Synopsis

Measuring the arterial transit time (ATT) helps for the optimization and quantification of arterial spin labeling experiments. Moreover, ATT may provide information on potential underlying vascular pathologies. In preclinical perfusion studies, multiple 2D-slices are commonly acquired to measure perfusion. However, this readout limits the number of slices for which ATT can be measured accurately in rodents. In this study, we implemented and optimized a dynamic ASL labeling scheme with a 3D echo planar imaging (EPI) readout to simultaneously map cerebral blood flow, arterial transit time and tissue T1 in the mouse brain at 9.4T.

Introduction

Measuring the arterial transit time (ATT), i.e. the time it takes for the blood to travel from the labeling slice to the tissue of interest, helps for the optimization and quantification of perfusion measurement by means of arterial spin labeling (ASL)¹. Moreover, ATT may provide information on potential underlying vascular pathologies².

In preclinical perfusion studies, multiple 2D-slices are commonly acquired to measure perfusion^3,4,5. However, this readout limits the number of slices for which ATT can be measured accurately in rodents: the time it takes to acquire all slices (~30ms/slice) rapidly exceeds the short ATTs, which are around 100ms in healthy mice⁵. Thus, accurate blood arrival sampling becomes difficult for the last acquired slices. A 3D-readout is therefore particularly interesting, since it allows for having the same postlabeling delay (PLD) for all acquired slices, without any additional delay due to the acquisition order.

In this study, we implemented and optimized a dynamic ASL labeling scheme^6,7 with a 3D echo planar imaging (EPI) readout to simultaneously map CBF, ATT and tissue T₁ (T_1t) in the mouse brain. Then, we applied the developed sequence on a mouse model of schizophrenia to evaluate the effect of knocking-out (KO) the microtubule-associated protein 6 (MAP6) – a protein that has been shown to play a critical role during the development of cerebral axonal tracts⁸- on ATT and CBF.

Methods

Experiments were performed on a 9.4T horizontal scanner (Bruker Biospec, AVIII-HD) with a 86mm-volume coil for excitation and a 4-channel cryoprobe surface coil for reception. Mice were anesthetized using isoflurane (1.5-2%) in air:O2 70:30%. Two groups of C57Bl6/129Sv male mice (n=4/group) were studied: a wild type (WT) group and a group for which MAP6 was knocked out (KO).

Unbalanced pCASL labeling pulses were applied in the mice’s neck (at -12mm from the isocenter) during 4s following the DASL scheme shown on Fig.1. This scheme is optimized to sample the signal with high accuracy (50ms temporal resolution) around the blood arrival time (i.e.between 0-0.4s and 2-2.4s) and with lower temporal resolution (200ms) during the capillary blood filling (i.e.from 0.4-2s and 2.4-4s).

The labeling pulses consisted of Hanning window shaped RF pulses with an average amplitude of 5μT, duration of 400μs, repeated every 800μs, with optimized phases. Gmax/Gave was set to 90/10 mT/m. Image acquisition was performed through single-shot EPI: TE/TR=4000/22ms, resolution=0.2*0.2*1.6 mm³, number of slices=10, T_acq=22min. For CBF-quantification, the labeling efficiency was measured 4mm downstream the labeling plane with a flow-compensated, ASL-encoded FLASH.

The signal time-course from the DASL experiment was fitted voxel-by-voxel to the model described by Barbier et al^6,7 (Fig.2) to obtain quantitative CBF, ATT and T_1t maps. ROIs were manually drawn in four brain regions.

Results and discussion

Fig.3 shows examples of CBF, ATT and T_1t maps obtained from a 3D-DASL-EPI experiment. The observed contrast in the CBF maps is as expected, with higher cortical and thalamic perfusion values compared to white matter. Although the quantitative CBF values (Fig.4a) are much higher than that acquired with standard, 2D, pCASL^3,5, they are in the range of those measured with an Hadamard-encoded ASL scheme⁵: in WT mice we measured an average brain CBF of 350±128mL/100g/min. ATT values (246±80ms on average, Fig.4b) are also in the same range as the one previously reported⁵ and the regional variations are similar, with lower hippocampal transit times compared to other grey matter structures (cortex, thalamus and striatum). For all slices, T_1t (811±282ms on average in the brain, Fig.4c) is lower than the expected tissue T₁ (i.e. 1.7s at 9.4T). This low T_1t may be ascribed to the presence of intravascular signal in our time-encoded ASL approach (inflow and outflow effects). This intravascular signal and short T_1t may explain the high CBF values. Contrary to time-encoded measurements performed on humans (with longer transit times), short ATT values in rodents induce more intravascular signal and require a finer temporal sampling.

In our study, the WT and KO mice showed similar CBF and ATT values (Fig.4a and b). The WT/KO variability (i.e. SD/mean) on CBF and ATT was 5 and 7%, respectively, suggesting a reproducible approach.

Conclusion

Acknowledgements

References

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