Towards trustworthy system latency optimization of Positron Emission Tomography with RBC and GNN

Abstract

Positron Emission Tomography (PET) is a highly sensitive imaging instrument, and its system latency plays a pivotal role in real-world applications. We propose a novel trustworthy optimization method to reduce PET system latency by leveraging Role-Based Collaboration (RBC) and Graph Neural Networks (GNN). First, we establish an end-to-end physical model for latency computation, with specific consideration of PET’s characteristics. Then, the GRA (Group Role Assignment)-PET model is proposed based on the E-CARGO (Environments–Classes, Agents, Roles, Groups, and Objects)/GRA model to comprehensively manage the task offloading strategy between PET detector modules and GPU servers, incorporating mechanisms for network contention resolution and computational resource arbitration, with a proof of algorithm convergence provided. Finally, we design an Extended Integer Linear Programming (x-ILP) solution to the proposed model. Furthermore, we develop a trustworthy GNN-based event-filtering approach, leveraging the structural characteristics of PET detector modules. Simulation results show that the proposed collaborative solution significantly improves the overall system performance.

The authors' abstract, as published at the source. Journal of Management Analytics, 2026 · DOI ↗