{
  "id": 18077,
  "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1813948",
  "title": "Scanner-agnostic MRI harmonization via SSIM-guided disentanglement",
  "summary": "IntroductionThe variability introduced by differences in MRI scanner models, acquisition protocols, and imaging sites hinders consistent analysis and generalizability across multicenter studies.MethodsWe present a novel image-based harmonization framework for 3D T1-weighted brain MRI, which disentangles anatomical content from scanner- and site-specific variations. The model incorporates a differentiable loss based on the Structural Similarity Index Measure (SSIM) to preserve biologically meanin",
  "authors": "Luca Caldera",
  "category": "research",
  "topics": "biotech",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-10T00:00:00.000Z",
  "fetched_at": "2026-08-11T05:10:37.351Z",
  "source_slug": "x-frontiers-in-artificial-intelligence",
  "source_name": "Frontiers in Artificial Intelligence",
  "source_homepage": "https://www.frontiersin.org/journals/artificial-intelligence",
  "ethics_ai_record_url": "https://ethics.ai/record/18077",
  "original_url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1813948",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}