{
  "abstract": "Background: Dynamic MRI reconstruction often requires accurate, fast, and low‐latency algorithms that generalize across multiple applications, sampling schemes, and rates, without problem‐specific parameter tuning. Existing methods either lack generalizability or require extensive parameter adjustment.\n\nPurpose: To develop generalizable, low‐latency MRI reconstruction algorithms based on low‐rank (LR) and low‐rank + sparse (L+S) models that are accurate, fast, and require minimal parameter tuning across diverse dynamic MRI settings.\n\nMethods: We propose a novel Alternating Gradient Descent and Minimization (AltGDmin) approach for L+S matrix recovery from column‐wise undersampled measurements, and extend it to a 3‐level hierarchical L+S model for dynamic MRI. A few‐shot (FS) learning modification (AGM‐L+S‐FS) is introduced to enable near low‐latency reconstruction with per‐frame processing time comparable to or faster than scan time. A simpler LR‐only variant is also evaluated.\n\nResults: The proposed algorithms were validated on 6 prospective and 12 retrospective dynamic MRI datasets, covering cardiac, speech, and abdominal applications, with Cartesian, pseudo‐radial, radial, and spiral sampling schemes. Experiments demonstrate accurate reconstruction, generalizability across datasets and sampling rates, and low‐latency performance. Detailed comparisons with state‐of‐the‐art LR, L+S, low‐latency, and unsupervised deep learning methods show superior or comparable performance.\n\nConclusions: Our AGM‐L+S‐FS approach provide generalizable, fast, and low‐latency MRI reconstruction across multiple dynamic MRI applications and sampling schemes, without the need for problem‐specific parameter tuning.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Electrical and Computer Engineering Iowa State University Ames, Iowa USA",
    "Roy J. Carver Department of Biomedical Engineering University of Iowa Iowa City, Iowa USA"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Silpa Babu"
    },
    {
      "affs": [
        1
      ],
      "name": "Sajan Goud Lingala"
    },
    {
      "affs": [
        0
      ],
      "name": "Namrata Vaswani"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/f3gnvd0ntzg17.bib",
  "cited_by": 0,
  "doi": "10.1002/mp.70713",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13714577/",
  "id": "f3gnvd0ntzg17",
  "issns": [
    "0094-2405",
    "2473-4209"
  ],
  "landing_url": "https://doi.org/10.1002/mp.70713",
  "language": "en",
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Research Article"
    ],
    [
      "openalex",
      "2741|Advanced MRI Techniques and Applications"
    ]
  ],
  "openalex": "W7224085908",
  "pdf_url": "https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/mp.70713",
  "pmcid": "PMC13714577",
  "pmid": "42947508",
  "published": "2026-10-27",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/f3gnvd0ntzg17.ris",
  "sources": [
    {
      "first_seen": "2026-11-01T12:22:02Z",
      "native_id": "PMC13714577",
      "source": "pmc",
      "url": "https://doi.org/10.1002/mp.70713"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2741|Advanced MRI Techniques and Applications",
      "subfield": "imaging"
    }
  ],
  "title": "Low latency and generalizable dynamic MRI via L+S alternating gradient descent and minimization",
  "title_text": "Low latency and generalizable dynamic MRI via L+S alternating gradient descent and minimization",
  "type": "peer-reviewed",
  "updated_at": "2026-11-01T12:22:02Z",
  "url": "https://research.draconitic.com/paper/f3gnvd0ntzg17",
  "venue": "Medical Physics",
  "versions": []
}