{
  "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, $B_0$-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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.02637",
  "author_total": 6,
  "authors": [
    {
      "name": "John T. LaMaster"
    },
    {
      "name": "Julian P. Merkofer"
    },
    {
      "name": "Dennis M. J. van de Sande"
    },
    {
      "name": "Brian J. Soher"
    },
    {
      "name": "Bernhard Strasser"
    },
    {
      "name": "Chao Ma"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dpvnt4x40ptza.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.02637",
  "fulltext_url": null,
  "id": "dpvnt4x40ptza",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.02637",
  "language": null,
  "licence": "CC-BY-SA-4.0",
  "licence_label": "CC BY-SA 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-sa/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "physics.med-ph"
    ],
    [
      "openalex",
      "2741|Advanced MRI Techniques and Applications"
    ]
  ],
  "openalex": "W7207530461",
  "pdf_url": "https://arxiv.org/pdf/2609.02637",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-02",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/dpvnt4x40ptza.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:16Z",
      "native_id": "2609.02637",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.02637"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.med-ph",
      "subfield": "biological-medical"
    }
  ],
  "title": "The 2024 MRSI Data Processing and Quantification Challenge Synthetic Dataset",
  "title_text": "The 2024 MRSI Data Processing and Quantification Challenge Synthetic Dataset",
  "type": "preprint",
  "updated_at": "2026-10-30T12:18:16Z",
  "url": "https://research.draconitic.com/paper/dpvnt4x40ptza",
  "venue": "arXiv",
  "versions": []
}