Jasper Verbree1 and Matthias J.P. van Osch1
1Radiology Department; Leiden Institute for Brain and Cognition; C.J. Gorter Center for High-field MRI, Leiden University Medical Center, Leiden, Netherlands
Synopsis
In pCASL, the blood tagged at the
end-of-labeling period is expected to contribute most to the perfusion signal due
to T1 recovery of the labeled spins. The influence on PCASL of cardiac triggering at the end-of-labeling was assessed with simulations and subsequently
applied in volunteers. Simulations predict a 9% variation in ASL-signal over
the cardiac cycle. In-vivo
measurements were unable to show the predicted effect nor a difference
in tSNR. Combining with earlier findings concerning cardiac triggering, neither triggering start- or end of labeling triggering
improves signal stability, suggesting
that cardiac triggering is not beneficial for pCASL.Background
Introducing cardiac triggering at the start of
every measurement has been shown to reduce signal variability in pulsed ASL[1,2], but
not pCASL[2]. Due to T
1 recovery of the labeled spins in pCASL, the blood
tagged close to the end of the labeling period is expected to contribute most to the
perfusion signal and will therefore be the most significant source of signal
variations. The goal of the current study is to investigate the influence of
the cardiac cycle on these spins labeled last. To this end, we first assessed
the contribution of the end-of-labeling period using simulations. Subsequently,
prospective end-of-labeling triggering was implemented and tested in human
volunteers. By increasing the labeling duration drastically (to an impractical
value of 7500 ms), the effect of cardiac cycle on the earliest labeled spins
was minimized.
Simulation
methods
The inflow of label into tissue was simulated
in MATLAB as a single vessel. For each (1ms) iteration fresh blood entered the
artery, thereby shifting the entire blood pool toward the tissue-compartment.
Subsequently, T
1-relaxation was accounted for in =the artery (T
1=1664ms) and tissue (T
1=1200ms).
The ASL signal at a fixed post labeling delay was assessed by integrating over
the tissue-compartment. A simulated pulsatile flow (Figure 1) was generated
with a mean heart rate of 50 bpm and constant stroke volume.
Simulations
results
The
simulated ASL signal over time followed the standard Buxton inflow-decay curve,
with flow pulsations superimposed (Figure 1a). Ending the labeling directly
after the trigger (EL
delay of 0ms) corresponded with a peak in the
Buxton curve (Figure 2a). Increasing the EL
delay to 500ms decreased
the ASL signal by 9% (Figure 2b). Further increases of the EL
delay resulted
in recovery of the ASL signal to the starting level. In summary, simulations
show a potential 9% effect of the cardiac cycle on the ASL-signal.
Implementation
details
The start-of-labeling (SL) was always
triggered, whereas the time of the end-of-labeling (EL) was varied relative to the finger
pulse oximeter signal (PPU-trigger) by ending labelling after a certain delay (EL
delay).
To isolate the effect of EL, care was taken that the average labeling duration (7500ms)
was similar for all ELs as well as for the untriggered ASL-scan. For such long
labeling durations, small variations in labeling duration have only a marginal
influence on the ASL-signal (0.5% increase for 500ms longer labeling, assuming a T
1 of 1664ms).
In-vivo MRI
methods
Data from six volunteers (3 female, 25-56 years)
were acquired on a Philips 3T MRI. The blood velocity through the labeling
plane was assessed using a retrospectively triggered phase-contrast scan with
15 cardiac phases (venc=200cm/s). The vendor supplied pCASL sequence was
modified to incorporate triggering of start- and end-of-labeling (ASLtrig). Three
distinct cardiac phases (a), (b) and (c) were determined for each subject
according to the flow data (Figure 1). The control scans were triggered at
start-labeling and had the same labeling duration as the preceding label acquisition.
The triggered scan was acquired continuously, cycling through the cardiac
phases (a-b-c-a-b-c….) resulting in 20 label and control images for each
cardiac phase.
A non-triggered reference ASL scan (NTASL) was
acquired with the same imaging parameters. For quantification purposes an M0
scan was also acquired. ASL-images were quantified [3], aligned to the first
dynamic, and scaled using the M0 scan. Average gray matter ASL-signal
was calculated for each dynamic. All analyses and statistics were performed in
MATLAB with the SPM8 toolbox.
In-vivo results
Heart rate (p=0.26) and mean labeling duration
(p=0.47) did not differ significantly between scans. Mean CBF was higher in
NTASL (p=0.035) compared to ASLtrig of cardiac phase (b). The temporal standard
deviation was comparable between the triggered and non-triggered scans (p=0.44)
(Figure 4). The moment of imaging with respect to the cardiac cycle varied
equally for triggered (23%) and non-triggered (27%) scans.
Conclusion
Simulations
predict a 9% variation in ASL-signal over the cardiac cycle when triggering the
end-of-labeling, with maximum signal at end-systole. In-vivo measurements were
unable to show either the predicted effect or a difference in tSNR. It
should be noted that using
end-of-label-triggering with normal labeling durations (≈1800ms) would result in
a variation in labeling duration that would need to be accounted for in
quantification. Combined
with earlier findings concerning cardiac triggering, neither start-[2] nor end
of labeling triggering improves signal stability, suggesting that cardiac triggering
is not beneficial for pCASL.
Scan
Parameters
ASLtrig: minimum
labeling duration=7000ms; post labeling delay=1800; TE=14 ms; flip angle=90°;
FOV=240x240x119mm3; matrix=80x79; slice thickness=7mm; number of
dynamics=60 (20 per condition); EPI factor=35; NTASL: same
as triggered, labeling duration=7500; dynamics=20; M0:
TR/TE/TE2=2000/11/14ms; FOV=220x220x102mm3; matrix=72x69; slice
thickness=6 mm; number of averages=10; EPI factor=25;
Acknowledgements
The authors would like to thank ir. J. Wezel for help with optimizing the simulations.References
[1]
Fushimi, et al. NMR biomed. 2013; (jan) [2] Wu et al. IEEE
TMI. 2007;26(1): 84-92 [3] Heijtel et al. MRM. 2014;92(C):182-192