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arXiv Computer Vision By Jiale Shen, Guolin Wang, Chenhao Wang, Xinhui Su, Wei Luo, Feng Yu

Efficient 3D Whole-Body PET Image Denoising via Conditional Rectified Flow With Optimized Sampling Strategy

Read the original on arXiv Computer Vision →

The paper introduces a one‑pass conditional 3D rectified flow (3D Flow) framework for denoising whole‑body PET images, employing an optimized non‑uniform sampling strategy and a linear‑interpolant velocity‑matching objective. It reconstructs a full 3D volume in about 30 seconds, dramatically faster than multi‑hour 3D diffusion models, while maintaining high global image quality and lesion conspicuity even at ultra‑low doses (down to 1/100 of standard). Zero‑shot transfer tests on independent clinical data demonstrate robust performance across datasets and unseen dose levels.

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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