Artificial Intelligence in PET: an Industry Perspective

TL;DR

This paper discusses the potential and challenges of integrating AI into PET imaging, highlighting industry-specific issues and future opportunities for innovation and clinical adoption.

Contribution

It provides an overview of industry challenges and explores how AI can enhance PET imaging processes and workflows in the near future.

Findings

AI can improve all aspects of PET imaging from data acquisition to interpretation.

Industry-specific challenges include standardization and commercialization hurdles.

AI-enabled workflows may lead to innovative PET imaging solutions.

Abstract

Artificial intelligence (AI) has significant potential to positively impact and advance medical imaging, including positron emission tomography (PET) imaging applications. AI has the ability to enhance and optimize all aspects of the PET imaging chain from patient scheduling, patient setup, protocoling, data acquisition, detector signal processing, reconstruction, image processing and interpretation. AI poses industry-specific challenges which will need to be addressed and overcome to maximize the future potentials of AI in PET. This paper provides an overview of these industry-specific challenges for the development, standardization, commercialization, and clinical adoption of AI, and explores the potential enhancements to PET imaging brought on by AI in the near future. In particular, the combination of on-demand image reconstruction, AI, and custom designed data processing workflows may open new possibilities for innovation which would positively impact the industry and ultimately patients.