arXiv:2608. 20112v1 Announce Type: cross Abstract: Generative models have shown strong potential for positron emission tomography (PET) image reconstruction.
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.
The paper introduces a high‑resolution Rectified Flow Matching model trained on 256×256 chest CT images to serve as a generative prior for CT reconstruction. A two‑stage training strategy—initial strong anatomically informed augmentation followed by fine‑tuning—helps mitigate overfitting and improve structural fidelity. When evaluated on various CT inverse problems, Flow Matching‑based reconstruction methods outperform diffusion‑based algorithms in PSNR, SSIM, and perceptual quality while requiring fewer sampling steps.
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.
arXiv:2603. 00205v2 Announce Type: replace-cross Abstract: Generative models, particularly Diffusion Models (DM), have shown strong potential for Computed Tomography (CT) reconstruction serving as expressive priors for solving ill-posed inverse problems.
Generative Translation Priors (GTP) is a Bayesian framework that repurposes diffusion-based image-to-image translation models as cross-modality priors for ill‑posed imaging inverse problems. It incorporates target‑domain measurements via likelihood guidance, steering the translation toward the desired posterior distribution. The authors analyze the resulting posterior dynamics, identify an intrinsic bias from likelihood guidance, and propose a ground‑truth‑free metric to estimate this bias, leading to two discretized GTP algorithms that demonstrate high‑fidelity reconstruction in CT and PET imaging using complementary side information.
arXiv:2609.12860v1 Announce Type: new Abstract: Computed tomography (CT) and positron emission tomography (PET) provide complementary anatomical and functional information for cancer diagnosis and tr...
arXiv:2610.17401v1 Announce Type: new Abstract: Incomplete field-of-view (FOV) diffusion MRI (dMRI) acquisitions compromise whole-brain white matter analysis by excluding brain regions from tractogra...