeks, rats were imaged over the breathing cycle by CT then immediately exposed to an aerosol of ∼ 1 μm FMS for ∼ 5 minutes. After the exposure, the lungs were excised and prepared for cryomicrotome imaging, where a 3D image of FMS deposition was acquired using serial sectioning. Cryomicrotome images were spatially registered to match the live CT images to facilitate direct quantitative comparisons of FMS signal intensity with the CT-based ventilation maps.

Results: Comparisons of fractional ventilation in contiguous, non-overlapping, 3D regions between CT-based ventilation maps and FMS images showed strong correlations in fractional ventilation (r = 0.888, p < 0.0001).

Conclusion: We conclude that ventilation maps derived from CT imaging are predictive of the 1 μm aerosol deposition used in ventilation-perfusion heterogeneity inhalation studies.

Keywords: CT imaging; animal models; fluorescent microspheres; lung; particle deposition; ventilation.

Figure 1

Images used for nonlinear registration of the cryomicrotome images to the CT images. A) In situ TLC image acquired post mortem. Vasculature are bright structures and conducting airways are dark. B) White light cryomicrotome image downsampled to match the size of A. C) Color-inverted version of B to better match the feature intensities of A. Note the general absence of vasculature compared to A.

Figure 2

Example of axial slices of the CT and cryomicrotome images. A) In vivo CT image at EEV. B) White light cryomicrotome image of approximately the same slice as A. The major airways in A and B (black arrows) were used as landmarks to select comparable slices. C) In vivo CT-based ventilation image. D) Cryomicrotome image of FMS distribution. White arrows indicate receiver saturation due to particle agglomerations.

Figure 3

A visual comparison of the CT-based ventilation map with the nonlinearly registered FMS image. Coronal and axial views of a CT-based ventilation map (A and C) are shown side-by-side with comparable views of the FMS images (B and D). Arrows indicate FMS particle agglomerations that were erased prior to nonlinear registration.

Figure 4

Plots of the least squares fit linear slope (A) and correlation coefficient (B) versus cube size in voxels for the spatial comparison of ventilation maps to FMS images.

Figure 5

A 3D representation of a rat lung at EEV shown with a 15×15×15 voxel cube for relative size comparison. The 15×15×15 cube is 3 mm on a side.

Figure 6

Scatter plot showing a comparison of the fractional ventilation (in percent) measured by two different cube sizes (10×10×10 and 30×30×30) for a single rat to illustrate the effect of cube size on the resulting slope and correlation. The solid lines represent linear least-squares fits.

Figure 7

A) Scatter plot of the fractional signal (in percent) contained within each 15×15×15 cube for all six rats. The black line is a linear least-squares fit, with slope of 0.946 ± 0.008. B) Bland-Altman plot of data for all rats. Dashed lines show ±1.96 standard deviations of the difference.