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MedGSSR: Generalizable Medical Image Super-Resolution 3D Reconstruction via Hierarchical Feed-forward Gaussian Splatting — arXiv:2609.06874

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.06874
License (as archived): CC0-1.0
Archived: 2026-10-16T15:12:42.652Z
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How to cite

MedGSSR: Generalizable Medical Image Super-Resolution 3D Reconstruction via Hierarchical Feed-forward Gaussian Splatting — arXiv:2609.06874 — arXiv (en), https://arxiv.org/abs/2609.06874 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_09cdfe8fd451 (Metraipolis archive dc_09cdfe8fd451, sha256 bfca48f3e7647ee33ef10e4a835dfe44a6591f6295c4bec708fff0fe1da5bd99)

BibTeX

@misc{dc09cdfe8fd451,
  title = {MedGSSR: Generalizable Medical Image Super-Resolution 3D Reconstruction via Hierarchical Feed-forward Gaussian Splatting — arXiv:2609.06874},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.06874},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_09cdfe8fd451; sha256 bfca48f3e7647ee33ef10e4a835dfe44a6591f6295c4bec708fff0fe1da5bd99},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.06874. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

High-resolution volumetric medical imaging is critical for clinical diagnosis, yet acquisition is often limited by scanner hardware, scan time, and for CT, radiation dose. Medical 3D Super-Resolution (Med3DSR) offers a computational alternative, but existing methods commonly rely on per-subject optimization, pretrained priors, or coordinate-based implicit representations, which compromise anatomical fidelity and limit efficiency. To address these limitations, we present MedGSSR, a fully end-to-end feed-forward framework that represents volumes as an explicit 3D Gaussian field for Med3DSR. Unlike coordinate-based implicit functions, our explicit 3D Gaussian representation naturally enhances signal continuity and local high-frequency fidelity. Specifically, MedGSSR explicitly decouples the reconstruction process into coarse-grained structural preservation and fine-grained textural refinement through the proposed Pyramid Anatomical Encoder and a Hierarchical Gaussian Projector. To support arbitrary-scale super-resolution, we introduce sub-voxel Gaussian decomposition and a Differentiable Gaussian Voxelizer that directly queries the continuous 3D intensity field, reducing discretization artifacts. Extensive experiments on MRI and CT benchmarks demonstrate that MedGSSR significantly outperforms state-of-the-art methods. Notably, our framework exhibits robust generalizability across unseen datasets without requiring per-subject optimization, enabling fast inference and high-fidelity volumetric super-resolution in practical clinical settings. Our project webpage, including code, is at this https URL

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-09 in cs.CV).
Categorie arXiv: Image and Video Processing (eess.IV) ; Computational Engineering, Finance, and Science (cs.CE); Computer Vision and Pattern Recognition (cs.CV); Quantitative Methods (q-bio.QM).
Pagina del lavoro: https://arxiv.org/abs/2609.06874 — 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 2026-10-16T14:49:57.424Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-09-09?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).