Downfield spectra of human brain obtained with and without water suppression at 9.4T
Nicole D Fichtner1,2, Ioannis Giapitzakis3, Nikolai Avdievich3, Anke Henning2,3, and Roland Kreis1

1Depts. Radiology and Clinical Research, University of Bern, Bern, Switzerland, 2Institute for Biomedical Engineering, UZH and ETH Zurich, Zurich, Switzerland, 3Max Planck Institute for Biological Cybernetics, Tuebingen, Germany

Synopsis

Ultra high field strengths offer the benefit of higher signal to noise ratio as well as improved separation of metabolites in spectroscopy, which is beneficial for evaluating downfield peaks. In the current work, the metabolite cycling technique is implemented at 9.4T in order to evaluate the downfield part of the human brain spectrum. The 9.4T spectra confirm the 3T findings on exchanging peaks, and indicate that the higher field strength improves metabolite separation, allowing for better quantification of exchanging peaks, which is also of great interest for chemical exchange dependent saturation transfer experiments.

Introduction and Purpose

Ultra-high field strengths offer the benefit of higher signal to noise ratio as well as improved separation of metabolites in spectroscopy. This improvement is expected to enable easier observation and characterization of metabolites that are on the downfield side of the spectrum, as they are often more difficult to see and separate at lower field strengths[1,2]. In particular, the downfield spectrum may provide information on metabolites that are not present or not so easily separated upfield[3,4], and furthermore can provide information on exchanging species, which is of great interest for chemical exchange dependent saturation transfer experiments. In the current work, the metabolite-cycling technique[5,6] is implemented at 9.4T in order to evaluate the downfield part of the human brain spectrum without water suppression, as well as to investigate any effects due to exchange after water suppression.

Materials and Methods

Three healthy volunteers were scanned using a 9.4T Siemens whole-body human MRI scanner and a four-channel transceiver array surface coil[7]. A 20x20x20mm3 region was selected in a mix of grey and white matter at the back of the brain. A Siemens FASTERMAP B0 shimming implementation was used prior to running metabolite-cycling scans. A 22ms asymmetric adiabatic metabolite cycling pulse was applied in the TM period of a STEAM sequence with TR/TE/TM=3000/8/45ms[8]. For each of the volunteers, 256 averages were acquired for the non-water suppressed metabolite-cycling sequence, as well as 256 averages for the same sequence with a SODA water suppression sequence[9] applied before STEAM, with an acquisition time of 12.8min for each scan. In one volunteer, only 128 averages were reconstructed as the other 128 showed motion artifacts. During the measurement, shots were stored individually; following acquisition, offline processing was performed: spectral alignment, scaling, and eddy current correction using the water reference signal; and averaging and coil combination.

Qualitative comparisons were also performed between the current study’s data and previous data acquired at 7T (Philips Achieva) using a conventional STEAM sequence with VAPOR water suppression for seven volunteers (256 averages each, quadrature transceiver surface coil (Rapid Biomedical) at TR/TE/TM = 4000/13/26ms[10].

Results

Non-water suppressed and water suppressed spectra from one volunteer are shown in Fig. 1. The averages over our initial three volunteers’ spectra are shown in Fig. 2, along with the difference spectrum visualizing the effect of exchange-related signal drop due to water suppression. Fig. 3 shows the averages of water-suppressed spectra from 3 volunteers at 9.4T and from 7 volunteers at 7T.

Discussion

The spectra in Fig. 1 are representative of the data acquired at 9.4T for all individuals, including peak separation and resolution; the individual spectra show some increase in separation of metabolites compared to 3T[1] and 7T (Fig. 3) and indicate differences between non-suppressed and water-suppressed data.

The averaged spectra in Fig. 2 show clear differences between water suppressed and non-water suppressed spectra due to chemical exchange of protons between the respective molecules and water. The respective difference spectrum indicates the most prominent exchanging peaks, which are similar to the exchanging peaks determined at 3T when comparing to Ref.[1]. There is some increased structure in the 8.2-8.5ppm region, both in the averaged spectra and the difference spectrum, and the peak at 5.8ppm is much stronger than at both lower field strengths (not expected to be fully accountable by differences in water suppression pulse bandwidths); furthermore, the two peaks at 6.0 and 6.1ppm appear stronger and better separated at 9.4T. Overall, the 9.4T spectra confirm the 3T findings on exchanging peaks, and improve slightly on metabolite separation.

In Fig. 3, major differences are highlighted between field strengths, in particular indicating peaks that are not as easily visible at 7T. Some of these differences may be due to the shorter SODA water suppression sequence used at 9.4T (130ms), compared to the VAPOR sequence (700ms). The longer water pre-suppression delay in VAPOR may well accentuate the exchange-related signal loss. However, the peak at approximately 6.1ppm does not appear to be greatly affected by exchange at 9.4T, but still is not visible at 7T. The other difference between the data is that the 9.4T data was collected in a voxel of mixed white and grey matter, compared to the 7T data which is from mainly grey matter.

The current work is a preliminary investigation into further elucidating the downfield part of the human brain spectrum and into measuring effects of exchange at ultra-high field strengths, and indicates that the higher field strength improves metabolite separation, allowing for better identification and quantification of exchanging peaks.

Acknowledgements

This research was supported by the Swiss National Science Foundation (#320030_156952).

References

1. MacMillan EL, Chong DGQ, Dreher W, Henning A, Boesch C, Kreis R. Magnetization exchange with water and T1 relaxation of the downfield resonances in human brain spectra at 3.0 T. Magn Reson Med 2011;65:1239-1246.

2. Kreis R, Boesch C, Vermathen P. Characterization of the downfield part of the human cerebral 1H MR spectrum at 3T [abstract]. Proc Intl Soc Mag Reson Med 2006: 2636.

3. Henning A, Fuchs A, Boesch C, Boesiger P, and Kreis R. Downfield spectra at ultrahigh field [abstract]. Proc Intl Soc Mag Reson Med 2008: 777.

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7. Pfrommer A, Avdievich N, und Henning A. Four channel transceiver array for functional magnetic resonance spectroscopy in the human visual cortex at 9.4T [abstract]. Proc Intl Soc Mag Reson Med 2014: 1305.

8. Giapitzakis IA , Nassirpour S , Avdievich N , Kreis R and Henning A. Metabolite cycled single voxel 1H spectroscopy at 9.4T [abstract]. Proc Intl Soc Mag Reson Med 2015: 4696.

9. Giapitzakis IA, Nassirpour S, and Henning A. Short duration water suppression using optimised flip angles (SODA) at ultra high fields [abstract]. 32nd Annual Scientific Meeting ESMRMB 2015: 519, S401-S402.

10. Fichtner ND, Henning A, Zoelch N, Boesch C, and Kreis R. T2 estimation of downfield metabolites in human brain at 7T [abstract]. Proc Intl Soc Mag Reson Med 2015: 745.

Figures

Fig. 1: Representative sample of non-water suppressed and water suppressed spectra from an individual volunteer.

Fig. 2: Average non-water suppressed and water suppressed spectra over the three volunteers (apodized by 3Hz). The difference between the averaged spectra is shown on the bottom. The largest differences are in the 8.2-8.5ppm region and at 7.8 and 5.8ppm.

Fig. 3: Averaged water suppressed spectra at 9.4T and 7T (apodized by 3Hz, 3 vs 7 volunteers, respectively). Major differences are shown by grey lines. However, some differences may be attributable to different durations of the water suppression sequences (130ms at 9.4T, 700ms at 7T) rather than different field strengths.



Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)
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