Dimo Ivanov1, Josef Pfeuffer2, Anna Gardumi1, Kâmil Uludağ1, and Benedikt A Poser1
1Department of Cognitive Neuroscience, Maastricht University, Maastricht, Netherlands, 2MR Application Development, Siemens Healthcare, Erlangen, Germany
Synopsis
Arterial spin labelling (ASL) is the primary
non-invasive MRI approach to measure cerebral blood flow in healthy subjects
and patients. Recently,
a consensus paper has recommended segmented versions of 3D spin-echo readouts
like GRASE, but these are susceptible to motion and have poor temporal
resolution. To alleviate these drawbacks, we propose to accelerate the 3D GRASE
readout and utilize 2D CAIPIRINHA for the reconstruction. We demonstrate that
our approach is superior or at least equivalent to the 2D GRAPPA technique, depending
on the acceleration factor used. The proposed approach will particularly benefit
functional and clinical ASL applications.
Purpose
Arterial spin labelling1 (ASL) is the
primary non-invasive MRI approach to measure cerebral blood flow (CBF) in healthy
subjects and patients.
The ASL white paper2 recommends the use
of pseudo-continuous labelling, background suppression and 3D spin-echo
sequences. Since T2-decay during the readout can cause significant
through-plane blurring in 3D single-shot acquisitions with high spatial
resolution and/or whole-brain coverage, currently the use of segmented
approaches is advised. However, segmented readouts require multiple RF
excitations, or shots, to acquire the complete k-space, and are thus susceptible
to motion artefacts, which are particularly problematic for ASL use in the
clinic. One way to reduce the duration or the number of shots per measurement is
through data undersampling and reconstruction using parallel imaging techniques3,4
and RF coil arrays. Nevertheless, data undersampling typically leads to a loss
in image signal-to-noise ratio (SNR) and temporal SNR (tSNR). The extent of
this SNR loss will depend on the acceleration factor (AF) and the properties of
the receive array, but can be reduced by utilizing controlled aliasing as in 2D
CAIPIRINHA5. In this study, we explore the benefits of CAIPIRINHA for
background-suppressed pCASL6,7 with accelerated 3D GRASE8
readout at 3T.Methods
A prototype 3D GRASE pCASL sequence supporting simultaneous
segmentation and GRAPPA4 acceleration, along with 2D CAIPIRINHA shifts
was implemented on a MAGNETOM Prisma 3T (Siemens Healthcare, Erlangen, Germany)
with a 64-channel head coil. The CAIPIRINHA shifts were realized by either
modulating the EPI readout with Δkz-blips (for phase-encoding AF ≤ partition-encoding
AF) or by shifting successive kz-planes by Δky and
appropriate echo shifts (for phase-encoding AF > partition-encoding AF). Experiments
were performed in seven healthy volunteers after obtaining informed consent. Optimized
background suppression9 was applied. A Time-Of-Flight angiogram was
acquired to determine the position of the labelling plane, and a subject-specific
grey-matter (GM) mask was generated from a T1-weighted MPRAGE scan. Six
4-minute ASL protocols were performed in pseudo-randomized order, each with
different combinations of in-plane (phase-encoding) and through-plane (partition-encoding)
acceleration factors [AF_PE x AF_Pa
(CAIPIRINHA_shift)], utilizing either GRAPPA or CAIPIRINHA reconstruction. The
accelerated protocols were either 2-shot segmented [2x1 (y1), 1x2 (z1), 3x1 (y1),
1x3 (z2)] or single-shot [2x2 (z1), 2x3 (z1)]. For comparison, a scan without parallel
acceleration (4 shots) was also obtained. Additional control images without
background suppression and long TR were acquired immediately after each
protocol for CBF quantification. All scans had 3mm isotropic nominal voxel size, labelling duration/post-labelling
delay/TR = 1500/1580/4120ms and 30 or 32 slices depending on the AF_Pa. The data were motion-corrected and
coregistered with SPM8. Voxel-wise CBF was computed according to Vidoretta et
al.9, whereas perfusion tSNR maps and perfusion tSNR efficiencies – according
to Li et al.10.Results
Figure 1 demonstrates that acceleration in the
partition-encoding direction can result in residual aliasing artefacts when
CAIPIRINHA is not used. However, no significant differences in mean GM CBF were
found among all the protocols tested once regions with artefacts were excluded.
Increasing the AF decreases the perfusion tSNR, and for acquisitions with
CAIPIRINHA the reduction is smaller or equal to the reduction when using GRAPPA
(Figures 2 and 3). CAIPIRINHA has significantly higher perfusion tSNR than
GRAPPA for the 1x2, 1x3 and 2x3 accelerations. Despite having significantly
lower perfusion tSNR than the segmented acquisition, the tSNR efficiencies of
the accelerated acquisitions compare more favourably to it (Figure 4).Discussion
The artefact reduction and perfusion tSNR increases
with CAIPIRINHA compared to GRAPPA allow higher acceleration factors, and
therefore alleviate the need for readout segmentation, which in addition reduces
the motion sensitivity of 3D GRASE ASL. Furthermore, the higher acceleration
factors can be employed to shorten the duration of the GRASE readout reducing
image blurring and distortions in susceptibility-affected regions, thereby
resulting in higher effective resolution. Alternatively, the higher acceleration
factors can also be used to increase the nominal spatial resolution. All of
these advantages apply equally to pulsed 3D GRASE ASL since they do not depend
on the labelling scheme.
The aforementioned scenarios offer significant
immediate benefits for functional and clinical ASL – two areas where the
non-invasiveness of ASL is particularly valuable, but the current segmented
approaches are performing suboptimally. Recently, acceleration techniques for
other commonly-used ASL readouts like stack-of-spirals have been proposed11.
The advantage of Cartesian CAIPIRINHA sampling and reconstruction is that it
can be readily implemented on the scanner and the data evaluated online, which
facilitates its direct clinical use.
In conclusion, CAIPIRINHA-accelerated 3D GRASE improves
on several shortcomings of current readout approaches, advancing ASL closer to
its full potential.
Acknowledgements
No acknowledgement found.References
1 Detre et al. MRM 1992. 23:37-45;
2 Alsop et al. MRM 2015. 73:102-116;
3
Pruessmann et al. MRM 1999. 42:952-962;
4
Griswold et al. MRM 2002. 47:1202-1210;
5
Breuer et al. MRM 2006. 55:549-556;
6
Dai et al. MRM 2008. 60:1488-1497;
7
Wu et al. MRM 2007. 58:1020-1027;
8
Oshio and Feinberg. MRM 1991. 20:344-349;
9
Vidorreta et al. NMR Biomed 2014. 27:1387-1396;
10
Li et al. Neuroimage 2015. 106:170-181;
11
Chang et al. MRM in press. doi: 10.1002/mrm.26549