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MOSAIC: A Self-supervised Dynamic Multi-encoding Reconstruction Framework for 3D Late Gadolinium Enhancement MRI — arXiv:2608.19506

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.19506
License (as archived): CC0-1.0
Archived: 2026-10-18T13:42:44.381Z
SHA-256 of the archived text: 8728062143fc85dc8758074e73e5bb7d70843888e3e51fdfa1d0831076419e61
Stable URL of this page: https://metraipolis.com/documento/dc_fb277e477bee

How to cite

MOSAIC: A Self-supervised Dynamic Multi-encoding Reconstruction Framework for 3D Late Gadolinium Enhancement MRI — arXiv:2608.19506 — arXiv (en), https://arxiv.org/abs/2608.19506 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_fb277e477bee (Metraipolis archive dc_fb277e477bee, sha256 8728062143fc85dc8758074e73e5bb7d70843888e3e51fdfa1d0831076419e61)

BibTeX

@misc{dcfb277e477bee,
  title = {MOSAIC: A Self-supervised Dynamic Multi-encoding Reconstruction Framework for 3D Late Gadolinium Enhancement MRI — arXiv:2608.19506},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.19506},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_fb277e477bee; sha256 8728062143fc85dc8758074e73e5bb7d70843888e3e51fdfa1d0831076419e61},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.19506. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Purpose: To develop and evaluate a self-supervised dynamic reconstruction framework for highly undersampled dual-echo three-dimensional late gadolinium enhancement (3D LGE) MRI. Methods: MOSAIC jointly models multi-echo image content, coil sensitivity maps, and beat-specific nonrigid motion directly from acquired undersampled data, without requiring fully sampled training datasets or accurate precomputed sensitivity maps. Unlike existing methods that bin the acquired data into different motion states, with or without motion compensation, MOSAIC reconstructs a motion-resolved 3D LGE image from each heartbeat. The method was evaluated using digital phantoms with simulated myocardial scars and in vivo animal and human studies. Results: In phantom experiments, MOSAIC achieved higher peak signal-to-noise ratio and structural similarity index measure than low-rank deep image prior reconstruction and ablation variants of MOSAIC. In animal and human studies, MOSAIC achieved higher blinded expert image-quality scores than inline image-navigated compressed-sensing and low-rank deep image prior reconstructions. Conclusion: MOSAIC demonstrated the feasibility of motion-resolved free-breathing dual-echo 3D LGE MRI at acceleration factors exceeding 1,000, with improved detail preservation and artifact suppression relative to the state-of-the-art comparison methods.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-08-21 in eess.IV).
Categorie arXiv: Image and Video Processing (eess.IV).
Pagina del lavoro: https://arxiv.org/abs/2608.19506 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-18T13:40:13.353Z dalla pagina https://arxiv.org/catchup/eess.IV/2026-08-21?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).