{
  "abstract": "Multi-energy CT (MECT) offers unique advantages in material decomposition, tissue characterization, and functional imaging, positioning it as a pivotal direction for next-generation CT. Currently, standardized scanning protocols for MECT have not yet been established. Considering growing public concern over X-ray radiation exposure, we propose a complementary sparse-view scanning protocol tailored for MECT, which reduces radiation dose while maximizing angular coverage. To reconstruct high-quality images from these sparse-view data and ensure algorithmic reliability in practical applications, we introduce an Online Adaptive Reconstruction (OA-Recon) framework that adapts robustly to varying acquisition settings through two designs. First, it adopts a Bayesian adaptation strategy for instance-specific optimization while preserving the learned prior. Second, it incorporates a Frequency-adaptive and Physics-informed Network (FaPiNet) for adaptive feature extraction and acquisition-conditioned feature modulation. In addition, it incorporates a spectral attention mechanism to fully exploit complementary information across energy channels. Experiments on simulated MECT and real mouse PCCT data show that FaPiNet-OA-Recon achieves better performance in suppressing streak artifacts, restoring image details, and maintaining CT-value accuracy. More importantly, OA-Recon demonstrates adaptability to changes in view, spectrum, and anatomy, providing a preliminarily feasible solution for clinical applications of MECT.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 11,
  "authors": [
    {
      "name": "Xi Wang"
    },
    {
      "name": "Tong Lin"
    },
    {
      "name": "Jiashun Wang"
    },
    {
      "name": "Xu Ji"
    },
    {
      "name": "Chun Yang"
    },
    {
      "name": "Tianyu Tang"
    },
    {
      "name": "Yikun Zhang"
    },
    {
      "name": "Jean-Louis Coatrieux"
    },
    {
      "name": "Jiasong Wu"
    },
    {
      "name": "Shenghong Ju"
    },
    {
      "name": "Yang Chen"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/b0khkejtxwnr0.bib",
  "cited_by": 0,
  "doi": "10.1109/tmi.2026.3728200",
  "fulltext_url": null,
  "id": "b0khkejtxwnr0",
  "issns": [
    "0278-0062"
  ],
  "landing_url": "https://hal.science/hal-05826365v1",
  "language": "en",
  "licence": "CC-BY-NC-4.0",
  "licence_label": "CC BY-NC 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "hal",
      "0.sdv"
    ],
    [
      "hal",
      "1.sdv.ib"
    ],
    [
      "openalex",
      "2204|Advanced X-ray and CT Imaging"
    ]
  ],
  "openalex": "W7204550045",
  "pdf_url": "https://hal.science/hal-05826365/document",
  "pmcid": null,
  "pmid": "42664097",
  "published": "2026-10-23",
  "publisher": "Institute of Electrical and Electronics Engineers",
  "ris": "https://research.draconitic.com/paper/b0khkejtxwnr0.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:01Z",
      "native_id": "hal-05826365",
      "source": "hal",
      "url": "https://hal.science/hal-05826365v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "hal:1.sdv.ib",
      "subfield": "systems-synthetic"
    }
  ],
  "title": "OA-Recon: An Online Adaptive Reconstruction Framework for Sparse-View Multi-Energy CT",
  "title_text": "OA-Recon: An Online Adaptive Reconstruction Framework for Sparse-View Multi-Energy CT",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:01Z",
  "url": "https://research.draconitic.com/paper/b0khkejtxwnr0",
  "venue": "IEEE Transactions on Medical Imaging",
  "versions": []
}