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arXiv AI By Linrui Dai, Jinxiu Liang, Chu Zhou, Mae Yamaguchi, Ryuya Takahashi, Imari Sato

Extremely Sparse-View Computed Tomography from 2D Projections via Pose-Aware Diffusion Priors

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The paper introduces a method for extremely sparse‑view computed tomography (xSV‑CT) that learns a pose‑aware diffusion prior from a single 2D projection per training sample. During inference, the diffusion process is guided by measured‑view fidelity, shared‑volume reprojection, and projection‑physics consistency to reconstruct 3D volumes from only 1, 4, or 8 views. Experiments on two public CT datasets show that this approach outperforms or matches baselines that rely on dense‑volume supervision, achieving superior novel‑view synthesis and competitive reconstruction quality.

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 AI.

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