NLTG Priors in Medical Image: Nonlocal TV-Gaussian (NLTG) prior for Bayesian inverse problems with applications to Limited CT Reconstruction

TL;DR

This paper introduces a novel hybrid NLTG prior combining nonlocal total variation and Gaussian distributions to improve Bayesian inverse problem solutions, especially in limited CT reconstruction with missing data.

Contribution

The paper extends the TG prior by proposing a hybrid NLTG prior that models textures and structures, with theoretical properties and applications to limited-angle tomography.

Findings

The NLTG prior effectively captures image textures and structures.

Numerical experiments demonstrate improved reconstruction quality.

The method is feasible for severe data missing scenarios.

Abstract

Bayesian inference methods have been widely applied in inverse problems, {largely due to their ability to characterize the uncertainty associated with the estimation results.} {In the Bayesian framework} the prior distribution of the unknown plays an essential role in the Bayesian inference, {and a good prior distribution can significantly improve the inference results.} In this paper, we extend the total~variation-Gaussian (TG) prior in \cite{Z.Yao2016}, and propose a hybrid prior distribution which combines the nonlocal total variation regularization and the Gaussian (NLTG) distribution. The advantage of the new prior is two-fold. The proposed prior models both texture and geometric structures present in images through the NLTV. The Gaussian reference measure also provides a flexibility of incorporating structure information from a reference image. Some theoretical properties are established for the NLTG prior. The proposed prior is applied to limited-angle tomography reconstruction problem with difficulties of severe data missing. We compute both MAP and CM estimates through two efficient methods and the numerical experiments validate the advantages and feasibility of the proposed NLTG prior.