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@ARTICLE{Alcicek:300296,
author = {S. Alcicek$^*$ and M. W. Ronellenfitsch$^*$ and J. P.
Steinbach$^*$ and A. Manzhurtsev and D. C. Thomas$^*$ and K.
Weber$^*$ and V. Prinz and M.-T. Forster and E.
Hattingen$^*$ and U. Pilatus and K. J. Wenger$^*$},
title = {{O}ptimized {L}ong-{TE} 1{H} s{LASER} {MR} {S}pectroscopic
{I}maging at 3{T} for {S}eparate {Q}uantification of
{G}lutamate and {G}lutamine in {G}lioma.},
journal = {Journal of magnetic resonance imaging},
volume = {62},
number = {3},
issn = {1053-1807},
address = {New York, NY},
publisher = {Wiley-Liss},
reportid = {DKFZ-2025-00749},
pages = {890-901},
year = {2025},
note = {2025 Sep;62(3):890-901},
abstract = {Glutamate and glutamine are critical metabolites in
gliomas, each serving distinct roles in tumor biology.
Separate quantification of these metabolites using in vivo
MR spectroscopy (MRS) at clinical field strengths (≤ 3T)
is hindered by their molecular similarity, resulting in
overlapping, hence indistinguishable, spectral peaks.To
develop an MRS imaging (MRSI) protocol to map glutamate and
glutamine separately at 3T within clinically feasible time,
using J-modulation to enhance spectral differentiation,
demonstrate its reliability/reproducibility, and quantify
the metabolites in glioma subregions.Prospective.Phantoms, 5
healthy subjects, and 30 patients with suspected glioma. IDH
wild-type glioblastoma cases were evaluated to establish a
uniform group.3T, Research protocol: 2D 1H sLASER MRSI (40
and 120 ms TE) with water reference, 3D T1-weighted and 2D
T2-weighted. Trial-screening process: T1-weighted,
T1-weighted contrast-enhanced, T2-weighted, FLAIR.Spectral
simulations and phantom measurements were performed to
design and validate the protocol. Spectral quality/fitting
parameters for scan-rescan measurements were obtained using
LCModel. The proposed long-TE data were compared with
short-TE data. BraTS Toolkit was employed for fully
automated tumor segmentation.Scan-rescan comparison was
performed using Bland-Altman analysis. LCModel coefficient
of modeling covariance (CMC) between glutamate and glutamine
was mapped to evaluate their model interactions for each
spectral fitting. Metabolite levels in tumor subregions were
compared using one-way ANOVA and Kruskal-Wallis. A p value <
0.05 was considered statistically significant.Spectral
quality/fitting parameters and metabolite levels were highly
consistent between scan-rescan measurements. A negative
association between glutamate and glutamine models at short
TE (CMC = -0.16 ± 0.06) was eliminated at long TE (0.01 ±
0.05). Low glutamate in tumor subregions
(non-enhancing-tumor-core: 5.35 ± 4.45 mM,
surrounding-non-enhancing-FLAIR-hyperintensity: 7.39 ± 2.62
mM, and enhancing-tumor: 7.60 ± 4.16 mM) was found compared
to contralateral (10.84 ± 2.94 mM), whereas glutamine was
higher in surrounding-non-enhancing-FLAIR-hyperintensity
(9.17 ± 6.84 mM) and enhancing-tumor (7.20 ± 4.42 mM), but
not in non-enhancing-tumor-core (4.92 ± 3.38 mM, p = 0.18)
compared to contralateral (2.94 ± 1.35 mM).The proposed
MRSI protocol (~12 min) enables separate mapping of
glutamate and glutamine reliably along with other
MRS-detectable standard metabolites in glioma subregions at
3T.1 TECHNICAL EFFICACY: Stage 3.},
keywords = {1H sLASER long‐TE (Other) / MR spectroscopy (Other) /
brain tumor (Other) / glutamate (Other) / glutamine (Other)
/ reproducibility (Other)},
cin = {FM01},
ddc = {610},
cid = {I:(DE-He78)FM01-20160331},
pnm = {899 - ohne Topic (POF4-899)},
pid = {G:(DE-HGF)POF4-899},
typ = {PUB:(DE-HGF)16},
pubmed = {pmid:40197808},
doi = {10.1002/jmri.29787},
url = {https://inrepo02.dkfz.de/record/300296},
}