{
  "abstract": "Motion artifacts are a major source of degradation in photoacoustic microscopy (PAM), where sequential raster scanning converts target-probe motion into structured coordinate mismatch, causing line misregistration, vessel discontinuity, truncation, and local deformation. We propose an acquisition-inspired framework for PAM motion artifact modeling and degradation-guided diffusion restoration. Motion corruption is formulated as raster-scan coordinate mismatch and decomposed into inter-line misregistration and intra-line sampling distortion. Based on this formulation, we construct a controllable simulator that generates paired clean-corrupted PAM images with explicit degradation priors. A conditional diffusion restoration model then uses motion-related priors for guidance and enforces degradation consistency by applying the forward inter-line and intra-line degradation operators during training. To support real-data inference, a degradation prior estimator predicts row-wise displacement and artifact-region information from corrupted images. Experiments on synthetic mouse brain PAM data show superior restoration performance, while selected real examples provide preliminary qualitative observations on non-synthetic motion-corrupted acquisitions.",
  "abstract_shown": true,
  "affiliations": [
    "Key Laboratory of Brain Health Intelligent Evaluation and Intervention (Beijing Institute of Technology), Ministry of Education, China; School of Medical Science and Engineering, Beijing Institute of Technology, China",
    "Key Laboratory of Brain Health Intelligent Evaluation and Intervention (Beijing Institute of Technology), Ministry of Education, China; Frontier Interdisciplinary Domain, Beijing Institute of Technology, Zhuhai, China",
    "Key Laboratory of Brain Health Intelligent Evaluation and Intervention (Beijing Institute of Technology), Ministry of Education, China; School of Computer Science and Technology, Beijing Institute of Technology, China"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Heqing Wang"
    },
    {
      "affs": [
        1
      ],
      "name": "Shuyan Zhang"
    },
    {
      "affs": [
        0
      ],
      "name": "Jingtan Li"
    },
    {
      "affs": [
        2
      ],
      "name": "Xinyi Lim"
    },
    {
      "name": "Naidi Sun"
    },
    {
      "name": "Bin Hu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/97dze6w8rd9sk.bib",
  "cited_by": 0,
  "doi": "10.1016/j.pacs.2026.100867",
  "fulltext_url": "http://www.sciencedirect.com/science/article/pii/S221359792600073X",
  "id": "97dze6w8rd9sk",
  "issns": [
    "2213-5979"
  ],
  "landing_url": "https://doi.org/10.1016/j.pacs.2026.100867",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "QC1-999"
    ],
    [
      "lcc",
      "QC221-246"
    ],
    [
      "lcc",
      "QC350-467"
    ],
    [
      "openalex",
      "2204|Photoacoustic and Ultrasonic Imaging"
    ]
  ],
  "openalex": "W7196941976",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-29",
  "publisher": "Elsevier",
  "ris": "https://research.draconitic.com/paper/97dze6w8rd9sk.ris",
  "sources": [
    {
      "first_seen": "2026-10-31T14:22:26Z",
      "native_id": "0861b6954082490eb2635f3acd7f1d78",
      "source": "doaj",
      "url": "https://doi.org/10.1016/j.pacs.2026.100867"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "lcc:QC350-467",
      "subfield": "optics-photonics"
    }
  ],
  "title": "Acquisition-inspired motion artifact modeling and degradation-consistent diffusion restoration in photoacoustic microscopy",
  "title_text": "Acquisition-inspired motion artifact modeling and degradation-consistent diffusion restoration in photoacoustic microscopy",
  "type": "peer-reviewed",
  "updated_at": "2026-10-31T14:22:26Z",
  "url": "https://research.draconitic.com/paper/97dze6w8rd9sk",
  "venue": "Photoacoustics",
  "versions": []
}