{
  "abstract": "PurposeTo develop and evaluate a recurrent neural network that operates directly on undersampled golden-angle radial k-space, enabling low-latency cardiac cine reconstruction for interventional cardiac MRI (iCMR).\n\nMethodsKOSTAL (K-space Optimized Spatial Temporal Architecture for Low latency) combines a per-coil k-space convolutional gated recurrent unit (convGRU), which learns a mask-conditioned view-sharing rule over the accumulated k-space, with an image-space recurrent module. It was trained and tested on the open-access multi-coil OCMR dataset using 10 simulated golden-angle spokes per frame against a 1000-spoke reference. Ablations varied recurrent module architecture and placement; temporal behavior was assessed using a imaging-plane transition test. Generalization was evaluated without retraining in 23 pediatric patients prospectively scanned during free-breathing using a radial golden-angle bSSFP sequence with binned XD-GRASP and clinical breath-held cine serving as references.\n\nResultsAt 10 spokes per frame, KOSTAL reached a median SSIM of 0.907 (gridding: 0.622) in under 34 ms of processing time per frame. Image quality (IQ) matched gridded results formed from 7-11 times more spokes. Recurrence in k-space rather than image space accounted for most of this gain (0.891 versus [≤]0.650). IQ recovered from an abrupt plane transition in 75 ms, versus 303 ms for 200-spoke gridding. KOSTAL had the best agreement with the binned reference and the highest long-axis CNR. KOSTAL accurately tracked left-ventricular blood-pool volume (end-diastolic volume bias-0.7 mL) with a shorter lag (71 ms) than iterative reconstruction (239-287 ms).\n\nConclusionLearned, mask-conditioned recurrence in k-space yields high image quality at a latency compatible with interventional guidance.",
  "abstract_shown": true,
  "affiliations": [
    "University of California, San Diego"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 10,
  "authors": [
    {
      "name": "M. Buckup"
    },
    {
      "name": "N. Mahajan"
    },
    {
      "name": "A. E. Rodriguez-Soto"
    },
    {
      "name": "J. Blood"
    },
    {
      "name": "S. Hegde"
    },
    {
      "name": "E. L. Schuchardt"
    },
    {
      "name": "B. Printz"
    },
    {
      "name": "H. K. Narayan"
    },
    {
      "name": "B. L. Gordon"
    },
    {
      "affs": [
        0
      ],
      "name": "F. Contijoch"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dbwsr1dvcra8m.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.21.26363257",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.09.21.26363257v1.full",
  "id": "dbwsr1dvcra8m",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.09.21.26363257v1",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "medrxiv",
      "cardiovascular medicine"
    ],
    [
      "openalex",
      "2741|Advanced MRI Techniques and Applications"
    ]
  ],
  "openalex": "W7214552354",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.09.21.26363257v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-21",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/dbwsr1dvcra8m.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:30Z",
      "native_id": "10.64898/2026.09.21.26363257",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.09.21.26363257v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:cardiovascular medicine",
      "subfield": "cardiovascular"
    }
  ],
  "title": "K-space Optimized Spatial Temporal Architecture for Low Latency in Interventional Cardiac MRI",
  "title_text": "K-space Optimized Spatial Temporal Architecture for Low Latency in Interventional Cardiac MRI",
  "type": "preprint",
  "updated_at": "2026-10-30T12:19:30Z",
  "url": "https://research.draconitic.com/paper/dbwsr1dvcra8m",
  "venue": "medRxiv",
  "versions": []
}