Eva Heckova1, Ursel Antpusat1,2, Michal Považan3,4, Bernhard Strasser5, Gilbert Hangel1, Lukas Hingerl1, Philipp Moser1, Stephan Gruber1, Siegfried Trattnig1,6, and Wolfgang Bogner1,6
1High Field MR Centre, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria, 2Hamm-Lippstadt University of Applied Sciences, Hamm, Germany, 3Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States, 4F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States, 5Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States, 6Christian Doppler Laboratory for Clinical Molecular Molecular MR Imaging, Vienna, Austria
Synopsis
The goal was to
investigate how the use of different macromolecular baseline models affects both
the accuracy and test-retest reproducibility of metabolite quantification for
clinically attractive FID-MRSI scan with in-plane resolution of 3.4 x 3.4 mm2 and
acquisition time of 5 min. We confirmed that our 1H-FID-MRSI
sequence provides information about abundance and spatial distribution of
several neurometabolites with high accuracy. Including the
information about the macromolecular background into the quantification process
does not decrease its reproducibility.
Purpose
Magnetic
resonance spectroscopic imaging (MRSI) combines information about brain anatomy
with biochemical background of tissues. MRSI especially benefits from
ultra-high field strength (i.e., 7T), as the spatial and spectral resolution
improve significantly. However, at such high field strengths the T2 relaxation
times and hence the SNR of detectable neurometabolites decrease drastically. To
maximize the number of detectable compounds, FID-based MRSI with only negligible
acquisition delay was proposed[1]. While this improves the detectability of neurometabolites
such as N-acetyl aspartate,Choline,Creatine,Myo-inositol, Glutamate,Glutamine or Glutathione, FID-MRSI also enhances the background signal of
macromolecules (MM). This poses challenges for accurate metabolite
quantification in the presence of strong MM resonances[2]. Therefore, it is mandatory
to include MMs into the fitting model appropriately[3,4]. Several approaches have been proposed
including the entire MM baseline as a single component[2] or adding several
parameterized MM components to stay flexible in the presence of pathological
changes[3]. Both these approaches were evaluated for fairly high SNR (hence
long scan times) and although the accuracy for metabolite quantification was
investigated, the effects on reproducibility were only indirectly assessed via
CRLBs.
Therefore,
we wanted to investigate, how the use different MM baseline models or rather
skipping MMs for quantification affect both the accuracy and test-retest reproducibility
of metabolite quantification for clinically attractive FID-MRSI protocols.
Methods
Ten
volunteers (7M/3F, 28±4y) were scanned twice in separate sessions with a Siemens
7T whole-body MR scanner and a 32-channel head coil. A 1H-FID-MRSI sequence[1]
with TE/TR, 1.3/600ms; matrix size, 64x64; nominal voxel volume,
3.4x3.4x8mm3; 1024 spectral points, 6kHz bandwidth, 2D-CAIPIRINHA[5]
acceleration with ~5 min scan time was measured in transverse plane above the
ventricles. Auto-align ensured reproducible slice positioning between
test-retest measurements[6]. Data were processed with in-house developed software
employing LCModel, MATLAB, Bash and MINC and were evaluated with SPSS.
Three different basis
sets were used for LCModel quantification: i) without any MM spectrum included
–“no_MM”; ii) with a single measured
MM spectrum included –“full_MM”; iii)
with nine individual MM components included, but with soft constraints[4] –“parameterized_MM”(Figure 1). Test-retest
reproducibility for metabolite levels and their ratios was assessed within two
ROIs: in frontal white matter (ROI1) and in parietal white matter (ROI2). Spectral
quality measures (SNR, linewidth), fitting quality (CRLBs), coefficients of
variation(CV), intra-class correlation coefficients(ICC) and Bland-Altman
statistics were obtained.Results
We found
low CV and high ICC for five metabolic ratios (tNAA/tCr, tCho/tCr, Ins/tCr,
Glx/tCr, GSH/tCr) when using the “no_MM” basis
set. The CVs varied between 3.0-9.5% and ICCs between 0.694-0.95. Within the
ROIs the mean SNR was 7.1 and CRLBs varied between 3.8-12.9%.
Quantification with the “full_MM”
basis set similarly revealed highly reproducible quantification accuracy with
CVs varying between 2.8-9.3% and ICC between 0.678-0.954 for the same metabolic
ratios. In ROIs the mean SNR was 7 and CRLBs in a range of 4.2-13.2%.
Finally,
quantification using the “parameterized_MM”
basis set also provided reproducible results for the majority of the metabolic
ratios (ICC 0.616-0.977 and CV 3.7-10.2%). Only quantification of J-coupled
resonances (e.g.,GSH or Glx) in parietal region was biased by the presence of
MM components in the basis set (ICCs: 0.409 for GSH/tCr, 0.469 for Glx/tCr). The
mean SNR was 6.1 and CRLBs in a range of 5-16%. Results are summarized in
Figure 2 and 3. Bland-Altman plots of
test-retest reproducibility for tNAA/tCr and Glx/tCr are displayed in Figure 4.
Figure 5 shows representative metabolic maps.
Discussion
Based on CVs
and ICCs we confirmed that our 1H-FID-MRSI sequence provides
information about abundance and spatial distribution of several
neurometabolites with high accuracy and reproducibility in only ~5min. Including the information about the macromolecular
background into the quantification process either via a “full_MM” or “paramterized_MM”
basis sets did not significantly decrease the reproducibility of most
metabolite ratios. This confirms earlier reports that were performed with much
higher SNR and longer scan times of ~30min[2,3]. Yet, the slightly increased
variability introduced into quantification by separate fitting of parameterized
MM resonances can become a problem for lower abundant J-coupled metabolites when
SNR is low. This must be taken into account when MM levels themselves are of
interest. In that case SNR and consequently quantification accuracy can be
improved, e.g. by increasing the acquisition time. So far we have only completed
the evaluation of a single MRSI slice. However, the full study included
acquisition of four transversal slices at different levels of the brain, which
will provide a more complete picture of the current clinical feasibility of 7T
FID-MRSI.Conclusion
Clinically attractive 7T FID-MRSI with adequate MM prior knowledge
provides highly reproducible results for the most common metabolites even
when using parameterized MM models.Acknowledgements
This study
was supported by the Austrian Science Fund (FWF): KLI 646 and P 30701, and FFG Bridge Early Stage Grant #846505.References
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