arXiv (All)
open access
Generative Priors for Total-Body PET Reconstruction
Total-body positron emission tomography (TB-PET) enables fast, low-dose, and dynamic imaging over a long axial field of view, but reconstruction remains challenging due to reduced counts, large image volumes, and increased data dimensionality. Generative models offer powerful learned priors for low-
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Penalized PET/CT Reconstruction Algorithms With Automatic Realignment for Anatomical Priors#1090636
- Maximum-Likelihood Joint Image Reconstruction/Motion Estimation in Attenuation-Corrected Respiratory Gated PET/CT Using a Single Attenuation Map#1090646
- 3D CT-to-PET Translation via Latent Brownian Bridge Diffusion#1070316
- Beyond the limits of space and time: Can 4D imaging enhance postmortem investigation?#1069005
- Highly accelerated 3D Cartesian MPnRAGE with implicit neural representation reconstruction#1012766
Record · ID 1388437
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive