{
  "annotation_id": "9b09ca62-1bc8-490e-b1b0-a38c1948f668",
  "date_created": "2026-02-17T05:53:19.952000Z",
  "date_modified": "2026-02-17T05:53:19.952000Z",
  "file_hash": "57420fbe488311866284a56bc79d693d8df465bab56adf9bdffe7272f94d2aaf",
  "private": false,
  "record": {
    "abstract": "Longitudinal brain MRI is essential for lifespan study, yet high attrition rates often lead to missing data, complicating analysis. Deep generative models have been explored, but most rely solely on image intensity, leading to two key limitations: 1) the fidelity or trustworthiness of the generated brain images are limited, making downstream studies questionable; 2) the usage flexibility is restricted due to fixed guidance rooted in the model structure, restricting full ability to versatile application scenarios. To address these challenges, we introduce DF-DiffCom, a Kolmogorov-Arnold Networks (KAN)-enhanced diffusion model that smartly leverages deformation fields for trustworthy longitudinal brain image completion. Trained on OASIS-3, DF-DiffCom outperforms state-of-the-art methods, improving PSNR by 5.6% and SSIM by 0.12. More importantly, its modality-agnostic nature allows smooth extension to varied MRI modalities, even to attribute maps such as brain tissue segmentation results.",
    "arxiv_id": "2601.09572",
    "authors": [
      "Tianli Tao",
      "Ziyang Wang",
      "Delong Yang",
      "Han Zhang",
      "Le Zhang"
    ],
    "categories": [
      "cs.CV"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "Trustworthy Longitudinal Brain MRI Completion: A Deformation-Based Approach with KAN-Enhanced Diffusion Model",
    "url": "https://arxiv.org/abs/2601.09572",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "621b78fb-893c-4a08-a56f-3f44f77ad791",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}