Review Outlines AI Integration Across PET Reconstruction Pipeline

This review examines how artificial intelligence, particularly deep learning, is being incorporated across acquisition, correction, reconstruction, and postprocessing stages of PET imaging as digital detectors, long-axial FOV systems, and time-of-flight technology increase data demands. The authors state that most AI methods augment rather than replace established physics-based reconstruction frameworks, and that early clinical and multicenter studies have shown selected AI-based methods maintain noninferior diagnostic performance and quantitative metrics within defined contexts. The review also addresses regulatory and practical considerations, including intended-use validation, interoperability, traceability, and software lifecycle management, that shape deployment into routine practice.

Most relevant for: Relevant to clinical and technical teams involved in PET image acquisition, reconstruction, and quality oversight, including those evaluating adoption of AI-based reconstruction tools.

Summary written by an AI model from the source text and dated by the source's publication date. Check the original before acting on it. How MedFollow selects and summarizes updates.

Informational only. Not medical advice. Not a substitute for clinical judgment. Always consult current guidelines and your own clinical judgment.