Quantitative Metabolite Mapping of the Human Brain at 9.4 T
Andrew Martin Wright1,2, Saipavitra Murali Manohar1,3, and Anke Henning1,4
1MRZ, Max Planck Institute for Biological Cybernetics, Tuebingen, Germany, 2International Max Planck Research School, University of Tuebingen, Tuebingen, Germany, 3University of Tuebingen, Tuebingen, Germany, 4Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, TX, United States


Quantitative metabolite maps are reported from in vivo results at 9.4T. These maps are produced by quantifying with an internal water reference and utilize a novel T1 correction method applied to each voxel individually. Quantitative results allow cross-vendor and cross-site comparisons of results which may help to understand and characterize a variety neurological diseases.


High-resolution 1H MRSI of the human brain has been showcased previously from 9.4 T1, and displayed promise in detecting a more comprehensive neurochemical profile compared to results at lower field strengths2. MRSI adds valuable diagnostic information to understanding pathologies such as multiple sclerosis3, tumors4, epilepsy5 and neurodegenerative diseases6,7 due to detectable variations of the neurochemical profile. It has been shown that brain 1H MRSI at 7T brings additional information specifically with respect to the spatial inhomogeneity and spread of brain tumors or in multiple sclerosis where traditional anatomical imaging methods cannot visualize the extent of tissue abnormality beyond lesions3,8.

However, to acquire high-resolution 1H MRSI data with sufficient coverage of the brain in a time efficient manner a very short TR of < 300 ms must be used which leads to heavy T1-weighting of metabolites9,10. In addition the sensitivity profile of the receive coil causes a substantial bias field in uncorrected metabolite maps. Thus, this work presents the implementation of an internal water referencing framework for 1H FID MRSI data of the human brain acquired at 9.4T that considers the tissue composition of each voxel for water content and water T1 relaxation corrections and performs additional T1 corrections for all metabolites. Furthermore, previously measured water and metabolite T1-relaxation times for human grey matter (GM) and white matter (WM) tissue at 9.4 T11,12 have been used to produce quantitative metabolite maps of the human brain .


Three healthy volunteers participated in this study with IRB approval and signed consent. An 18/32 Tx/Rx coil13 was utilized to acquire elliptically shuttered high-resolution 1H FID-MRSI (TE* = 1.5ms, TR = 300ms) with a matrix size of 64x64 and FOV of 220 x 220 x 8 mm3 (nominal voxel size: 3.44 x 3.44 x 8 mm3), flip angle of 47$$$^{\circ}$$$, BW of 4000 Hz, and 512 data points acquired. To accompany metabolite data water references with identical sequence parameters were acquired. MRSI and water reference data were acquired from a slice positioned directly above the corpus callosum for each volunteer.

MP2RAGE data was acquired (Fig. 1) and reconstructed as described in Hagberg et al.11. This data was segmented using SPM1214 into grey matter (GM), white matter (WM), and cerebrospinal fluid (CSF) components to account for the tissue contribution to each voxel in the 1H MRSI data. An in house tool was used to align the MP2RAGE and MRSI data by using a rigid body transformation, and then tissue type distribution maps were down-sampled to match the resolution of the MRSI data (Fig. 2). The tissue type composition was then extracted from each voxel.

MRSI data was preprocessed by performing eddy current correction and frequency alignment by centering the water peak, residual water was removed by using Hankel Lanczos SVD, and first order phase was corrected for using a Yule-walked method15 for back prediction of the FID. Spectral fitting was performed in LCModel (v-6.3)16 using a FID-sequence simulated basis set created using the GAMMA library (https://scion.duhs.duke.edu/vespa/gamma).

The T1-relaxation for each metabolite was corrected for each voxel from the MRSI acquisition considering its specific voxel composition by utilizing the tissue probability maps and GM and WM T1 relaxation time estimates for 9.4T reported in Wright, et al.12. T1-relaxation times for water in GM, WM, and CSF were taken from 9.4T human results from Hagberg, et al.11 . Quantification of metabolite maps using an internal water reference was performed by utilizing the method as described by Gasparovic et al.17; hence, concentrations are reported in mmolal.


Figure 3 displays quantitative metabolite maps [mmolal] from two subjects, which agree with previously acquired results at ultra-high field for metabolite concentrations, and display tissue contrast for metabolites: tCho and Glu+Gln. As seen in Figure 4, the CRLB maps for the acquired data show a respectable range for tCho, tCr, NAA and Glu+Gln with a maximum value of 20.

Linear regressions (Fig. 5) were performed for multiple metabolites; these results show metabolite concentrations as a function of GM fraction.


When using a short TE* sequence macromolecules will obviously influence spectra. In this study the dkntmn parameter was set to 0.25 during fitting using LCModel to account for macromolecule influence. In the future, it would be advantageous to use tailored MM baselines either by simulation18 or as described by Považan, et al.19. Fitting macromolecules may also help to improve the variance seen in the Glu+Gln regression (Fig. 5).

Quantitative results tend to align with metabolite concentrations that have previously been reported at lower field strengths20–23. However, slight streaking, likely due to hardware instability, makes it difficult to fully assess differences between GM and WM concentrations for all metabolites visible in spectra. This problem will be addressed before moving forward with the acquisition of high-resolution 1H MRSI data to derive whole brain quantitative metabolite maps.


Quantitative, fully T1 corrected in vivo metabolite maps are reported for the first time from 9.4 T. Improving upon high-resolution metabolite mapping is valuable in diagnostic imaging to aid in characterizing progressions of neurological diseases. Having quantitative readouts allows for comparisons between results across sites and vendors, which is valuable when assessing data for larger meta-analysis studies.


The authors would like to gratefully acknowledge funding from the following sources: ERC Starting Grant, SYNAPLAST MR, (Grant Number: 679927), Horizon2020, CDS-QUAMRI, (Grant Number: 634541), and the Cancer Prevention and Research Institute of Texas (CPRIT) (Grant Number: RR180056)


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Fig. 1: MP2RAGE slice from the same position as MRSI data. Data was segmented into GM, WM, and CSF to perform tissue fraction corrections for quantitative metabolite maps. Images show summed slices which correspond to the MRSI 8mm slice thickness and ROI.

Fig2: Work flow of data acquisition through pre and post-processing steps to derive quantitative metabolite maps. MP2RAGE data was aligned to MRSI data by performing a rigid body transformation followed by matching MRSI voxel placement to the segmented images. Individual voxel tissue fractions were used to calculate the T1 of each metabolite in each respective voxel for voxel specific correction of the T1 of metabolites. Finally internal water referencing was performed considering tissue specific water content and T1 relaxation times to reconstruct quantitative metabolite maps.

Fig 3. metabolite maps shown in MRSI native resolution of 3.44 x 3.44 x 8 mm3 for each voxel. Contrast between GM and WM is clearly visible for tCho maps and slightly visible for Glu+Gln maps. All maps are quantitative with respect to an internal water reference and reported in mmolal.

Fig 4. CRLB maps show satisfactorily low CRLBs for the metabolites shown in Fig. 3. These maps are reported at the same resolution of the MRSI data from Fig. 3.

Fig 5. Data from all three subjects was pooled to plot linear regressions. The y-axis is given in units of mmolal which are post T1 corrections. The x-axis is given as the fraction of GM in each respective voxel.

Proc. Intl. Soc. Mag. Reson. Med. 28 (2020)