{
  "abstract": "Diffusion MRI across multiple gradient directions, b-values and echo times (TEs) provides complementary tissue contrast but requires extensive sampling. Joint k-space and q-space undersampling requires reconstruction that preserves diffusion- and TE-dependent signal variation while exploiting shared anatomical information. Here we introduce DIFFUSE (Diffusion-relaxometry Implicit Field Fitting from Undersampled k-q Space across Echoes), a subject-specific joint k-q-TE image reconstruction framework. It fits a single implicit neural representation (INR) directly to complementary undersampled multi-coil k-space measurements across diffusion encodings and echo times. The INR (a self-supervised neural network) maps spatial location, b-value and TE to baseline b0 signals and spherical harmonic coefficients for directional diffusion-weighted image reconstruction. The measurement-specific forward model incorporates phase, coil sensitivities and motion and eddy current related transforms. We evaluated DIFFUSE using retrospective Human Connectome Project data, controlled motion simulations and in vivo multi-shell and multi-TE acquisitions. At sixfold in-plane acceleration, DIFFUSE reduced normalized mean squared error by 57-73% relative to SENSE across two diffusion shells in the retrospective experiment. In the controlled motion experiment, motion-aware DIFFUSE reduced normalized mean squared error by 66-72% relative to static DIFFUSE. The prospective study demonstrated multi-shell reconstruction at 1.3 mm isotropic resolution with significantly high signal-to-noise ratio (SNR). In a separate multi-TE experiment, a single representation jointly reconstructed all nine contrasts corresponding to 3 b-value/TE combinations. DIFFUSE thus provides a common reconstruction strategy for accelerated protocols spanning multiple diffusion encodings and echo times.",
  "abstract_shown": true,
  "affiliations": [
    "Harvard medical school"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Y. Wang"
    },
    {
      "name": "Q. Liu"
    },
    {
      "name": "W. Consagra"
    },
    {
      "name": "L. Ning"
    },
    {
      "name": "K. Setsompop"
    },
    {
      "name": "Y. Rathi"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/egxgx4btb2yqg.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.21.760608",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.21.760608v1.full",
  "id": "egxgx4btb2yqg",
  "issns": [
    "2692-8205"
  ],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.21.760608v1",
  "language": "en",
  "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"
    ],
    [
      "heading",
      "Article"
    ],
    [
      "openalex",
      "2741|Advanced MRI Techniques and Applications"
    ]
  ],
  "openalex": "W7223675748",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.21.760608v1.full.pdf",
  "pmcid": "PMC13733696",
  "pmid": "42990112",
  "published": "2026-10-21",
  "publisher": "Cold Spring Harbor Laboratory Preprints",
  "ris": "https://research.draconitic.com/paper/egxgx4btb2yqg.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:43Z",
      "native_id": "10.64898/2026.10.21.760608",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.21.760608v1"
    },
    {
      "first_seen": "2026-11-08T12:23:24Z",
      "native_id": "PMC13733696",
      "source": "pmc",
      "url": "https://doi.org/10.64898/2026.10.21.760608"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:neuroscience",
      "subfield": "neuroscience"
    }
  ],
  "title": "Joint reconstruction of multi-contrast relaxation-diffusion imaging",
  "title_text": "Joint reconstruction of multi-contrast relaxation-diffusion imaging",
  "type": "preprint",
  "updated_at": "2026-11-08T12:23:24Z",
  "url": "https://research.draconitic.com/paper/egxgx4btb2yqg",
  "venue": "bioRxiv",
  "versions": []
}