Probing airway conditions governing ventilation defects in asthma via hyperpolarized MRI image functional modeling

Abstract

Image functional modeling (IFM) has been introduced as a method to simultaneously synthesize imaging and mechanical data with computational models to determine the degree and location of airway constriction in asthma. Using lung imaging provided by hyperpolarized (3)He MRI, we advanced our IFM method to require matching not only to ventilation defect location but to specific ventilation throughout the lung. Imaging and mechanical data were acquired for four healthy and four asthmatic subjects pre- and postbronchial challenge. After provocation, we first identified maximum-size airways leading exclusively to ventilation defects and highly constricted them. Constriction patterns were then found for the remaining airways to match mechanical data. Ventilation images were predicted for each pattern, and visual and statistical comparisons were done with measured data. Results showed that matching of ventilation defects requires severe constriction of small airways. The mean constriction of such airways leading to the ventilation defects needed to be 70-80% rather than fully closed. Also, central airway constriction alone could not account for dysfunction seen in asthma, so small airways must be involved.

Figures

Fig. 1.

Schematic depiction of process to create a model-predicted image slice of ventilation distribution at the same resolution as an actual image. An actual ventilation image (A) is used to determine where there are data in the lung field. A corresponding model slice (B) depicts predicted ventilation to each terminal airway in the slice. The terminal airway slice is converted to the same resolution as the data by averaging the nonzero pixels in the surrounding box, as shown at right. This is repeated recursively for the entire model slice (C).

Fig. 2.

A binary baseline mask (A) is used to map the terminal branch locations into the lung region defined by the mask. This terminal branch map (B) is then multiplied by the postchallenge mask (C) to determine the ventilated (white) and nonventilated (orange) terminal branches postchallenge (D).

Fig. 3.

Model airway tree for asthmatic subject A2 postbronchoconstriction with methacholine (left) and postalbuterol (right). In black are airways that are ventilated; in red are airways that are not being ventilated due to methacholine. Visually, there are a large number of airways that are unventilated; after albuterol, some airways are recovered, but not all.

Fig. 4.

Measured and predicted lung resistance (Rl) and elastance (El) as a function of frequency for asthmatic subject A2. Data were measured at baseline data and postchallenge, and the postchallenge model was fit with 90% constriction applied to ventilation defects vs. 70% constriction.

Fig. 5.

Cumulative distribution function of ventilation for a real image postchallenge and for several model predictions. Without any constriction imposed (baseline), the model predicts nearly homogenous ventilation distribution with the majority of the pixels around 0.25. In the postchallenge model, when 90% constriction is applied to ventilation defects, the model substantially overpredicts the amount of low-ventilated voxels compared with real data. When constriction leading to defects is reduced to 70%, there is a better match to low-ventilating units.

Fig. 6.

Real and predicted images of 3 middle lung slices for asthmatic subject A2. The real image (top) has a diversity of bright colors representing high ventilation and less darker colors representing poor ventilation (i.e., clear heterogeneous ventilation). The simulated image with 90% constriction to defects (middle) has more poorly ventilating units than the real image. Simulating an image with 70% constriction (bottom) to defects does not change the image much from that for 90%. Red circles highlight differences between the model predictions.

Fig. 7.

Pooled results of image functional modeling (IFM) for the best match to both mechanics and ventilation for healthy and asthmatic subjects. The mean and standard deviation of constriction are shown for airways leading to ventilation defects, airways outside of ventilation defects, and all airways in the tree. Slightly larger mean constriction was seen in all asthmatic airways; however, no significant differences were seen between healthy and asthmatic subjects.