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Reconstruction of 4D Mitral Regurgitation Hemodynamics from Sparse Planar Data using Deep Operator Networks with Test-Time Adaptation — arXiv:2609.20857

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.20857
License (as archived): CC0-1.0
Archived: 2026-10-10T01:47:23.337Z
SHA-256 of the archived text: 7f6ab19a1b5b2820e88c762d9005c769ae953e81bc0dd114b03183a1b8ef242c
Stable URL of this page: https://metraipolis.com/documento/dc_5973eeefba85

How to cite

Reconstruction of 4D Mitral Regurgitation Hemodynamics from Sparse Planar Data using Deep Operator Networks with Test-Time Adaptation — arXiv:2609.20857 — arXiv (en), https://arxiv.org/abs/2609.20857 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_5973eeefba85 (Metraipolis archive dc_5973eeefba85, sha256 7f6ab19a1b5b2820e88c762d9005c769ae953e81bc0dd114b03183a1b8ef242c)

BibTeX

@misc{dc5973eeefba85,
  title = {Reconstruction of 4D Mitral Regurgitation Hemodynamics from Sparse Planar Data using Deep Operator Networks with Test-Time Adaptation — arXiv:2609.20857},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.20857},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_5973eeefba85; sha256 7f6ab19a1b5b2820e88c762d9005c769ae953e81bc0dd114b03183a1b8ef242c},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.20857. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Quantifying mitral regurgitation severity remains limited by the assumptions of clinical flow convergence methods, while high-fidelity simulation and volumetric velocimetry are too slow for routine use. We investigate whether a learned solution operator can reconstruct transient three-dimensional transvalvular hemodynamics from the sparse observation an in-vitro experiment actually provides: a single planar velocity slice and two boundary pressure traces. A Deep Operator Network is pretrained on an experimentally benchmarked URANS database spanning eleven mitral regurgitation orifice phantoms, learning a mapping from a masked two-component planar velocity snapshot to the surrounding volumetric field, and is subsequently adapted to unseen target cases by fine-tuning on their sparse measurements. Adaptation reliably corrects the flow topology within the supervised plane, reorienting a strongly eccentric jet that the pretrained operator predicts as straight, and yields full-field predictions in minutes rather than the days required by the underlying simulations. Its influence decays sharply with distance from that plane, however: measured against phase-resolved particle image velocimetry, the reconstruction error rises from 24.6% at 2mm to 52.6% at 6mm, and the resulting mismatch between corrected and uncorrected layers degrades physical consistency. Single-plane supervision thus constrains the observed plane far more effectively than the surrounding volume, which we identify as the principal obstacle to coherent 4D reconstruction from sparse planar data.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-21 in cs.LG).
Categorie arXiv: Medical Physics (physics.med-ph) ; Machine Learning (cs.LG); Fluid Dynamics (physics.flu-dyn).
Pagina del lavoro: https://arxiv.org/abs/2609.20857 — 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 2026-10-10T01:14:16.679Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-09-21?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).