Active CT Reconstruction with a Learned Sampling Policy

TL;DR

This paper introduces a learned, adaptive sampling policy for CT imaging that optimizes sampling positions to improve image quality and reduce radiation dose, especially in limited-view scenarios.

Contribution

It proposes an active learning approach with an intelligent agent to dynamically select sampling positions based on ongoing reconstructions, outperforming traditional uniform sampling methods.

Findings

Improved reconstruction quality with fewer views on NIH-AAPM dataset.

Enhanced region-of-interest reconstruction accuracy.

Effective adaptive sampling policy demonstrated on VerSe dataset.

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 and limited-angle CT are developed with preserved image quality. However, these methods are still stuck with a fixed or uniform 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 sampling policy that optimizes the sampling positions for patient-specific, high-quality reconstruction. To this end, we design an \textit{intelligent agent} for active recommendation of 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 uniform sampling, especially when the number of views is small. Finally, such a design also enables RoI-aware reconstruction with improved reconstruction quality within regions of interest (RoI's) that are clinically important. Experiments on the VerSe dataset demonstrate this ability of our sampling policy, which is difficult to achieve based on uniform sampling.