Denoising of low-dose CT images via low-rank tensor modeling and total variation regularization

Artif Intell Med. 2019 Mar;94:1-17. doi: 10.1016/j.artmed.2018.12.006. Epub 2018 Dec 31.

Abstract

Low-dose Computed Tomography (CT) imaging is a most commonly used medical imaging modality. Though the reduction in dosage reduces the risk due to radiation, it leads to an increase in noise level. Hence, it is a mandatory requirement to include a noise reduction technique as a pre- and/or post-processing step for better disease diagnosis. The nuclear norm minimization has attracted a great deal of research interest in contemporary years. This paper proposes a low-rank approximation based approach for denoising of CT images by effectively utilizing the global spatial correlation and local smoothness properties. The tensor nuclear norm is used to describe the global properties and the tensor total variation is used to characterize the local smoothness as well as to improve global smoothness. The resulting optimization problem is solved by the Alternative Direction Method of Multipliers (ADMM) technique. Experimental results on simulated and real CT data prove that the proposed methods outperform the state-of-art works.

Keywords: Computed tomography image; Denoising; Tensor low rank recovery; Tensor total variation.

MeSH terms

  • Algorithms
  • Humans
  • Signal-To-Noise Ratio*
  • Tomography, X-Ray Computed / methods*