Characterization of tissue-specific pre-log Bayesian CT reconstruction by texture-dose relationship.

ABSTRACT:

Purpose

Tissue textures have been recognized as biomarkers for various clinical tasks. In computed tomography (CT) image reconstruction, it is important but challenging to preserve the texture when lowering x-ray exposure from full- toward low-/ultra-low dose level. Therefore, this paper aims to explore the texture-dose relationship within one tissue-specific pre-log Bayesian CT reconstruction algorithm.

Methods

To enhance the texture in ultra-low dose CT (ULdCT) reconstruction, this paper presents a Bayesian type algorithm. A shifted Poisson model is adapted to describe the statistical properties of pre-log data, and a tissue-specific Markov random field prior (MRFt) is used to incorporate tissue texture from previous full-dose CT, thus called SP-MRFt algorithm. Utilizing t

SUBMITTER: Gao Y 

PROVIDER: S-EPMC7721985 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature