{
  "abstract": "Purpose: Low-rank reconstruction methods such as SPICE substantially improve Magnetic Resonance Spectroscopic Imaging (MRSI) reconstruction quality. However, SPICE does not provide an estimate of reconstruction uncertainty. A framework for posterior uncertainty estimation in SPICE reconstruction is proposed, hereafter referred to as the framework. Theory and Methods: A Laplace approximation is used to estimate the covariance of the reconstructed spatial coefficients. This covariance is propagated through the spectral basis and spectral fitting to obtain metabolite concentration uncertainty. Matrix-free Hessian--vector products and voxel-wise conjugate-gradient estimation are used to avoid explicit construction or inversion of the full Hessian, making the method tractable. Results: Validation using repeated in vivo 7 T MRSI acquisitions showed that the estimated posterior uncertainty exhibited spatial and spectral patterns similar to those of empirical repeated-scan uncertainty. Experiments further demonstrated how spatial regularization and k-space undersampling affect the estimated uncertainty. Conclusion: The framework provides a practical framework for uncertainty quantification when using SPICE reconstruction of MRSI. It could be adapted to other regularised low-rank reconstruction methods.",
  "abstract_shown": true,
  "affiliations": [
    "University of Oxford"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "name": "T. Lyu"
    },
    {
      "name": "S. Jbabdi"
    },
    {
      "name": "S. M. Finney"
    },
    {
      "affs": [
        0
      ],
      "name": "W. T. Clarke"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/c9978nydxh4qa.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.11.05.764591",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.11.05.764591v1.full",
  "id": "c9978nydxh4qa",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.11.05.764591v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "neuroscience"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.11.05.764591v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-05",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/c9978nydxh4qa.ris",
  "sources": [
    {
      "first_seen": "2026-11-07T00:21:14Z",
      "native_id": "10.64898/2026.11.05.764591",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.11.05.764591v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:neuroscience",
      "subfield": "neuroscience"
    }
  ],
  "title": "A Framework for SPICE MRSI Reconstruction Posterior Uncertainty Quantification",
  "title_text": "A Framework for SPICE MRSI Reconstruction Posterior Uncertainty Quantification",
  "type": "preprint",
  "updated_at": "2026-11-08T00:21:05Z",
  "url": "https://research.draconitic.com/paper/c9978nydxh4qa",
  "venue": "bioRxiv",
  "versions": []
}