Generalizability of diffusion-based CT reconstruction across scanners and protocols

Investigate the generalizability of diffusion-based computed tomography reconstruction methods across different scanners, acquisition geometries, and acquisition protocols to ascertain how well such models transfer to diverse real-world CT settings.

Background

The paper introduces DM4CT, a comprehensive benchmark evaluating diffusion models for CT reconstruction across medical, industrial, and synchrotron datasets. Despite strong performance in controlled scenarios, the authors highlight practical challenges such as distribution shifts and varying acquisition conditions that may hinder deployment.

Recognizing that CT systems vary widely in scanner hardware, geometry (e.g., parallel-beam, cone-beam, helical), and acquisition protocols, the authors note that it remains uncertain how well diffusion-based reconstruction methods trained under one setting perform under mismatched conditions. They suggest extending DM4CT with multi-institutional or cross-protocol datasets to rigorously evaluate transferability.