Synopsis
Creatine(Cr) is a
significant brain metabolite and its alterations has been reported in various
disease conditions. In this study, chemical-exchange-saturation-transfer (CEST)
MRI of Creatine(CrCEST) was performed in human brain at 7T MRI scanner.
Numerical simulations were also carried out for evaluating contributions from
other brain metabolites to CrCEST and a method to reduce this contamination was
proposed based upon simulated data observations. Using, conventional CESTasy
method, CrCEST has ~50% contribution from Cr. Using proposed subtraction based
approach it is feasible to reduce contaminations from other
metabolites/molecules and hence making CrCEST more specific to Cr.Purpose
Chemical-exchange-saturation-transfer (CEST) MRI
techniques are being developed for in
vivo mapping of labile molecules and metabolites
1-6. CEST
mapping of Creatine(CrCEST) has been demonstrated well in phantom studies
6,7.
Application of CrCEST in human calf muscle during and in animal myocardium
infarction has been demonstrated at 7T
5 and 3T
4
respectively. Creatine(Cr) is a significant metabolite in brain and its
alterations has been reported in various disease conditions. Previously, we
reported CrCEST mapping in rat brain at 9.4T
8-9. Purpose of this study
was to perform CrCEST mapping in human brain and to evaluate possible
contributions from other metabolites/molecules to CrCEST contrast using
numerical simulations and to develop method to reduce the contamination.
Materials and
Methods
MRI data acquisition: The study was
conducted under an approved Institutional Review Board protocol. CEST imaging
of the human brain were performed at whole body 7T MRI scanner (Siemens). The
study protocol consisted of the following steps: a localizer, WASSR5,
CEST and B1 map data collection using pulse sequence reported previousely3.
CEST data were acquired at following frequency offsets (Δω): ±1 to ±3ppm with
0.2ppm steps. For CEST data, a saturation pulse of 1s and multiple B1rms of 0.7
µT, 1.4 µT, 2.2 µT, 2.9 µT were used.
Numerical
Simulation: Bloch
McConnell equation solvers incorporating relaxation and chemical exchange for
the experimental conditions were used in this study. Specifically, we
used a six-site exchange model (free-, bound pool of water, MI, Cr, Glu (+GABA),
and amide protons) for in vivo brain simulations as described previously in
supplementary table3.
Data
Analysis:
B0 map was computed using WASSR data10 and used for correction of CEST
data (at ±1.8ppm) for B0 in-homogeneities. CEST maps(∆ω =1.8ppm) were generated
using following equation: CESTasy(∆ω)=100*[Msat(-∆ω) – Msat(+∆ω)]/Msat(-∆ω), where Msat
(±∆ω) are the water magnetization obtained with saturation at a ‘+’ or ‘–’ ∆ω.
Plots between CESTasy contrast and B1rms were generated for different ROIs and
polynomial of 2nd degree was fitted over a limited range of B1rms for
generating calibration parameters. CEST map at B1rms of 2.2µT corrected for B1
in-homogeneity was generated. This map is termed as CrCEST map.
Results and Discussion
A
high B0 and B1 inhomogeneities were present in brain slice and final CEST maps
were corrected well for these field inhomogeneities. The CESTasy map at 1.8ppm
show ~12% contrast value in GM. WM tissue show less value compared to GM
tissue, which is in agreement with Cr distribution in brain. As such, CEST
contrast at 1.8ppm increased with increase in B1rms. Based upon Cr phantom and
simulation data, it has been observed that CrCEST increase with B1rms (for
B1rms range used in the current study); however, in brain data at high B1rms
contribution from other metabolites, particularly Glutamate starts increasing.
Therefore, in this study we have selected B1rms of 2.2µT for CrCEST mapping in
brain data. Numerical simulation results indicates that CESTasy map at 1.8ppm,
using B1rms of 2.2 µT, and duration of 1s, have around 52% contribution from Cr
for human brain data.
Following observations are drawn from
CESTasy curves of numerical simulations in Fig.2:
1)
Major
contamination to CrCEST (CEST_All in Fig.2) is from ‘Glutamate + GABA + APT’
2)
CESTasy
contrast from ‘Glutamate + GABA + APT’ at 3 ppm is similar to at 1.8 ppm either
due to fast exchange (Glu &GABA) or due to short T2 (APT)).
3)
Cr
contribution to CESTasy at 3 ppm is very small.
4)
Therefore,
major contamination from ‘Glutamate + GABA + APT’ to CrCEST can be removed by
subtracting CESTasy value at 3 ppm.
5)
This
resulted in new CrCEST values of ~ 4.1% for GM in comparison to conventional 11.8%,
which has predominant contribution from Cr.
For
in vivo data, using this new subtraction approach CrCEST contrast of ~4% was
observed in GM tissue. For in vivo data, relayed exchange NOE (rNOE) effect
(~-3.5ppm) might also contribute to CEST effect (negatively) at 1.8ppm. rNOE
effect is also quite broad and due to this, value at -3ppm and -1.8ppm should
be similar. Therefore, proposed CESTasy subtraction approach is expected to
remove rNOE contamination from CrCEST map. Further studies are being carried
out for optimizing this subtraction based approach to obtain CrCEST map in
human brain with minimal contamination from other metabolites.
Conclusion
In
conclusion, in brain conventional CESTasy map at ~1.8 ppm can be used to map Cr
distribution; however, a substantial contamination (~48%) from other
metabolites also contributes. Using proposed difference approach it is possible
to mitigate the contamination from other metabolites in CrCEST map.
Acknowledgements
This project was supported by internal grant
from IIT-Delhi and National Institute of Biomedical Imaging and Bioengineering
of the NIH through Grant Number P41-EB015893.References
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