Active CT Reconstruction with a Learned Sampling Policy

Abstract

Computed tomography (CT) is a widely-used imaging technology that assists clinical decision-making with high-quality human body representations. To reduce the radiation dose posed by CT, sparse-view (SV) CT is developed with preserved image quality. However, these methods are still stuck with a fixed uniform SV (USV) sampling strategy, which inhibits the possibility of acquiring a better image with an even reduced dose. In this paper, we explore this possibility via learning an active SV (ASV) sampling policy that optimizes the sampling positions for regions of interest (RoI)-specific, high-quality reconstruction. To this end, we design an sampling agent for the recommendation of ASV sampling positions based on on-the-fly reconstruction with obtained sinograms in a progressive fashion. With such a design, we achieve better performances on the NIH-AAPM dataset over popular USV sampling, especially when the number of views is small. Finally, such a design enables the RoI-aware reconstruction with improved local quality within the RoI that are clinically important. Experiments on the VerSe dataset demonstrate the ability of the proposed sampling policy, which is difficult to achieve with USV sampling.