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In vivo flow mapping in complex vessel networks by single image correlation.

Sironi L, Bouzin M, Inverso D, D'Alfonso L, Pozzi P, Cotelli F, Guidotti LG, Iannacone M, Collini M, Chirico G - Sci Rep (2014)

Bottom Line: Fluorescent flowing objects produce diagonal lines in the raster-scanned image superimposed to static morphological details.The analytical expression of the CCF has been derived by applying scanning fluorescence correlation concepts to drifting optically resolved objects and the theoretical framework has been validated in systems of increasing complexity.The power of the technique is revealed by its application to the intricate murine hepatic microcirculatory system where blood flow speed has been mapped simultaneously in several capillaries from a single xy-image and followed in time at high spatial and temporal resolution.

View Article: PubMed Central - PubMed

Affiliation: Università degli Studi di Milano-Bicocca, Physics Department, Piazza della Scienza 3, I-20126, Milan, Italy.

ABSTRACT
We describe a novel method (FLICS, FLow Image Correlation Spectroscopy) to extract flow speeds in complex vessel networks from a single raster-scanned optical xy-image, acquired in vivo by confocal or two-photon excitation microscopy. Fluorescent flowing objects produce diagonal lines in the raster-scanned image superimposed to static morphological details. The flow velocity is obtained by computing the Cross Correlation Function (CCF) of the intensity fluctuations detected in pairs of columns of the image. The analytical expression of the CCF has been derived by applying scanning fluorescence correlation concepts to drifting optically resolved objects and the theoretical framework has been validated in systems of increasing complexity. The power of the technique is revealed by its application to the intricate murine hepatic microcirculatory system where blood flow speed has been mapped simultaneously in several capillaries from a single xy-image and followed in time at high spatial and temporal resolution.

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Measurement of the blood flow speed in the hepatic microcirculation on a wide field of view.(a) xy-image acquired by detecting the photoluminescence (shown in white) of 5-nm QDs (λexc = 900 nm, detection bandwidth = 640–690 nm); the lower right corner corresponds to the same region analysed in Figure 3. fline = 627 Hz, δx = 0.102 μm, scale bar, 15 μm. CCFs have been derived on the selected ROIs (~ 100 × 50–200 × 100 pixels) for (J-I)δx = 0.51–2.55 μm; the estimated /v/ and /v/0, recovered by the fit (equation (3)) and from the peak time (equation (4)) of the experimental CCFs, are reported in Table 1. (b) Schematic of the vessel centrelines for the image in (a). In each ROI, the arrow defines the flow direction and the color codes for the speed value /v/. Vessels not analysed are shown in grey. (c) CCFs computed for (J-I)δx = 2.04 μm in ROIs 5, 6 and 11 (errors are within the size of data points). The fit (equation (3)) led to /v/ = 499 ± 18 μm/s in ROI 5, /v/ = 187 ± 2 μm/s in ROI 6 and /v/ = 396 ± 3 μm/s in ROI 11; as expected, the CCF peak shifts toward shorter lag times as the flow speed increases.
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f4: Measurement of the blood flow speed in the hepatic microcirculation on a wide field of view.(a) xy-image acquired by detecting the photoluminescence (shown in white) of 5-nm QDs (λexc = 900 nm, detection bandwidth = 640–690 nm); the lower right corner corresponds to the same region analysed in Figure 3. fline = 627 Hz, δx = 0.102 μm, scale bar, 15 μm. CCFs have been derived on the selected ROIs (~ 100 × 50–200 × 100 pixels) for (J-I)δx = 0.51–2.55 μm; the estimated /v/ and /v/0, recovered by the fit (equation (3)) and from the peak time (equation (4)) of the experimental CCFs, are reported in Table 1. (b) Schematic of the vessel centrelines for the image in (a). In each ROI, the arrow defines the flow direction and the color codes for the speed value /v/. Vessels not analysed are shown in grey. (c) CCFs computed for (J-I)δx = 2.04 μm in ROIs 5, 6 and 11 (errors are within the size of data points). The fit (equation (3)) led to /v/ = 499 ± 18 μm/s in ROI 5, /v/ = 187 ± 2 μm/s in ROI 6 and /v/ = 396 ± 3 μm/s in ROI 11; as expected, the CCF peak shifts toward shorter lag times as the flow speed increases.

Mentions: Experimentally, the approximate /v/0 has also been compared with the value /v/ obtained from the CCFs fit (taken as un unbiased estimate of the flow speed) for all the data presented in this work (Figures 2,3,4): the average ratio /v///v/0 ranges from 0.87 to 0.96, suggesting that the blood flow speed can be obtained directly from the peak time of the experimental CCFs in most of the examined cases, thereby simplifying the analysis.


In vivo flow mapping in complex vessel networks by single image correlation.

Sironi L, Bouzin M, Inverso D, D'Alfonso L, Pozzi P, Cotelli F, Guidotti LG, Iannacone M, Collini M, Chirico G - Sci Rep (2014)

Measurement of the blood flow speed in the hepatic microcirculation on a wide field of view.(a) xy-image acquired by detecting the photoluminescence (shown in white) of 5-nm QDs (λexc = 900 nm, detection bandwidth = 640–690 nm); the lower right corner corresponds to the same region analysed in Figure 3. fline = 627 Hz, δx = 0.102 μm, scale bar, 15 μm. CCFs have been derived on the selected ROIs (~ 100 × 50–200 × 100 pixels) for (J-I)δx = 0.51–2.55 μm; the estimated /v/ and /v/0, recovered by the fit (equation (3)) and from the peak time (equation (4)) of the experimental CCFs, are reported in Table 1. (b) Schematic of the vessel centrelines for the image in (a). In each ROI, the arrow defines the flow direction and the color codes for the speed value /v/. Vessels not analysed are shown in grey. (c) CCFs computed for (J-I)δx = 2.04 μm in ROIs 5, 6 and 11 (errors are within the size of data points). The fit (equation (3)) led to /v/ = 499 ± 18 μm/s in ROI 5, /v/ = 187 ± 2 μm/s in ROI 6 and /v/ = 396 ± 3 μm/s in ROI 11; as expected, the CCF peak shifts toward shorter lag times as the flow speed increases.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4256590&req=5

f4: Measurement of the blood flow speed in the hepatic microcirculation on a wide field of view.(a) xy-image acquired by detecting the photoluminescence (shown in white) of 5-nm QDs (λexc = 900 nm, detection bandwidth = 640–690 nm); the lower right corner corresponds to the same region analysed in Figure 3. fline = 627 Hz, δx = 0.102 μm, scale bar, 15 μm. CCFs have been derived on the selected ROIs (~ 100 × 50–200 × 100 pixels) for (J-I)δx = 0.51–2.55 μm; the estimated /v/ and /v/0, recovered by the fit (equation (3)) and from the peak time (equation (4)) of the experimental CCFs, are reported in Table 1. (b) Schematic of the vessel centrelines for the image in (a). In each ROI, the arrow defines the flow direction and the color codes for the speed value /v/. Vessels not analysed are shown in grey. (c) CCFs computed for (J-I)δx = 2.04 μm in ROIs 5, 6 and 11 (errors are within the size of data points). The fit (equation (3)) led to /v/ = 499 ± 18 μm/s in ROI 5, /v/ = 187 ± 2 μm/s in ROI 6 and /v/ = 396 ± 3 μm/s in ROI 11; as expected, the CCF peak shifts toward shorter lag times as the flow speed increases.
Mentions: Experimentally, the approximate /v/0 has also been compared with the value /v/ obtained from the CCFs fit (taken as un unbiased estimate of the flow speed) for all the data presented in this work (Figures 2,3,4): the average ratio /v///v/0 ranges from 0.87 to 0.96, suggesting that the blood flow speed can be obtained directly from the peak time of the experimental CCFs in most of the examined cases, thereby simplifying the analysis.

Bottom Line: Fluorescent flowing objects produce diagonal lines in the raster-scanned image superimposed to static morphological details.The analytical expression of the CCF has been derived by applying scanning fluorescence correlation concepts to drifting optically resolved objects and the theoretical framework has been validated in systems of increasing complexity.The power of the technique is revealed by its application to the intricate murine hepatic microcirculatory system where blood flow speed has been mapped simultaneously in several capillaries from a single xy-image and followed in time at high spatial and temporal resolution.

View Article: PubMed Central - PubMed

Affiliation: Università degli Studi di Milano-Bicocca, Physics Department, Piazza della Scienza 3, I-20126, Milan, Italy.

ABSTRACT
We describe a novel method (FLICS, FLow Image Correlation Spectroscopy) to extract flow speeds in complex vessel networks from a single raster-scanned optical xy-image, acquired in vivo by confocal or two-photon excitation microscopy. Fluorescent flowing objects produce diagonal lines in the raster-scanned image superimposed to static morphological details. The flow velocity is obtained by computing the Cross Correlation Function (CCF) of the intensity fluctuations detected in pairs of columns of the image. The analytical expression of the CCF has been derived by applying scanning fluorescence correlation concepts to drifting optically resolved objects and the theoretical framework has been validated in systems of increasing complexity. The power of the technique is revealed by its application to the intricate murine hepatic microcirculatory system where blood flow speed has been mapped simultaneously in several capillaries from a single xy-image and followed in time at high spatial and temporal resolution.

Show MeSH
Related in: MedlinePlus