ArticleMagnetic resonance in medicine2025
SMART MRS: A Simulated MEGA-PRESS ARTifacts toolbox for GABA-edited MRS.
Article in Magnetic resonance in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- GABA+-Edited Magnetic Resonance Spectroscopy Deep Learning Quality Assessment Framework.Magnetic resonance in medicine · 2026Article
- Identifying Out-of-Voxel Echoes in Edited MRS With Phase Cycle Inversion.Magnetic resonance in medicine · 2026Article
- SMART MRS: A Simulated MEGA-PRESS ARTifacts toolbox for GABA-edited MRS.Magnetic resonance in medicine · 2025Article
- Identifying out-of-voxel echoes in edited MRS with phase cycle inversion.bioRxiv : the preprint server for biology · 2025Article
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Authors and funding
4 authors.
Funding
Abstract
purposeTo create a Python-based toolbox to simulate commonly occurring artifacts for single voxel gamma-aminobutyric acid (GABA)-edited MRS data.
methodsThe toolbox was designed to maximize user flexibility and contains artifact, applied, input/output (I/O), and support functions. The artifact functions can produce spurious echoes, eddy currents, nuisance peaks, line broadening, baseline contamination, linear frequency drifts, and frequency and phase shift artifacts. Applied functions combine or apply specific parameter values to produce recognizable effects such as lipid peak and motion contamination. I/O and support functions provide additional functionality to accommodate different kinds of input data (MATLAB FID-A.mat files, NIfTI-MRS files), which vary by domain (time vs. frequency), MRS data type (e.g., edited vs. non-edited) and scale. A frequency and phase correction machine learning model experiment trained on corrupted simulated data and validated on in vivo data is shown to highlight the utility of our toolbox.
resultsData simulated from the toolbox are complementary for research applications, as demonstrated by training a frequency and phase correction deep learning model that is applied to in vivo data containing artifacts. Visual assessment also confirms the resemblance of simulated artifacts compared to artifacts found in in vivo data.
conclusionOur easy to install Python artifact simulated toolbox SMART_MRS is useful to enhance the diversity and quality of existing simulated edited-MRS data and is complementary to existing MRS simulation software.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.