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Spatial Distribution of Ventilation and Perfusion: Mechanisms and Regulation

Robb W. Glenny, H. Thomas Robertson

10.1002/cphy.c100002

Source: Volume 1, Issue 1, January 2011

Published online: January 2011

Full Article on Wiley Online Library



Abstract

With increasing spatial resolution of regional ventilation and perfusion, it has become more apparent that ventilation and blood flow are quite heterogeneous in the lung. A number of mechanisms contribute to this regional variability, including hydrostatic gradients, pleural pressure gradients, lung compressibility, and the geometry of the airway and vascular trees. Despite this marked heterogeneity in both ventilation and perfusion, efficient gas exchange is possible through the close regional matching of the two. Passive mechanisms, such as the shared effect of gravity and the matched branching of vascular and airway trees, create efficient gas exchange through the strong correlation between ventilation and perfusion. Active mechanisms that match local ventilation and perfusion play little if no role in the normal healthy lung but are important under pathologic conditions. © 2011 American Physiological Society. Compr Physiol 1:373‐395, 2011.

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Figure 1. Figure 1.

Left – Gravitationally mediated gradient of alveolar size at FRC due to lung compression as the bases. Right – when the lung is expanded to TLC, the alveolar sizes become uniform throughout the lung. With inspiration, the greatest change in regional lung volume will occur in the dependent lung regions.
Figure 2. Figure 2.

Slopes representing the gravitational gradients of deposition of inhaled Tc‐99m aerosol administered in either supine or prone posture, with the slope representing the regional activity observed in supine (solid line) or prone (dotted line) postures (from Petersson et al (90).
Figure 3. Figure 3.

Regional ventilation in four transverse sections of lung measured by CT/Xe in prone and supine positions, with measurement in each section plotted as a function of distance from the dependent surface (74).
Figure 4. Figure 4.

Regional ventilation in prone and supine positions marked by fluorescent microsphere aerosol deposition with per‐piece ventilation plotted as a function of the distance from the most dependent lung surface (84).
Figure 5. Figure 5.

Average log of supine to prone ventilation ratios plotted along the caudo‐cranial axis in seven pigs, with separate slopes calculated for above diaphragm and below diaphragm data (2).
Figure 6. Figure 6.

The coefficient of variation (CV) for regional ventilation and regional blood flow in a pig, where the log CV is linearly related to the log of the lung piece size used to make the calculation of CV (1).
Figure 7. Figure 7.

Starling resistor model applied to pulmonary circulation. The pulmonary capillaries are collapsible and surrounded by the alveolar pressure (PA). If PA is greater than the pressure within the capillaries (Pa), they will completely collapse and flow will stop. If PA is less than Pa, flow will occur but the driving pressure is Pa − PA. When PA is less than the venous pressure (Pv), flow occurs and the driving pressure is Pa − Pv.
Figure 8. Figure 8.

Initial 3 zone model of pulmonary perfusion popularized by John West (125). Reproduced with permission from (125).
Figure 9. Figure 9.

The relationships between Pa, Pv, and PA create regional differences in pulmonary perfusion. The zones have been traditionally vertically stacked on top of each other. However, recent observations that perfusion is heterogeneous within isogravitational planes demonstrates that zonal conditions may also vary within horizontal planes. The numbers of different zones within each plane likely shifts with increasing hydrostatic pressure down the lung from predominantly zones 1 and 2 at the top of the lung to all zone 3 conditions in the dependant lung regions.
Figure 10. Figure 10.

Left – color‐coded map of blood flow distribution within an isogravitational plane. Note that the distribution is not random in space, but rather that high‐flow regions are near other high‐flow regions and low‐flow areas neighbor other low‐flow areas. Right – a dichotomously branching vascular tree with slight asymmetry of resistance at each branch creates heterogeneous blood flow distributions that are spatially correlated due to the shared heritage up the tree among neighboring regions.
Figure 11. Figure 11.

The vertical gradient of blood flow in the lung is influenced by gravity. In this experiment, blood flow to nearly 1500 lung pieces was determined within the same animal during 2G supine, 0G supine and 2G prone conditions. It is clear that gravity redistributes blood flow in the direction expected.
Figure 12. Figure 12.

Blood flow to nearly 1500 lung pieces within the same animal under 2G supine and 2G prone conditions. Note that despite the large difference in gravity and posture, high‐flow piece remain high‐flow and low‐flow piece remain low‐flow.
Figure 13. Figure 13.

Blood flow as a function of height up the lung in an upright primate. Data are from 1,265 pieces of lung (2 cm3 in volume). Left, data averaged within horizontal planes to reproduce the spatial resolution available at the time the gravitational model was conceptualized. Right, same data but at a resolution that permits the heterogeneity of perfusion to be observed. At the lower spatial resolution, the data are remarkably similar to those of Hughes and West (64) and gravity appears to be a major determinant of perfusion (r2 = 0.640). However, at the higher resolution, gravity can account for at most 28% of the variability in perfusion. Reproduced with permission from (38).
Figure 14. Figure 14.

Variability in the V/Q ratio as a function of height up the lung and posture. There is a clear vertical gradient in the supine but not the prone posture. From (84).
Figure 15. Figure 15.

Shared effect of gravity on ventilation and perfusion matching. Gravity imposes a vertical gradient on both ventilation and perfusion (left) grossly matching ventilation and perfusion. However, the vertical gradient of perfusion is relatively larger resulting in some ventilation to perfusion inequality at the top and bottom of lung. West and colleagues proposed that this V/Q heterogeneity down the lung accounted for the observed A‐aO2 difference in the normal lung. Adapted from (124).
Figure 16. Figure 16.

Graphical presentations of ventilation and perfusion distributions from one animal in which the lung was dissected into 850 pieces. Upper right panel plots the ventilation and perfusion to each piece against each other. The heterogeneity of ventilation can be appreciated by collapsing the ventilation data to the vertical axis and then viewing as a frequency density distribution (upper left panel). The heterogeneity of perfusion can be viewed similarly by collapsing the data to the horizontal axis (lower right panel). Despite the heterogeneous distributions of ventilation and perfusion, their piece by piece matching is quite good (r = 0.80, upper right panel). The upper right panel includes isopleths of V/Q ratios from which gas exchange can be inferred. The resultant ventilation and perfusion distributions with respect to the V/Q ratio, as popularized by the multiple inert gas technique (MIGET), are presented in the lower left panel.


Figure 1.

Left – Gravitationally mediated gradient of alveolar size at FRC due to lung compression as the bases. Right – when the lung is expanded to TLC, the alveolar sizes become uniform throughout the lung. With inspiration, the greatest change in regional lung volume will occur in the dependent lung regions.



Figure 2.

Slopes representing the gravitational gradients of deposition of inhaled Tc‐99m aerosol administered in either supine or prone posture, with the slope representing the regional activity observed in supine (solid line) or prone (dotted line) postures (from Petersson et al (90).



Figure 3.

Regional ventilation in four transverse sections of lung measured by CT/Xe in prone and supine positions, with measurement in each section plotted as a function of distance from the dependent surface (74).



Figure 4.

Regional ventilation in prone and supine positions marked by fluorescent microsphere aerosol deposition with per‐piece ventilation plotted as a function of the distance from the most dependent lung surface (84).



Figure 5.

Average log of supine to prone ventilation ratios plotted along the caudo‐cranial axis in seven pigs, with separate slopes calculated for above diaphragm and below diaphragm data (2).



Figure 6.

The coefficient of variation (CV) for regional ventilation and regional blood flow in a pig, where the log CV is linearly related to the log of the lung piece size used to make the calculation of CV (1).



Figure 7.

Starling resistor model applied to pulmonary circulation. The pulmonary capillaries are collapsible and surrounded by the alveolar pressure (PA). If PA is greater than the pressure within the capillaries (Pa), they will completely collapse and flow will stop. If PA is less than Pa, flow will occur but the driving pressure is Pa − PA. When PA is less than the venous pressure (Pv), flow occurs and the driving pressure is Pa − Pv.



Figure 8.

Initial 3 zone model of pulmonary perfusion popularized by John West (125). Reproduced with permission from (125).



Figure 9.

The relationships between Pa, Pv, and PA create regional differences in pulmonary perfusion. The zones have been traditionally vertically stacked on top of each other. However, recent observations that perfusion is heterogeneous within isogravitational planes demonstrates that zonal conditions may also vary within horizontal planes. The numbers of different zones within each plane likely shifts with increasing hydrostatic pressure down the lung from predominantly zones 1 and 2 at the top of the lung to all zone 3 conditions in the dependant lung regions.



Figure 10.

Left – color‐coded map of blood flow distribution within an isogravitational plane. Note that the distribution is not random in space, but rather that high‐flow regions are near other high‐flow regions and low‐flow areas neighbor other low‐flow areas. Right – a dichotomously branching vascular tree with slight asymmetry of resistance at each branch creates heterogeneous blood flow distributions that are spatially correlated due to the shared heritage up the tree among neighboring regions.



Figure 11.

The vertical gradient of blood flow in the lung is influenced by gravity. In this experiment, blood flow to nearly 1500 lung pieces was determined within the same animal during 2G supine, 0G supine and 2G prone conditions. It is clear that gravity redistributes blood flow in the direction expected.



Figure 12.

Blood flow to nearly 1500 lung pieces within the same animal under 2G supine and 2G prone conditions. Note that despite the large difference in gravity and posture, high‐flow piece remain high‐flow and low‐flow piece remain low‐flow.



Figure 13.

Blood flow as a function of height up the lung in an upright primate. Data are from 1,265 pieces of lung (2 cm3 in volume). Left, data averaged within horizontal planes to reproduce the spatial resolution available at the time the gravitational model was conceptualized. Right, same data but at a resolution that permits the heterogeneity of perfusion to be observed. At the lower spatial resolution, the data are remarkably similar to those of Hughes and West (64) and gravity appears to be a major determinant of perfusion (r2 = 0.640). However, at the higher resolution, gravity can account for at most 28% of the variability in perfusion. Reproduced with permission from (38).



Figure 14.

Variability in the V/Q ratio as a function of height up the lung and posture. There is a clear vertical gradient in the supine but not the prone posture. From (84).



Figure 15.

Shared effect of gravity on ventilation and perfusion matching. Gravity imposes a vertical gradient on both ventilation and perfusion (left) grossly matching ventilation and perfusion. However, the vertical gradient of perfusion is relatively larger resulting in some ventilation to perfusion inequality at the top and bottom of lung. West and colleagues proposed that this V/Q heterogeneity down the lung accounted for the observed A‐aO2 difference in the normal lung. Adapted from (124).



Figure 16.

Graphical presentations of ventilation and perfusion distributions from one animal in which the lung was dissected into 850 pieces. Upper right panel plots the ventilation and perfusion to each piece against each other. The heterogeneity of ventilation can be appreciated by collapsing the ventilation data to the vertical axis and then viewing as a frequency density distribution (upper left panel). The heterogeneity of perfusion can be viewed similarly by collapsing the data to the horizontal axis (lower right panel). Despite the heterogeneous distributions of ventilation and perfusion, their piece by piece matching is quite good (r = 0.80, upper right panel). The upper right panel includes isopleths of V/Q ratios from which gas exchange can be inferred. The resultant ventilation and perfusion distributions with respect to the V/Q ratio, as popularized by the multiple inert gas technique (MIGET), are presented in the lower left panel.

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Article Title: Spatial Distribution of Ventilation and Perfusion: Mechanisms and Regulation
 

How to Cite

Robb W. Glenny, H. Thomas Robertson. Spatial Distribution of Ventilation and Perfusion: Mechanisms and Regulation. Compr Physiol 2011, 1: 373-395. doi: 10.1002/cphy.c100002