Automated MR Spectrum Registration for In Vivo Mouse MEGA-PRESS Study at 9.4T And the Introduction of MRSMouse2.0
Jia Guo1, Douglas L. Rothman2, and Scott A. Small3

1Department of Biomedical Engineering, Columbia University, New York, NY, United States, 2Biomedical Engineering & Radiology and Biomedical Imaging, Yale University, New Haven, CT, United States, 3Departments of Neurology, Radiology or Psychiatry, Columbia University College of Physicians and Surgeons, New York, NY, United States


The goal of this study was to develop the tailored automated processing and quantification software for Meshcher–Garwood point resolved spectroscopy (MEGA-PRESS) in research of mouse models at 9.4T. The proposed software, MRSMouse, consists of two modules: the data loading module, MRSMouseLoad, which reads the Bruker time-domain raw data and processes it into a frequency-domain GABA-edited spectrum with bandwidth, frequency and phase drift correction through spectrum registration; and the spectral fitting module, MRSMouseFit, which quantifies the metabolite concentrations by modeling the spectra as a linear combination of the simulated basis-set and a smooth-spline in the frequency domain.


MRS spectral editing studies of mice have been limited due to difficulties in spectrum processing and the lack of a standardized software package for analysis. Using the same sequence, MRS scans from different subjects, or different sessions can have different receiver gain, water suppression, phase or frequency shift, bandwidth, signal-to-noise ratio (S/N) and etc. A direct comparison between metabolite quantities can be biased by the inter-scan variances from sources listed above or simply because of the difference in CSF fraction within the acquired voxel. If those variances get too large, the chance of detecting small metabolic changes in vivo vanishes due to the loss of sensitivity, which is the case in in vivo study of small animals where the targeting metabolites have intrinsically low concentrations in the brain.

We recently successfully developed an acquisition protocol and a Matlab toolkit for MEGA-PRESS study of the mouse brain at 9.4T1, to allow in vivo detection and quantification of GABA and glutamate/glutamine (Glx). MRSMouse loads Bruker raw data and performs automated spectral processing and quantitation. It was designed to process spectra of every single scan separately and quantify the level of metabolite using the pipeline which is similar to most MRS processing software2.

Registration is used extensively3,4 for MRI images which brings images into a common spatial coordinate in which each voxel corresponds to a predetermined anatomic entity and is necessary for automated groupwise analysis. In this work, we present an automated MEGA-PRESS spectra processing pipeline that is designed to enable an unbiased evaluation of inter-scan spectra through spectrum registration.


A Bruker BioSpec 94/20 system was used with a 23mm 1H-circularly-polarized transmit/receive-capable mouse head volume-coil. Four GABA-editing scans per mouse were acquired at the thalamus in a 2-hour time-course for 9 male wild-type mice with 1-1.5% isoflurane, using MEGA-PRESS protocol proposed previously1. A total of 320 repetitions were obtained (160 "on",160 "off") for each scan (NA=8).

Given a single MEGA-PRESS scan, multiple repetitions were acquired to gain S/N. Using MRSMouseLoad, we first run within-scan spectrum registration for each subject to reduce the between-repetition variances (i.e., phase/frequency drift) with the ‘off’ spectra as input. After within-scan spectrum registration, averaged-‘off’ spectrum, -‘on’ spectrum and -edited spectrum were generated for each scan. Then we continued with inter-scan spectrum registration for all scans obtained, to reduce the between-scan variances (phase, frequency and bandwidth differences) using their averaged ‘off’ spectra as the input. The spectrum registration for both steps used the ‘off’ spectra as input, and same adjustments were applied to their pair-wise ‘on’ spectra so that the co-registered GABA-edited spectra can be generated as the averaged difference (‘on’-‘off’).

As illustrated in Figure-1, the automatic spectrum registration algorithm involves two steps and runs recursively till the template converges: step-1 (blue), median template construction; step-2 (red), spectrum registration. The automatic process stops if the relative difference of the templates generated from two successive iterations is below a threshold, or the number of iterations reaches the predefined upper-bound. With this robust spectrum registration, an unbiased, median template is generated by iteratively aligning all input spectra in the frequency domain and a simultaneous spectral co-registration is achieved.

After spectrum registration, the GABA-edited spectra were quantified using the modified MRSMouseFit module, by adding a smooth spline term into the optimization model to address the baseline distortion. GABA and Glx levels were successfully calculated for all data.


Figure-1 outlines the workflow of our algorithm.

Examples of within-scan and inter-scan registration are shown in Figure-2 and -3, respectively.

Quantification of thalamic GABA and Glx levels were successfully obtained (Figure-4).

Figure-5 shows the time-course of thalamic GABA and Glx levels during anesthesia.


For in vivo MEGA-PRESS data of mouse brain, the limited S/N and the various sources of variances put the standard spectra processing approach at a disadvantage. In the present study, we developed and validated a spectrum registration based pipeline for in vivo MRS quantification in a fully automated fashion. A freely available toolkit, MRSMouse2.0, will be released with the aim of robust automatic processing and quantification of small animal MEGA-PRESS data in vivo.

Targeting metabolite levels can be quantified by normalizing to the within-scan NAA or Cr concentration.


We present the toolkit MRSMouse2.0, designed for automated analysis of MEGA-PRESS spectra of the mouse brain. With Bruker raw data supported, our toolkit is fully automated and rater-independent and is optimized to deal with low S/N spectra and perform groupwise study. We anticipate that it will be a useful tool for the adoption of J-difference editing studies in transgenic mice.


No acknowledgement found.


1. Guo, J. et al. In vivo detection and automatic analysis of GABA in the mouse brain with MEGA-PRESS at 9.4 T. NMR in Biomedicine, e3837-n/a, doi:10.1002/nbm.3837.

2. Mullins, P. G. et al. Current practice in the use of MEGA-PRESS spectroscopy for the detection of GABA. Neuroimage 86, 43-52, doi:10.1016/j.neuroimage.2012.12.004 (2014).

3. Avants, B. B. et al. A reproducible evaluation of ANTs similarity metric performance in brain image registration. Neuroimage 54, 2033-2044, doi:10.1016/j.neuroimage.2010.09.025 (2011).

4. Oliveira, F. P. & Tavares, J. M. Medical image registration: a review. Comput Methods Biomech Biomed Engin 17, 73-93, doi:10.1080/10255842.2012.670855 (2014).

5. Chen, L., Weng, Z., Goh, L. & Garland, M. An efficient algorithm for automatic phase correction of NMR spectra based on entropy minimization. Journal of Magnetic Resonance 158, 164-168, doi:https://doi.org/10.1016/S1090-7807(02)00069-1 (2002).


FIGURE 1: Overview of the proposed algorithm. Step-1 (blue), an (initial) template is generated as the median of all input spectra. The phase of the template is corrected through spectral Entropy minimization5 and its frequency is corrected by calibrating the 2.0 ppm NAA peak. Step-2 (red), the input spectra are registered to the template by minimizing their sum-of-squared-residuals through the phase, frequency or bandwidth adjustments. An updated template is generated as the median of the co-registered spectra (back to step-1) and a new iteration starts until stop criteria satisfied. Algorithm outputs are the registered spectra and an optimal template.

FIGURE 2: Results of within-scan spectrum registration. (A) The input ‘off’ spectra from a single scan. (B) The input ‘diff’ spectra from the corresponding single scan. (C) registered ‘off’ spectra. (D) registered ‘diff’ spectra. (E) Initial template (blue) and the optimal template (red) of the ‘off’ spectra. (F) Initial template (blue) and the optimal template (red) of the ‘diff’ spectra. The optimal template is constructed with corrected phase and frequency, narrower linewidth and more symmetric Lorentzian lineshapes.

FIGURE 3: Results of inter-scan spectrum registration. (A) The input ‘off’ spectra from multiple scans. (B) The input ‘diff’ spectra from the corresponding scans, (C) registered ‘off’ spectra. (D) Registered ‘diff’ spectra. (E) Initial template (blue) and the optimal template (red) of the ‘off’ spectra. (F) Initial template (blue) and the optimal template (red) of the ‘diff’ spectra. (E*, F*) The zoom-in views of E and F showing the optimal templates (red) have narrower linewidth and more symmetric Lorentzian lineshapes.

FIGURE 4: MRSMouseFit spectral fitting results of all 36 GABA-edited MEGA-PRESS ‘diff’ spectra (9 mice x 4-time points). Each GABA-edited spectrum (blue) was fitted (red) using simulated basis set including GABA and Glx (Glu:Gln = 8:2) and a smooth spline to model the baseline (magenta). Spectral fitting was successfully obtained with the fitting error smaller than 7.5% for each case and the fitting residue is showing in black. GABA peaks are located at 3 ppm and Glx peaks at 3.75 ppm. All spectra were displayed on the same amplitude scale. Smooth spline baseline correction will be added to MRSMouse2.0.

FIGURE 5: Examine the influence of isoflurane on mouse thalamic GABA and Glx levels. (A) Single voxel at mouse thalamus with size 5x3x3 mm3. (B) The paired t-test showed significant higher initial GABA levels compared to the following up scans (3rd and 4th-time points). (C) The paired t-test showed significant lower initial Glx levels compared to the following up scans (3rd and 4th-time points).

Proc. Intl. Soc. Mag. Reson. Med. 26 (2018)