Enhancing photon-counting computed tomography reconstruction via subspace dictionary learning and spatial sparsity regularization.

ABSTRACT:

Background

Photon-counting computed tomography (CT) is an advanced imaging technique that enables multi-energy imaging from a single scan. However, the limited photon count assigned to narrow energy bins leads to increased quantum noise in the reconstructed spectral images. To address this issue, leveraging the prior information in the spectral images is essential. This study aimed to develop an efficient algorithm that enhances image reconstruction quality by reducing noise levels and preserving image details.

Methods

To improve image reconstruction quality for photon-counting CT, we propose an algorithm based on the subspace-assisted multi-prior information, including global, nonlocal, and local priors, for spectral CT reconstruction. Specifically, the algorithm first maps

SUBMITTER: Xing Q 

PROVIDER: S-EPMC11744124 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature