Automated quantification of microvascular perfusion

Abstract

Objective: Changes in microvascular perfusion have been reported in many diseases, yet the functional significance of altered perfusion is often difficult to determine. This is partly because commonly used techniques for perfusion measurement often rely on either indirect or by-hand approaches.

Methods: We developed and validated a fully automated software technique to measure microvascular perfusion in videos acquired by fluorescence microscopy in the mouse gastrocnemius. Acute perfusion responses were recorded following intravenous injections with phenylephrine, SNP, or saline.

Results: Software-measured capillary flow velocity closely correlated with by-hand measured flow velocity (R² = 0.91, P < 0.0001). Software estimates of capillary hematocrit also generally agreed with by-hand measurements (R² = 0.64, P < 0.0001). Detection limits range from 0 to 2000 μm/s, as compared to an average flow velocity of 326 ± 102 μm/s (mean ± SD) at rest. SNP injection transiently increased capillary flow velocity and hematocrit and made capillary perfusion more steady and homogenous. Phenylephrine injection had the opposite effect in all metrics. Saline injection transiently decreased capillary flow velocity and hematocrit without influencing flow distribution or stability. All perfusion metrics were temporally stable without intervention.

Conclusions: These results demonstrate a novel and sensitive technique for reproducible, user-independent quantification of microvascular perfusion.

Keywords: capillary recruitment; computational image processing; intravital microscopy; microvascular perfusion; nitric oxide.

Figure 1:

Stabilization of raw microscope videos. A) Intensity prominence of the first frame (green) and a subsequent frame (red), showing a translational offset. B) 2D Cross-correlation reveals a maximum agreement at the appropriate offset between consecutive frames, allowing correction for motion artifacts. C) Following stabilization, the number of vessels with adequate signal-noise ratio for inclusion in final analysis significantly increases (p<0.0001).

Figure 2:

Detection of vessel segments suitable for tracking. A) Time-average of plasma fluorescence in the stabilized video. B) Fluorescence intensity prominence (red, defined as percent brighter than 5-pixel neighborhood average intensity) and intensity gradient (green) are used to identify plasma perfused capillaries. C) Under/overexposed regions (green) are discarded, and suitable capillary segments for tracking (blue) are identified.

Figure 3:

Example of cross-correlation measurement of capillary flow velocity. A) Corrected fluorescence intensity profiles along the centerline of an example capillary segment in two consecutive frames, showing similar features with an axial offset. B) Average of the 20% highest relative agreement cross-correlation curves corresponding to the same capillary segment as in panel A, showing a clear peak at velocity = 800 μm/s. C) Individual frame-frame observations converge to an average value of velocity = 800 μm/s for the 20% of frames with the highest signal/noise ratio in this example, but do not converge for frames with poor signal/noise ratio. Note that the flow velocity in this example is faster than average and was used to exemplify the case during a situation requiring high rigor. It is a computationally more difficult measurement due to increased risk of blurring and aliasing.

Figure 4:

Examples of binarized space-time images used to estimate capillary hematocrit. The left panel in each example image pair is the raw space-time image corresponding to each capillary. Plasma gaps are bright, while RBCs are dark. Thus, RBC flow produces diagonal dark streaks in the space-time image, and the slope of these streaks corresponds to flow velocity. The right panel in each example pair is the binarized version of the space-time image, where RBCs are shown as white, while plasma gaps are shown as black. The fraction of the binarized space-time image filled by RBCs is used as an estimate of capillary discharge hematocrit.

Figure 5:

By-hand measurement techniques used for software validation. A) Visible objects (either bright or dark deviations) were tracked by hand, and for each object, the total distance travelled and time elapsed were recorded. B) In the intensity prominence image produced for each video, three lines were drawn perpendicular to the prevailing orientation of the capillary network, and the average number of vessel intersections per mm was recorded. Note that the flow velocity in this example is unusually fast, and thus represents a case in which blurring of flowing cells and plasma gaps makes velocity measurement particularly challenging.

Figure 6:

Comparison of software and by-hand measurements. A) 5-second average of capillary flow velocity as measured by hand is consistent with 5-second average of capillary flow velocity as measured using our software technique (R²=0.91, p<00001). Red indicates values affected by software detection limits. Slope of the regression line shown is slightly less than unity due to these values. B) Maximum flow velocity recorded within each vessel as measured by hand is consistent with maximum flow velocity as measured using our software technique (R²=0.85, p<0.0001). C) Minimum flow velocity recorded within each vessel as measured by hand is consistent with minimum flow velocity as measured using our software technique (R²=0.87, p<0.0001). D) Capillary hematocrit as measured by hand is consistent with capillary hematocrit as estimated using our software technique (R²=0.64, p<00001), although the dynamic range of hematocrit measurements is underestimated by the software, as reflected by a slope less than unity in the regression line. E) Statistically significant bias (p<0.05, mean difference 9.4%) was detected comparing 5-second average of capillary flow velocity as measured by hand and by software, but this bias was absent (p=NS) for measurements within software detection limits (see Section 3.2). F) No significant bias was found in software measurements of capillary density (p=NS).

Figure 7:

Sensitivity of software measurements to image noise and exposure time. A) Addition of 20% noise induces a small but statistically significant (p<0.0001) decrease in number of vessels with adequate SNR, whereas addition of 80% noise induces a large, statistically significant (p<0.0001) decrease in the number of vessels with adequate SNR. B) MFV as measured in the raw microscope videos correlates strongly with MFV as measured in videos with 20% added noise (R²=0.94, p<0.0001), but not in videos with 80% added noise (R²=0.05, p=NS). C) PHI as measured in the raw microscope videos correlates strongly with PHI as measured in videos with 20% added noise (R²=0.68, p<0.0001). D) PLI as measured in the raw microscope videos correlates strongly with PLI as measured in videos with 20% added noise (R²=0.90, p<0.0001). E) MFV as measured in 20ms exposure time videos is equivalent to that observed in 10ms exposure time videos up to a maximum capillary flow velocity of V=1000 μm/s, above which 20ms exposure time causes underestimation of MFV. F) Increasing exposure time from 10ms to 20ms significantly decreases the number of vessels with adequate SNR (p<0.0001).

Figure 8:

Software characterization of acute microcirculatory changes in response to an intravenous 0.45 mg/kg SNP bolus injection. Data presented as mean ± SEM. A) MFV significantly increased (p<0.0001) within the first two minutes of SNP injection and then returned to baseline within 5 minutes. B) PHI significantly decreased (p<0.001) within the first two minutes following SNP injection and then returned to baseline within 5 minutes. C) PLI significantly decreased for the first two minutes following SNP injection (p<0.0001), and then returned to baseline within 5 minutes. D) PPV significantly increased within 1 minute following SNP bolus injection (p<0.05) and then returned to baseline within 5 minutes. E) Mean capillary hematocrit significantly increased (p<0.05) within the first two minutes of SNP injection and then returned to baseline within 5 minutes. F) Hematocrit heterogeneity significantly decreased (p<0.05) within 1 minute following SNP bolus injection and then returned to baseline within 5 minutes.

Figure 9:

Software characterization of acute microcirculatory changes in response to an intravenous 0.35 mg/kg phenylephrine bolus injection. Data presented as mean ± SEM. A) MFV significantly decreased (p<0.001) during the first two minutes following phenylephrine injection and then returned to baseline within 5 minutes. B) PHI significantly rose (p<0.0001) during the first two minutes following phenylephrine injection and then returned to baseline within 5 minutes. C) PLI significantly increased (p<0.0001) during the first two minutes following phenylephrine injection and then returned to baseline within 5 minutes. D) PPV significantly decreased (p<0.001) during the first two minutes following phenylephrine injection and then returned to baseline within 5 minutes. E) Mean capillary hematocrit significantly decreased (p<0.05) within 1 minute following phenylephrine injection and then returned to baseline within 5 minutes. F) Hematocrit heterogeneity significantly increased (p<0.001) during the first three minutes following phenylephrine injection and then returned to baseline within five minutes.

Figure 10:

Software characterization of acute microcirculatory changes in response to an intravenous bolus 1.67 mL/kg injection of saline. Data presented as mean ± SEM. A) MFV significantly decreased for the first two minutes following injection of saline (p<0.05), and then returned to baseline within 5 minutes. B) PHI did not significantly change in response to saline injection (p=NS). C) PLI did not significantly change in response saline injection (p=NS). D) PPV did not significantly change in response to saline injection (p=NS). E) Mean capillary hematocrit significantly decreased (p<0.01) within 1 minute of saline injection and then returned to baseline within 5 minutes. F) Hematocrit heterogeneity did not significantly change in response to saline injection (p=NS).

Figure 11:

Software characterization of microvascular perfusion time course without intervention. Data presented as mean ± SEM. A) MFV does not significantly change over time without intervention (p=NS). B) PHI does not significantly change over time without intervention (p=NS). C) PLI does not significantly change over time without intervention (p=NS). D) PPV does not significantly change over time without intervention (p=NS). E) The number of vessels with adequate SNR for flow measurement significantly decreases over time (p<0.001). The fraction of the FOV with appropriate illumination for flow tracking significantly decreases over time (p<0.05).

Figure 12:

Changes in blood pressure in response to vasoactive drugs. Data presented as mean ± SEM. A) Blood pressure underwent a small (<3 mmHg) but statistically significant (p<0.001) transient increase in blood pressure between injection of fluorescent dextran (Rho-Dex) and injection with 0.45 mg/kg SNP, 0.35 mg/kg phenylephrine, or 0.9% saline (time 0 minutes through time 3 minutes in panel A). B) Saline injection produced a small, transient decrease in blood pressure (~5 mmHg, p<0.01), whereas SNP injection caused a large, transient decrease in blood pressure (~37 mmHg, p<0.0001) and phenylephrine caused a large, transient increase in blood pressure (~47 mmHg, p<0.0001).