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arXiv Computer Vision By Haley Duba-Sullivan, Patxi Fernandez-Zelaia, Obaidullah Rahman, Amirkoushyar Ziabari

Toward a Foundation Plug-and-Play Prior for Computed Tomography Reconstruction via a Multimodal Diffusion Model

Read the original on arXiv Computer Vision →

The paper proposes a single diffusion model trained across multiple imaging domains to act as a reusable prior for computed tomography (CT) reconstruction. By keeping the model frozen, it is applied to three distinct datasets—flaw analysis in additively manufactured metal parts using cone‑beam X‑ray CT, and concrete microstructure imaging with parallel‑beam neutron CT—each differing in modality, geometry, material, and degradation. In all cases, the method outperforms analytic reconstructions, demonstrating its potential as a foundation plug‑and‑play prior for heterogeneous CT problems.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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