Purpose 4D flow cardiovascular magnetic resonance (CMR) and the assessment of wall shear stress (WSS) are non-invasive tools to study cardiovascular risks in vivo. Major limitations of conventional triggered methods are the long measurement times needed for high-resolution data sets and the necessity of stable electrocardiographic (ECG) triggering. In this work an ECG-free retrospectively synchronized method is presented that enables accelerated high-resolution measurements of 4D flow and WSS in the aortic arch of mice. Methods 4D flow and WSS were measured in the aortic arch of 12-week-old wildtype C57BL/6 J mice (n = 7) with a radial 4D-phase-contrast (PC)-CMR sequence, which was validated in a flow phantom. Cardiac and respiratory motion signals were extracted from the radial CMR signal and were used for the reconstruction of 4D-flow data. Rigid motion correction and a first order B0 correction was used to improve the robustness of magnitude and velocity data. The aortic lumen was segmented semi-automatically. Temporally averaged and time-resolved WSS and oscillatory shear index (OSI) were calculated from the spatial velocity gradients at the lumen surface at 14 locations along the aortic arch. Reproducibility was tested in 3 animals and the influence of subsampling was investigated. Results Volume flow, cross-sectional areas, WSS and the OSI were determined in a measurement time of only 32 min. Longitudinal and circumferential WSS and radial stress were assessed at 14 analysis planes along the aortic arch. The average longitudinal, circumferential and radial stress values were 1.52 ± 0.29 N/m2, 0.28 ± 0.24 N/m2 and − 0.21 ± 0.19 N/m2, respectively. Good reproducibility of WSS values was observed. Conclusion This work presents a robust measurement of 4D flow and WSS in mice without the need of ECG trigger signals. The retrospective approach provides fast flow quantification within 35 min and a flexible reconstruction framework.

BackgroundThe aortic pulse-wave velocity (PWV) is an important indicator of cardiovascular risk. In recent studies MRI methods have been developed to measure this parameter noninvasively in mice. Present techniques require additional hardware for cardiac and respiratory gating. In this work a robust self-gated measurement of the local PWV in mice without the need of triggering probes is proposed.MethodsThe local PWV of 6-months-old wild-type C57BL/6J mice (n=6) was measured in the abdominal aorta with a retrospectively triggered radial Phase Contrast (PC) MR sequence using the flow-area (QA) method. A navigator signal was extracted from the CMR data of highly asymmetric radial projections with short repetition time (TR=3 ms) and post-processed with high-pass and low-pass filters for retrospective cardiac and respiratory gating. The self-gating signal was used for a reconstruction of high-resolution Cine frames of the aortic motion. To assess the local PWV the volume flow Q and the cross-sectional area A of the aorta were determined. The results were compared with the values measured with a triggered Cartesian and an undersampled triggered radial PC-Cine sequence.ResultsIn all examined animals a self-gating signal could be extracted and used for retrospective breath-gating and PC-Cine reconstruction. With the non-triggered measurement PWV values of 2.3±0.2 m/s were determined. These values are in agreement with those measured with the triggered Cartesian (2.4±0.2 m/s) and the triggered radial (2.3±0.2 m/s) measurement. Due to the strong robustness of the radial trajectory against undersampling an acceleration of more than two relative to the prospectively triggered Cartesian sampling could be achieved with the retrospective method.ConclusionWith the radial flow-encoding sequence the extraction of a self-gating signal is feasible. The retrospective method enables a robust and fast measurement of the local PWV without the need of additional trigger hardware.

The proposed method extends "wireless" cardiac MRI to non-steady-state inversion recovery measurements. T maps were generated with a quality comparable to ECG based reconstructions. As the method does not rely on an ECG trigger signal it provides easier animal handling. Magn Reson Med 76:1887-1894, 2016. © 2016 International Society for Magnetic Resonance in Medicine.

BackgroundLocal 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.MethodsIn 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.ResultsAccelerated 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.ConclusionIn 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.

The proposed method is capable of producing quantitative perfusion maps on arbitrary positions in the heart cycle within a short measurement time. The method is robust against irregular breathing patterns and heart rate changes and can be implemented on all scanners.

It is shown that the Look-Locker readout as well as the nonideal slice-selective inversion experiment have a considerable influence and have to be included properly to correctly quantify myocardial perfusion.

Background Fast and accurate T1ρ mapping in myocardium is still a major challenge, particularly in small animal models. The complex sequence design owing to electrocardiogram and respiratory gating leads to quantification errors in in vivo experiments, due to variations of the T1ρ relaxation pathway. In this study, we present an improved quantification method for T1ρ using a newly derived formalism of a T1ρ* relaxation pathway. Methods The new signal equation was derived by solving a recursion problem for spin-lock prepared fast gradient echo readouts. Based on Bloch simulations, we compared quantification errors using the common monoexponential model and our corrected model. The method was validated in phantom experiments and tested in vivo for myocardial T1ρ mapping in mice. Here, the impact of the breath dependent spin recovery time Trec on the quantification results was examined in detail. Results Simulations indicate that a correction is necessary, since systematically underestimated values are measured under in vivo conditions. In the phantom study, the mean quantification error could be reduced from − 7.4% to − 0.97%. In vivo, a correlation of uncorrected T1ρ with the respiratory cycle was observed. Using the newly derived correction method, this correlation was significantly reduced from r = 0.708 (p < 0.001) to r = 0.204 and the standard deviation of left ventricular T1ρ values in different animals was reduced by at least 39%. Conclusion The suggested quantification formalism enables fast and precise myocardial T1ρ quantification for small animals during free breathing and can improve the comparability of study results. Our new technique offers a reasonable tool for assessing myocardial diseases, since pathologies that cause a change in heart or breathing rates do not lead to systematic misinterpretations. Besides, the derived signal equation can be used for sequence optimization or for subsequent correction of prior study results.

No abstract