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MRI Super-Resolution with RCDM/WaveMix and Task-Aware Segmentation — arXiv:2609.39083

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.39083
License (as archived): CC0-1.0
Archived: 2026-10-07T14:45:47.003Z
SHA-256 of the archived text: f12e05d9f8ec3c5bdadc9facc697aff0cd64c0a86314fb17d1538690ee41ef30
Stable URL of this page: https://metraipolis.com/documento/dc_3ca549ced744

How to cite

MRI Super-Resolution with RCDM/WaveMix and Task-Aware Segmentation — arXiv:2609.39083 — arXiv (en), https://arxiv.org/abs/2609.39083 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_3ca549ced744 (Metraipolis archive dc_3ca549ced744, sha256 f12e05d9f8ec3c5bdadc9facc697aff0cd64c0a86314fb17d1538690ee41ef30)

BibTeX

@misc{dc3ca549ced744,
  title = {MRI Super-Resolution with RCDM/WaveMix and Task-Aware Segmentation — arXiv:2609.39083},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.39083},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_3ca549ced744; sha256 f12e05d9f8ec3c5bdadc9facc697aff0cd64c0a86314fb17d1538690ee41ef30},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.39083. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Super-resolution and quality enhancement of 1.5\,T brain MRI are normally validated with image-fidelity metrics, although their purpose is to improve downstream analysis. We study whether enhancement improves tissue segmentation, and for which segmenters. We propose an unpaired, physics-guided training pipeline for a lightweight ($\le$2.5\,M parameter) recurrent convolutional enhancer: a six-module stochastic 1.5\,T degradation operator, a residual adversarial network that adds scanner-specific texture without moving anatomy, and a cycle-consistent objective with an anti-identity penalty that rules out the copy solution. We then train U-Net, Swin-UNet and wavelet token-mixing segmenters \citep{jeevan2023wavemix} from scratch on either raw or enhanced 1.5\,T images of the same subjects, using identical labels and subject-level splits, for three enhancer variants and two datasets. On ABIDE (41 held-out subjects, FreeSurfer labels) enhancement significantly improves the wavelet segmenter (mean Dice $+0.014$, Wilcoxon $p=3.5\times10^{-5}$; CSF $+0.018$, grey matter $+0.013$), significantly degrades the U-Net ($-0.008$, $p=5.1\times10^{-4}$) and leaves Swin-UNet unchanged. On IXI, whose labels come from FSL-FAST, enhancement lowers Dice for all nine pairings, almost entirely through CSF; we trace this to spatially implausible CSF voxels in the labels that penalise smoother predictions. Enhancement of low-field MRI should therefore be validated per downstream model and against reliable labels.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-10-01 in cs.CV).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2609.39083 — 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 2026-10-07T14:19:00.661Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-10-01?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).