Vertical distribution of specific ventilation in normal supine humans measured by oxygen-enhanced proton MRI

Abstract

Specific ventilation (SV) is the ratio of fresh gas entering a lung region divided by its end-expiratory volume. To quantify the vertical (gravitationally dependent) gradient of SV in eight healthy supine subjects, we implemented a novel proton magnetic resonance imaging (MRI) method. Oxygen is used as a contrast agent, which in solution changes the longitudinal relaxation time (T1) in lung tissue. Thus alterations in the MR signal resulting from the regional rise in O(2) concentration following a sudden change in inspired O(2) reflect SV-lung units with higher SV reach a new equilibrium faster than those with lower SV. We acquired T1-weighted inversion recovery images of a sagittal slice of the supine right lung with a 1.5-T MRI system. Images were voluntarily respiratory gated at functional residual capacity; 20 images were acquired with the subject breathing air and 20 breathing 100% O(2), and this cycle was repeated five times. Expired tidal volume was measured simultaneously. The SV maps presented an average spatial fractal dimension of 1.13 ± 0.03. There was a vertical gradient in SV of 0.029 ± 0.012 cm(-1), with SV being highest in the dependent lung. Dividing the lung vertically into thirds showed a statistically significant difference in SV, with SV of 0.42 ± 0.14 (mean ± SD), 0.29 ± 0.10, and 0.24 ± 0.08 in the dependent, intermediate, and nondependent regions, respectively (all differences, P < 0.05). This vertical gradient in SV is consistent with the known gravitationally induced deformation of the lung resulting in greater lung expansion in the dependent lung with inspiration. This SV imaging technique can be used to quantify regional SV in the lung with proton MRI.

Fig. 1.

Specific ventilation (SV) of a simple unit: V₀ is the end-expiratory volume of the unit and ΔV the volume increase that occurs during inspiration. C₀ is the initial concentration at end expiration. SV is defined as the ratio ΔV/V₀. Top left: schematic view depicts a low-SV unit. Top right: a unit with higher SV. Bottom: simulated response of both units to a sudden change in inspired O₂ fraction (Fi_O2; dotted line). The high-SV unit equilibrates faster (continuous line, SV = 0.8) than the lower-SV unit (dashed line, SV = 0.2).

Fig. 2.

A: correlation delay time for 2 simulated units with different SV (SV = 0.72 and SV = 0.12, continuous lines). Vertical lines (breaths 20 and 40) represent changes in inspired gas. The shifted driving function that maximizes the correlation with each unit is represented (square wave, dashed line). The integer amount of breath by which the driving function is shifted so as to maximize correlation is the correlation delay time (2 breaths for SV = 0.72 and 6 breaths for SV = 0.12). Only the first 60 breaths are represented here, yet the entire series (220 breaths) was used in the computation of the correlation delay time. B: translation function from measured correlation delay time (y-axis) to SV for 2 different values of delay due to plumbing—zero delay (continuous line) and 1-breath delay (dashed line). The addition of a plumbing delay resulted, as expected, in a parallel vertical displacement of the curve.

Fig. 3.

Time series of signal intensity for a single voxel. When the subject changed from breathing air to oxygen, signal intensity increased. Fi_O2 is represented by the dashed line. Once the extrinsic plumbing delay has been accounted for, the correlation delay time (expressed in number of breaths) between the Fi_O2 driving function and each voxel's time series is a quantitative measure of ventilation. Units with higher SV equilibrate faster; thus signal intensity increases faster, and correlation delay time is shorter, than for units with lower SV. With application of the modeling and signal-processing algorithm described in methods, different correlation delay times were converted into a physiologically meaningful measure of SV. The correlation delay time for this voxel was 2 breaths, corresponding to a SV of 0.35 (P < 0.0001).

Fig. 4.

A: correlation delay time map (in number of breaths) in a sagittal slice of the right lung of a typical subject. B: corresponding SV map, using the “translation” shown in Fig. 2B. In this plane, the head is located on right and the diaphragm on left. The vector g indicates the direction of gravity. The subject was supine. Note the shorter correlation delay time in the dependent regions (A), which indicates a greater SV in these regions than in the nondependent lung (B).

Fig. 5.

SV per third of the lung, grouped by the vertical distance from the most dependent portion. ANOVA for repeated measures comparing all 3 regions showed a highly significant difference (F = 26.8, P < 0.0001). Post hoc testing (paired t-test) P values are shown (7 degrees of freedom, t values of 5.5, 2.4, and 5.8 for the dependent-intermediate, intermediate-nondependent, and dependent-nondependent region paired comparisons, respectively).

Fig. 6.

SV averaged along the isogravitational lines, in 1-cm bins, plotted vs. the vertical distance from the most dependent portion of the lung for the right lung slice. Height increases up along the y-axis. As different subjects have different lung heights, data were only plotted when all the subjects contributed to the average. A: SV plotted against height of the lung for a single subject (S8), with horizontal error bars corresponding to the variability (SD) present in each isogravitational bin. B: individual height profiles of mean SV for the 8 subjects studied, each determined as in A. C: average height profiles of SV, with error bars corresponding to intersubject variability. The dashed line corresponds to the average slope determined by averaging the individual slopes of the 8 individual height-SV profiles (B).