Semi-Stationary Multisource AI-Powered Real-Time Tomography

Abstract

Over the past decades, the development of computed tomography (CT) technologies has been largely driven by the need for cardiac imaging but the temporal resolution remains insufficient for clinical CT in difficult cases and rather challenging for preclinical CT since small animals have much higher heart rates than humans. To address this challenge, here we report a semi-stationary multisource artificial intelligence (AI)-based real-time tomography (SMART) CT system. This unique scanner is featured by 29 source-detector pairs fixed on a circular track to collect X-ray signals in parallel, enabling instantaneous tomography in principle. Given the multisource architecture, the field of view covers only a cardiac region. To solve the interior problem, an AI-empowered interior tomography approach is developed to synergize sparsity-based regularization and learning-based reconstruction. To demonstrate the performance and utilities of the SMART system, extensive results are obtained in physical phantom experiments and animal studies, including dead and live rats as well as live rabbits. The reconstructed volumetric images convincingly demonstrate the merits of the SMART system using the AI-empowered interior tomography approach, enabling cardiac CT with the unprecedented temporal resolution of 33 ms, which enjoys the highest temporal resolution than the state of the art.