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Related Experiment Video

Updated: Mar 15, 2026

The Use of Magnetic Resonance Spectroscopy as a Tool for the Measurement of Bi-hemispheric Transcranial Electric Stimulation Effects on Primary Motor Cortex Metabolism

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Correcting for Frequency Drift in Clinical Brain MR Spectroscopy.

Benjamin C Rowland¹, Huijun Liao¹, Fatah Adan¹

¹Center for Clinical Spectroscopy, Department of Radiology, Brigham and Women's Hospital, Boston, MA.

Journal of Neuroimaging : Official Journal of the American Society of Neuroimaging

|September 8, 2016

Summary

Temporal variations in magnetic resonance spectroscopy (MRS) data can distort results. Spectral registration effectively corrects frequency drift in MRS scans, improving metabolite quantification, especially after diffusion tensor imaging (DTI).

Keywords:

1H MRS frequency correction signal processing spectral registration spectroscopy

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Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

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Published on: June 30, 2018

Area of Science:

Neuroimaging
Biomedical Engineering
Spectroscopy

Background:

Magnetic resonance spectroscopy (MRS) commonly averages data to enhance signal-to-noise ratio.
Temporal B0 field variations in MRS can lead to spectral misalignment, peak broadening, and distorted quantification.
These errors are particularly problematic after sequences like diffusion tensor imaging (DTI).

Purpose of the Study:

To compare the effectiveness of different methods for correcting temporal B0 field variations in in vivo MRS data.
To evaluate the impact of these correction methods on spectral linewidths and metabolite concentration estimates.
To identify the optimal frequency correction technique for MRS data, especially when acquired after DTI.

Main Methods:

Applied three correction methods to 53 in vivo brain MRS scans: residual water peak alignment, creatine fitting, and spectral registration.
Acquired diffusion tensor imaging (DTI) prior to MRS in 32 subjects to induce higher frequency drift.
Compared spectral linewidths and metabolite concentration estimates before and after correction.

Main Results:

MRS data acquired after DTI showed a fourfold increase in frequency drift compared to data without DTI.
All three tested correction methods significantly improved spectral linewidths compared to uncorrected data.
Spectral registration demonstrated the best performance in linewidth improvement, with a minor margin over other methods.

Conclusions:

Frequency correction is a critical step in MRS data processing, significantly influencing metabolite quantification.
Echo-planar imaging, often used with MRS in clinical settings, exacerbates frequency drift issues.
Spectral registration is the most effective method for correcting frequency drift in MRS data.