Gilbert Hangel1, Bernhard Strasser1, Michal Považan1, Martin Gajdošík1, Stephan Gruber1, Marek Chmelík1, Siegfried Trattnig1,2, and Wolfgang Bogner1
1MRCE, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria, 2Christian Doppler Laboratory for Clinical Molecular MR Imaging, Vienna, Austria
Synopsis
Reliable lipid suppression is essential for robust
quantification of parallel imaging accelerated high-
resolution MRSI. This work compared the performance of non-selective
lipid suppression using double inversion recovery (DIR) with the application of
L1- and L2-regularisation during data processing for single-slice MRSI with a
64×64 matrix and a GRAPPA-acceleration of nine in five volunteers. While DIR
featured the best lipid suppression, it increased the measurement time and
reduced metabolite SNR. L1 and L2 did not have these downsides, but twice as
much lipid signal remained, with L1 increasing the data pre-processing time
before spectral quantification by a factor of six.Purpose
High-resolution MRSI in the brain at 7 T1,2 allows the non-invasive mapping
of spatial metabolite distribution in detail, but suffers from long measurement
times and contamination by macromolecules and trans-cranial lipids originating
from a non-optimal point-spread function or B0-inhomgeneities.
Parallel imaging3,4 solves the first problem but worsens the second
one due to lipid fold-in. Different concepts for lipid suppression at 7 T were
introduced so far, such as outer volume suppression2 or
non-selective double inversion recovery5 (DIR) during the
measurement itself. Another
approach is the general removal of lipid signal projected into the brain from
the trans-cranial region during post-processing like L1- and L2-regularisation6,7.
This is based on the magnitudes stronger total lipid signal and the
orthogonality of lipid and metabolite spectra. Using parallel imaging accelerated
MRSI data5, we compared the performance of DIR, L1 and L2 in order
to facilitate the right choice of methods for specific MRSI needs.
Methods
Five volunteers were
previously measured
5 with a Siemens 7 T
Magnetom scanner and a 32-channel coil using an FID-MRSI sequence with phase
encoding and elliptical weighting. The sequence with no lipid suppression (NLS)
had a TR of 1038 ms, while the DIR sequence (Figure 1) had a total TR of 1300
ms which included a TI1/TI2 of 210/52 ms. Common
parameters were an acquisition delay of 1.3 ms, a 64×64×1 matrix, an FOV
of 220×200×10 mm³, 2048 sampling points with 6000 Hz receive bandwidth and a
3×3-GRAPPA-acceleration with an effective R of 8.3 (6:17 min for NLS, 6:51 for
DIR). An anatomical MP2RAGE reference scan (4:39 min) was acquired.
We used an in-house
developed pipeline
8 for data processing that included LCModel
fitting. For the lipid suppression comparison, L1 with 5 and 10 iterations as
well as L2 were applied to both NLS and DIR datasets. The performance of the
methods was compared using the lipid signal remaining after suppression (signal
sum of the 1.2 ppm lipid resonance after baseline subtraction), processing times
for L1/L2 as well as NAA SNR, CRLB and FWHM. Further, individual spectra, total
lipid maps and NAA maps were evaluated.
Results
In general, the application of L1 and L2 to the
DIR datasets did not lead to reliable results, excluding these combinations
from the comparison. Only negligible differences were found between the results
of 5 and 10 iterations of L1. Figure 2 provides an overview of the performance:
Considering lipid suppression, DIR was the most effective with around half as
much remaining lipid signal as L1/L2, but lost more of the SNR due to the
double inversion. The apparent SNR reduction of L1/L2 (and partially for DIR) was
to some extent caused by the removal of contamination in the NAA region that
would have been otherwise wrongly attributed to the NAA signal. The processing
time before the LCModel fitting increased by a factor of 6 for L1, but did not
significantly change for L2. Exemplary for these results are the spectra of Figure 3. Comparing the lipid maps (Figure
4) and NAA maps (Figure 5) of all methods shows that the lipid suppression
performance of all is adequate. The higher SNR of L1 and L2 translates into a
better metabolite map quality.
Discussion/Conclusions
Overall,
DIR lipid suppression as well as L1 and L2
regularisation allow sufficient removal of lipid artefacts, minimising
their impact on metabolite quantification. While DIR has the best suppression
efficiency, it is affected by longer measurement times due to SAR limitations
and metabolite SNR loss. L1’s and L2’s performance is similar, but L2 does not
effectively increase processing times. If the absolute lipid suppression
efficiency is less important than minimising measurement times, L2 regularisation
appears to be the most attractive choice in lipid suppression for brain MRSI at
7 T.
Acknowledgements
This study was supported by the Austrian Science Fund (FWF): KLI-61 and the FFG Bridge
Early Stage Grant #846505.References
[1] Bogner et al., NMR Biomed 2012; 25(6):873-82
[2] Henning et al., NMR
Biomed 2009; 22(7):683-96
[3]
Kirchner et al., Magn Reson Med 2015; 73(2):469-80
[4]
Strasser et al., Proc. Intl. Soc. MRM 21 (2013):2018
[5]
Hangel et al., NMR in Biomed 2015; 28(11):1413-25
[6]
Bilgic et al., MRM 2013; 69(6):1501-11
[7]
Bilgic et al., JMRI 2014; 40(1):181-191
[8] Považan et al., Proc.
Intl. Soc. MRM 23 (2015): 1973