Tensor Dictionary Learning with an Enhanced Sparsity Constraint for Sparse-View Spectral CT Reconstruction

Xuru Li,Yanbo Zhang,Xueqin Sun,Jinxiao Pan,Ping Chen

doi:10.3390/photonics9010035

Xuru Li, Yanbo Zhang + Show 3 more

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https://doi.org/10.3390/photonics9010035

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Abstract

Spectral computed tomography (CT) can divide collected photons into multi-energy channels and gain multi-channel projections synchronously by using photon-counting detectors. However, reconstructed images usually contain severe noise due to the limited number of photons in the corresponding energy channel. Tensor dictionary learning (TDL)-based methods have achieved better performance, but usually lose image edge information and details, especially from an under-sampling dataset. To address this problem, this paper proposes a method termed TDL with an enhanced sparsity constraint for spectral CT reconstruction. The proposed algorithm inherits the superiority of TDL by exploring the correlation of spectral CT images. Moreover, the method designs a regularization using the L0-norm of the image gradient to constrain images and the difference between images and a prior image in each energy channel simultaneously, further improving the ability to preserve edge information and subtle image details. The split-Bregman algorithm has been applied to address the proposed objective minimization model. Several numerical simulations and realistic preclinical mice are studied to assess the effectiveness of the proposed algorithm. The results demonstrate that the proposed method improves the quality of spectral CT images in terms of noise elimination, edge preservation, and image detail recovery compared to the several existing better methods.

Highlights

Computed tomography (CT) can provide intramural organ tissue and structure information via a nondestructive imaging technology, which has been extensively employed in medical diagnosis, industrial detection, and archaeology [1,2]
The conventional FBP, TV minimization, TV combining with low rank (TVLR), and Tensor dictionary learning (TDL) are implemented and compared
In this study, to solve the limitation of the existing TDL-based spectral CT reconstruction method and further improve the reconstructed image quality, we propose a method termed TDL with an enhanced sparsity constraint for spectral CT reconstruction

Summary

Introduction

Computed tomography (CT) can provide intramural organ tissue and structure information via a nondestructive imaging technology, which has been extensively employed in medical diagnosis, industrial detection, and archaeology [1,2]. Traditional CT still cannot satisfy many practical requirements due to its limitations It uses the energy integration detectors and performs poorly at identifying the energy of X-ray photon, leading to energy-dependent information missing. The reconstructed images often suffer from strong beam hardening artifacts [3,4]. It increases radiation risk because multiple scans are needed to obtain multiple energy projections [5,6]. The photon counting detector-based spectral CT has gained considerable attention; it divides the received photons into multiple energy channels and generates multi-channel projections at the same time, providing more spectral information than the DECT [10]. How to improve the image quality of PCD-based spectral CT has become a hot research topic

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