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Distribution of Perfusion

Robb Glenny, H. Thomas Robertson

10.1002/cphy.c100012

Source: Volume 1, Issue 1, January 2011

Published online: January 2011

Full Article on Wiley Online Library



Abstract

Local driving pressures and resistances within the pulmonary vascular tree determine the distribution of perfusion in the lung. Unlike other organs, these local determinants are significantly influenced by regional hydrostatic and alveolar pressures. Those effects on blood flow distribution are further magnified by the large vertical height of the human lung and the relatively low intravascular pressures in the pulmonary circulation. While the distribution of perfusion is largely due to passive determinants such as vascular geometry and hydrostatic pressures, active mechanisms such as vasoconstriction induced by local hypoxia can also redistribute blood flow. This chapter reviews the determinants of regional lung perfusion with a focus on vascular tree geometry, vertical gradients induced by gravity, the interactions between vascular and surrounding alveolar pressures, and hypoxic pulmonary vasoconstriction. While each of these determinants of perfusion distribution can be examined in isolation, the distribution of blood flow is dynamically determined and each component interacts with the others so that a change in one region of the lung influences the distribution of blood flow in other lung regions. © 2011 American Physiological Society. Compr Physiol 1:245‐262, 2011.

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Figure 1. Figure 1.

Schematic of longitudinal resistances across arterial (RA), capillary (RC), and venous (RV) compartments in the lung. Flow (F) to a given lung region is determined by the relative longitudinal resistances through that region. The greatest flows will be to regions with relatively lower resistances.
Figure 2. Figure 2.

Resin cast of the human airway showing the dichotomous branching. In the left lung, the pulmonary arteries and veins are also shown in red and blue, respectively. Generously provided by Dr. Ewald Weibel, Institute of Anatomy, University of Berne, Berne, Switzerland.
Figure 3. Figure 3.

Dichotomous branching model. (Left) Basic transformation is the dichotomous branching of each terminal element. The asymmetric branching of the terminal branch divides flow into two fractions, γ and 1 − γ that are distributed to the daughter branches. Following a second iteration in which all terminal nodes branch again, blood flow is now distributed to four terminal branches.

With permission from Glenny and Robertson 41
Figure 4. Figure 4.

Frequency distribution of pulmonary blood flow in a laboratory animal. Note the rightward skew to the distribution consistent with a log‐normal distribution (fitted line).
Figure 5. Figure 5.

Frequency distributions of blood flow created with a dichotomous branching model similar to Figure 3. (Left) Flow distribution at the terminal branches after five generations (64 pieces). (Right) Flow distribution at the terminal branches after 10 generations (1024 pieces). Note the apparently greater heterogeneity revealed at the smaller piece size despite the flows being determined by the same branching asymmetry. This demonstrates that the scale dependence of flow heterogeneity measurements is a basic characteristic of all progressively branching distribution systems.
Figure 6. Figure 6.

(Left) Visual map of blood flow to ∼2‐cm3 lung pieces within a horizontal plane of a supine dog. Note the large heterogeneity of perfusion and the spatial organization with high‐flow regions near other high‐flow regions and low‐flow areas neighboring other low‐flow areas. (Right) Vascular tree with asymmetrical branching that leads to neighboring regions having similar flows.

Reproduced with permission from Glenny 31
Figure 7. Figure 7.

Correlation in blood flow between lung pieces as a function of distance between pieces. Neighboring regions (centers of regions separated by 1.2 cm) were highly correlated (r = 0.676). Correlation between regions decreased with distance, eventually becoming negatively correlated. Closed circles are significantly different from a correlation of r = 0.0.

Reproduced with permission from Glenny et al. 30
Figure 8. Figure 8.

Scanning electron micrograph of alveolar capillaries from a rat lung. The bar indicates 50 μm. B V indicates a large blood vessel.

Reproduced with permission from Guntheroth 46
Figure 9. Figure 9.

The temporal variability of pulmonary blood flow is spatially clustered. Lung pieces with similar temporal patterns are near each other. In addition, there are complementary patterns in which blood flow increases to one region at the expense of another region. These observations suggested that most of the variability in blood flow occurs at the level of lobar arteries 37.
Figure 10. Figure 10.

Per piece pulmonary perfusion distribution is relatively fixed with both decreasing cardiac output (tilt) and increasing cardiac output (exercise) during either exercise or decreased cardiac output (tilt) compared to resting blood flow.

Reprinted with permission from Parker et al. 79
Figure 11. Figure 11.

Initial studies used external scintillation counters on the chest wall to estimate regional blood flow and ventilation. (Left) These external counters averaged flow within isogravitational planes.

Adapted from Ball et al. 6. (Right) These studies revealed a vertical distribution of both ventilation and perfusion with increasing flows down the lung. Adapted from West 108
Figure 12. Figure 12.

Initial three‐zone model of pulmonary perfusion popularized by John West 109.

Reproduced with permission from West et al. 109
Figure 13. Figure 13.

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 dependent lung regions.

Reproduced with permission from Glenny and Robertson 39
Figure 14. Figure 14.

Distribution of pulmonary blood flow with respect to height up the lung in the supine, prone, and upright postures. Blood flow was marked in each posture while imaging was performed in the supine posture for all measures so that the confounding effects of parenchymal redistribution were nullified.

Adapted with permission from Okada et al. 77
Figure 15. Figure 15.

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 2‐G supine, 0‐G supine, and 2‐G prone conditions. It is clear that gravity redistributes blood flow in the direction expected.
Figure 16. Figure 16.

Blood flow to nearly 1500 lung pieces within the same animal under 2‐G supine and 2‐G 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 17. Figure 17.

Blood flow as a function of height up the lung in an upright primate. Data are from 1265 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 within isogravitational planes to be observed. At the lower spatial resolution, the data are remarkably similar to those of West 97 and gravity appears to be a major determinant of perfusion (r2 = 0.64). However, at the higher resolution, gravity can account for at most 28% of the variability in perfusion.

Reproduced with permission from Fung and Sobin 25
Figure 18. Figure 18.

The hypoxic pulmonary vasoconstriction (HPV) response to global hypoxia varies within regions of the lung. In this study by Hlastala et al., lung pieces (∼2 cm3 in volume) were clustered into groups defined by changes in the blood flow to each piece with gradated hypoxia. The clusters are color coded and then represented in their spatial location above. Note that pieces with a similar HPV response are grouped together.

Adapted with permission from Hillier et al. 51


Figure 1.

Schematic of longitudinal resistances across arterial (RA), capillary (RC), and venous (RV) compartments in the lung. Flow (F) to a given lung region is determined by the relative longitudinal resistances through that region. The greatest flows will be to regions with relatively lower resistances.



Figure 2.

Resin cast of the human airway showing the dichotomous branching. In the left lung, the pulmonary arteries and veins are also shown in red and blue, respectively. Generously provided by Dr. Ewald Weibel, Institute of Anatomy, University of Berne, Berne, Switzerland.



Figure 3.

Dichotomous branching model. (Left) Basic transformation is the dichotomous branching of each terminal element. The asymmetric branching of the terminal branch divides flow into two fractions, γ and 1 − γ that are distributed to the daughter branches. Following a second iteration in which all terminal nodes branch again, blood flow is now distributed to four terminal branches.

With permission from Glenny and Robertson 41


Figure 4.

Frequency distribution of pulmonary blood flow in a laboratory animal. Note the rightward skew to the distribution consistent with a log‐normal distribution (fitted line).



Figure 5.

Frequency distributions of blood flow created with a dichotomous branching model similar to Figure 3. (Left) Flow distribution at the terminal branches after five generations (64 pieces). (Right) Flow distribution at the terminal branches after 10 generations (1024 pieces). Note the apparently greater heterogeneity revealed at the smaller piece size despite the flows being determined by the same branching asymmetry. This demonstrates that the scale dependence of flow heterogeneity measurements is a basic characteristic of all progressively branching distribution systems.



Figure 6.

(Left) Visual map of blood flow to ∼2‐cm3 lung pieces within a horizontal plane of a supine dog. Note the large heterogeneity of perfusion and the spatial organization with high‐flow regions near other high‐flow regions and low‐flow areas neighboring other low‐flow areas. (Right) Vascular tree with asymmetrical branching that leads to neighboring regions having similar flows.

Reproduced with permission from Glenny 31


Figure 7.

Correlation in blood flow between lung pieces as a function of distance between pieces. Neighboring regions (centers of regions separated by 1.2 cm) were highly correlated (r = 0.676). Correlation between regions decreased with distance, eventually becoming negatively correlated. Closed circles are significantly different from a correlation of r = 0.0.

Reproduced with permission from Glenny et al. 30


Figure 8.

Scanning electron micrograph of alveolar capillaries from a rat lung. The bar indicates 50 μm. B V indicates a large blood vessel.

Reproduced with permission from Guntheroth 46


Figure 9.

The temporal variability of pulmonary blood flow is spatially clustered. Lung pieces with similar temporal patterns are near each other. In addition, there are complementary patterns in which blood flow increases to one region at the expense of another region. These observations suggested that most of the variability in blood flow occurs at the level of lobar arteries 37.



Figure 10.

Per piece pulmonary perfusion distribution is relatively fixed with both decreasing cardiac output (tilt) and increasing cardiac output (exercise) during either exercise or decreased cardiac output (tilt) compared to resting blood flow.

Reprinted with permission from Parker et al. 79


Figure 11.

Initial studies used external scintillation counters on the chest wall to estimate regional blood flow and ventilation. (Left) These external counters averaged flow within isogravitational planes.

Adapted from Ball et al. 6. (Right) These studies revealed a vertical distribution of both ventilation and perfusion with increasing flows down the lung. Adapted from West 108


Figure 12.

Initial three‐zone model of pulmonary perfusion popularized by John West 109.

Reproduced with permission from West et al. 109


Figure 13.

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 dependent lung regions.

Reproduced with permission from Glenny and Robertson 39


Figure 14.

Distribution of pulmonary blood flow with respect to height up the lung in the supine, prone, and upright postures. Blood flow was marked in each posture while imaging was performed in the supine posture for all measures so that the confounding effects of parenchymal redistribution were nullified.

Adapted with permission from Okada et al. 77


Figure 15.

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 2‐G supine, 0‐G supine, and 2‐G prone conditions. It is clear that gravity redistributes blood flow in the direction expected.



Figure 16.

Blood flow to nearly 1500 lung pieces within the same animal under 2‐G supine and 2‐G 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 17.

Blood flow as a function of height up the lung in an upright primate. Data are from 1265 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 within isogravitational planes to be observed. At the lower spatial resolution, the data are remarkably similar to those of West 97 and gravity appears to be a major determinant of perfusion (r2 = 0.64). However, at the higher resolution, gravity can account for at most 28% of the variability in perfusion.

Reproduced with permission from Fung and Sobin 25


Figure 18.

The hypoxic pulmonary vasoconstriction (HPV) response to global hypoxia varies within regions of the lung. In this study by Hlastala et al., lung pieces (∼2 cm3 in volume) were clustered into groups defined by changes in the blood flow to each piece with gradated hypoxia. The clusters are color coded and then represented in their spatial location above. Note that pieces with a similar HPV response are grouped together.

Adapted with permission from Hillier et al. 51
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Article Title: Distribution of Perfusion
 

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Robb Glenny, H. Thomas Robertson. Distribution of Perfusion. Compr Physiol 2011, 1: 245-262. doi: 10.1002/cphy.c100012