The 2024 MRSI Data Processing and Quantification Challenge Synthetic Dataset

Abstract

Synthetic data is central to magnetic resonance spectroscopy method development because they provide ground truths for software validation, reproducible benchmarking, and machine- and deep-learning training. In MRSI, synthetic data must capture spatially varying anatomy, field inhomogeneity, nuisance signals, and measurement effects. The 2024 MRSI Data Processing and Quantification Challenge Synthetic Dataset is a simulated 3T brain FID-MRSI resource with ground-truth metabolite maps developed as a controlled testbed for MRSI processing and quantification methods. Subject-specific simulations used anatomical images and field maps from Human Connectome Project subjects. Tissue masks, quantum-mechanically simulated metabolite basis functions, in vivo-derived macromolecular components, Bloch-simulated post-WET residual water, registered in vivo lipid signals, spectral baseline, B₀-dependent frequency shifts, Voigt lineshape variations, and complex Gaussian noise were combined in a forward model. Water and lipid signals were synthesized on a high-resolution grid and Fourier-truncated to the final echo-planar spectroscopic imaging grid to model the finite spatial point spread function. This resource has 24 training and 8 testing datasets containing contaminated FID-MRSI data, anatomical images, B₀ maps, metadata, and ground-truth metabolite and component signals. Forward-model parameters are documented, including tissue-specific metabolite concentrations and relaxation times, macromolecular amplitude ratios, the water model, noise, and field-inhomogeneity ranges. This synthetic benchmark supports development and comparison of MRSI processing, nuisance-signal removal, and metabolite-quantification methods, while enabling method evaluation. It illustrates how characterized synthetic data can support rigorous evaluation when ground truth is difficult or impossible to obtain.

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BibTeX RIS

BibTeX

@misc{lamaster20262024,
  title = {{The 2024 MRSI Data Processing and Quantification Challenge Synthetic Dataset}},
  author = {John T. LaMaster and Julian P. Merkofer and Dennis M. J. van de Sande and Brian J. Soher and Bernhard Strasser and Chao Ma},
  howpublished = {arXiv},
  year = {2026},
  month = sep,
  doi = {10.48550/arxiv.2609.02637},
  eprint = {2609.02637},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.02637},
  note = {CC BY-SA 4.0},
}

RIS

TY  - UNPB
TI  - The 2024 MRSI Data Processing and Quantification Challenge Synthetic Dataset
AU  - John T. LaMaster
AU  - Julian P. Merkofer
AU  - Dennis M. J. van de Sande
AU  - Brian J. Soher
AU  - Bernhard Strasser
AU  - Chao Ma
PY  - 2026
DA  - 2026/09/02
PB  - arXiv
DO  - 10.48550/arxiv.2609.02637
UR  - https://arxiv.org/abs/2609.02637
AB  - Synthetic data is central to magnetic resonance spectroscopy method development because they provide ground truths for software validation, reproducible benchmarking, and machine- and deep-learning training. In MRSI, synthetic data must capture spatially varying anatomy, field inhomogeneity, nuisance signals, and measurement effects. The 2024 MRSI Data Processing and Quantification Challenge Synthetic Dataset is a simulated 3T brain FID-MRSI resource with ground-truth metabolite maps developed as a controlled testbed for MRSI processing and quantification methods. Subject-specific simulations used anatomical images and field maps from Human Connectome Project subjects. Tissue masks, quantum-mechanically simulated metabolite basis functions, in vivo-derived macromolecular components, Bloch-simulated post-WET residual water, registered in vivo lipid signals, spectral baseline, B_0-dependent frequency shifts, Voigt lineshape variations, and complex Gaussian noise were combined in a forward model. Water and lipid signals were synthesized on a high-resolution grid and Fourier-truncated to the final echo-planar spectroscopic imaging grid to model the finite spatial point spread function. This resource has 24 training and 8 testing datasets containing contaminated FID-MRSI data, anatomical images, B_0 maps, metadata, and ground-truth metabolite and component signals. Forward-model parameters are documented, including tissue-specific metabolite concentrations and relaxation times, macromolecular amplitude ratios, the water model, noise, and field-inhomogeneity ranges. This synthetic benchmark supports development and comparison of MRSI processing, nuisance-signal removal, and metabolite-quantification methods, while enabling method evaluation. It illustrates how characterized synthetic data can support rigorous evaluation when ground truth is difficult or impossible to obtain.
ER  -

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