The interface between measurement and modeling of peripheral lung mechanics

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Abstract

The mechanical properties of the lung periphery are vital to the overall function of the whole organ, and play a key role in the symptomatology of many lung diseases. We first review the experimental methodologies that have been used to investigate peripheral lung mechanics, including the retrograde catheter, the alveolar capsule, the alveolar capsule oscillator, and the forced oscillation technique. We then discuss the interpretation of the data provided by these techniques in terms of inverse mathematical models of the lung, including the constant-phase model. Finally, we describe efforts to construct anatomically accurate forward models of the lung based on data from imaging modalities such as computed tomography and magnetic resonance imaging. Together, these various approaches have provided a great deal of information about the relative importance of the lung periphery in mechanical function in animal models of lung disease and in human patients. An increasing body of evidence indicates that constriction in this part of the lung is a crucial determinant of the severity of asthma.

Introduction

The primary function of the lung is to mediate the transport of oxygen and carbon dioxide between environment and blood. A singular attribute of the lung in this regard is that, over a remarkably short distance, it moves between the modest cross-section of the tracheal opening to the enormous cross-section of the blood–gas interface. This is achieved by a tree of bifurcating airways that, although becoming progressively smaller in the distal direction, increase geometrically in number. The result is that the total airway cross-section increases almost exponentially with distance into the lung, with a corresponding decrease in axial airflow velocity. Thus, while gas transport occurs by bulk flow in the proximal airways, in the peripheral airways the mode of transport switches over to diffusion down partial pressure gradients that continues all the way to the capillary blood.

The transformation of function along the airway tree from gas flow to gas exchange is gradual. Nevertheless, physiologists find it convenient to invoke the concepts of central versus peripheral lung as those regions dominated, respectively, by conduction versus diffusion. Perhaps the most important implication to arise from these concepts is the now classic view of the lung periphery as the “silent zone”. That is, because of the negligible axial airflow velocities in the very small airways, and their small combined flow resistance, it has been postulated that they may develop significant pathology before a noticeable decrement in overall lung function occurs. Accordingly, diagnosing the early stages of small airways disease has traditionally been thought to be problematic. On the other hand, recent evidence provided by new technologies in concert with computational models suggests that the lung periphery may play a more important role in common diseases such as asthma than was originally thought.

Additional complications are introduced by the fact that regional heterogeneities in mechanical function develop throughout the lung in disease when distal airways become narrowed to different extents. Global measures of mechanical lung function are affected by these heterogeneities in a manner and frequency range akin to the impact of viscoelastic properties of the parenchyma alone (Similowski and Bates, 1991), causing us to revise our concept of where the functional periphery of the lung actually begins. The mechanical properties of the lung periphery are thus vital to the overall function of the whole organ, and play a key role in the symptomatology of many lung diseases. In this article we review the experimental methodologies that have been used to investigate peripheral lung mechanics, and discuss the interpretation of the data they provide in terms of mathematical and computational models of the lung.

Section snippets

Probing the lung periphery

Assessing the mechanical properties of the lung is a question of investigating the dynamic relationships between appropriate pressures and flows. The information that is obtained depends on where these pressures and flows are measured, and on the amplitude and frequency ranges they span. Usually, both pressure and flow are measured at the airway opening during regular breathing or ventilation, and provide values for overall lung resistance and elastance. However, a number of variations on this

Inverse modeling of lung mechanics

The simplest and still most widely invoked anatomically based model of pulmonary mechanics is that consisting of a single homogeneously ventilated alveolar compartment served by a single conduit (Fig. 4, top). The equation of motion of this model is obtained by assigning an elastic constant (E) to the alveolar compartment and a flow-resistive constant (R) to the conduit so that the total transpulmonary pressure (Ptp) across the system isPtp(t)=RV˙(t)+EV(t)+P0where P0 is the pressure across the

Anatomically accurate forward modeling of the lung periphery

The modern era of sophisticated forward modeling of lung mechanics was ushered by Fredberg and Hoenig (1978). These investigators incorporated the anatomy of the airway tree elucidated by Horsfield et al. (1982) into a computational model to predict the input impedance of the lung up to several thousand Hz. The Horsfield anatomy can be encapsulated in a very efficient recursive algorithm that captures the asymmetrical nature of the airway tree, and which can be easily adapted to different

Conclusions

Our understanding of the role of the lung periphery in determining overall lung function proceeds through the continual interplay between computational modeling and experiment, each supporting and extending the other. Models are used both in the inverse sense as vehicles for extracting physiological insight from experimental data, and in the forward sense as virtual laboratories for the testing of specific hypothesis about mechanism. The success of this endeavor depends critically on the

Acknowledgements

The authors acknowledge the financial support of NIH grants NCRR P20 RR15557, R01 HL62743, RO1 HL076778-01, and NIH EB-001689-02 and R33 EB001689. The authors thank Nora Tgavalekos for her contributions to the 3D image-based modeling analysis. Dr. Mitchell Albert's Laboratory developed and applied the Hyperpolarized Helium MRI system and data for this study. Dr. Jose Venegas developed the PET system that provided the images used.

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