Artificial Intelligence-Guided PET Image Reconstruction and Multi-Tracer Imaging: Novel Methods, Challenges, And Opportunities
Movindu Dassanayake, Alejandro Lopez, Andrew Reader, Gary J.R. Cook, Clemens Mingels, Arman Rahmim, Robert Seifert, Ian Alberts, Fereshteh Yousefirizi
TL;DR
This paper discusses the potential of LAFOV PET/CT combined with AI techniques to improve image quality, enable new imaging applications, and address current limitations for clinical use.
Contribution
It introduces novel AI-guided methods for PET image reconstruction and multi-tracer imaging, highlighting challenges and future opportunities.
Findings
AI can enhance PET image resolution
LAFOV PET/CT enables ultra-low dose imaging
Potential for multiplexed and faster imaging
Abstract
LAFOV PET/CT has the potential to unlock new applications such as ultra-low dose PET/CT imaging, multiplexed imaging, for biomarker development and for faster AI-driven reconstruction, but further work is required before these can be deployed in clinical routine. LAFOV PET/CT has unrivalled sensitivity but has a spatial resolution of an equivalent scanner with a shorter axial field of view. AI approaches are increasingly explored as potential avenues to enhance image resolution.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.