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. 2017 Oct 16;19(1):77.
doi: 10.1186/s12968-017-0382-2.

Assessment of local pulse wave velocity distribution in mice using k-t BLAST PC-CMR with semi-automatic area segmentation

Volker Herold  1 , Stefan Herz  2 , Patrick Winter  3 , Fabian Tobias Gutjahr  3 , Kristina Andelovic  4 , Wolfgang Rudolf Bauer  4 , Peter Michael Jakob  3
Affiliations

Assessment of local pulse wave velocity distribution in mice using k-t BLAST PC-CMR with semi-automatic area segmentation

Volker Herold et al. J Cardiovasc Magn Reson. .

Abstract

Background: Local aortic pulse wave velocity (PWV) is a measure for vascular stiffness and has a predictive value for cardiovascular events. Ultra high field CMR scanners allow the quantification of local PWV in mice, however these systems are yet unable to monitor the distribution of local elasticities.

Methods: In the present study we provide a new accelerated method to quantify local aortic PWV in mice with phase-contrast cardiovascular magnetic resonance imaging (PC-CMR) at 17.6 T. Based on a k-t BLAST (Broad-use Linear Acquisition Speed-up Technique) undersampling scheme, total measurement time could be reduced by a factor of 6. The fast data acquisition enables to quantify the local PWV at several locations along the aortic blood vessel based on the evaluation of local temporal changes in blood flow and vessel cross sectional area. To speed up post processing and to eliminate operator bias, we introduce a new semi-automatic segmentation algorithm to quantify cross-sectional areas of the aortic vessel. The new methods were applied in 10 eight-month-old mice (4 C57BL/6J-mice and 6 ApoE (-/-)-mice) at 12 adjacent locations along the abdominal aorta.

Results: Accelerated data acquisition and semi-automatic post-processing delivered reliable measures for the local PWV, similiar to those obtained with full data sampling and manual segmentation. No statistically significant differences of the mean values could be detected for the different measurement approaches. Mean PWV values were elevated for the ApoE (-/-)-group compared to the C57BL/6J-group (3.5 ± 0.7 m/s vs. 2.2 ± 0.4 m/s, p < 0.01). A more heterogeneous PWV-distribution in the ApoE (-/-)-animals could be observed compared to the C57BL/6J-mice, representing the local character of lesion development in atherosclerosis.

Conclusion: In the present work, we showed that k-t BLAST PC-MRI enables the measurement of the local PWV distribution in the mouse aorta. The semi-automatic segmentation method based on PC-CMR data allowed rapid determination of local PWV. The findings of this study demonstrate the ability of the proposed methods to non-invasively quantify the spatial variations in local PWV along the aorta of ApoE (-/-)-mice as a relevant model of atherosclerosis.

Keywords: ApoE (−/−); Magnetic resonance imaging; Phase contrast; Pulse wave velocity.

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Conflict of interest statement

Ethics approval and consent to participate

All experimental procedures were in accordance with institutional and internationally recognized guidelines and were approved by the Regierung von Unterfranken (Government of Lower Franconia, Würzburg, Germany, Az. 55.2-2531.01-23/11) to comply with german animal protection law.

Consent for publication

All authors have approved the final manuscript and give consent for publication.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures

Fig. 1
Fig. 1
a 2D-Phase-Contrast-CINE-FLASH sequence with through-plane flow encoding. Flow-compensation was applied for all three-gradient directions. b Undersampling scheme for the k-t BLAST data acquisition (shown for R=2). The different shadings of the data points represent the different repetitions of the sequence. Altogether five repetitions, each one shifted by a temporal delay of 1 ms were, performed
Fig. 2
Fig. 2
Post processing steps for the k-t BLAST QA-reconstruction
Fig. 3
Fig. 3
Semi-automatic segmentation of the cross sectional area of the vessel lumen. For a given threshold the time course of the cross sectional area was automatically determined for each of the three flow encoding steps. The threshold delivering the minimal deviation between the three segmentation results was considered as the optimum result
Fig. 4
Fig. 4
Bland-Altman plot (a) and scatter plot (b) of pulse-wave velocities obtained with k-t BLAST acceleration compared to PWV-measures from fully sampled datasets based on manual segmentation of the vessel cross section. c, d Bland-Altman plot and scatter plot of full sampled data versus undersampled data based on semi-automatic segmentation of the vessel cross section
Fig. 5
Fig. 5
a, b Scatter plots of the cross sectional area segmentation: manual versus semi-automatic. a Spatial resolution achieved by zero padding for manual segmentation: (86×86) μ m 2; semi-automatic segmentation: (21×21) μ m 2. b Same spatial resolution for manual and semi-automatic segmentation. c, d PWV calcualtions based on the data shown in a, b respectively
Fig. 6
Fig. 6
Results of the in-vivo measurements: a Distribution of the measurement locations to estimate the local PWV in the abdominal aorta. b Exemplary determination of the PWV for one measurement slice. c Distribution of the local-PWV measures for wild-type and ApoE (−/−)-animals. d Standard deviation of the set of PWV-measures for each animal
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