Total Variation Regularisation in Measurement and Image space for PET reconstruction

TL;DR

This paper introduces a novel PET image reconstruction method using total variation regularization on both the image and sinogram, demonstrating improved reconstruction of thin structures through theoretical analysis and numerical experiments.

Contribution

The paper proposes a new joint regularization model for PET reconstruction with theoretical guarantees and demonstrates its advantages over standard methods in numerical tests.

Findings

Improved reconstruction of thin structures in PET images.

Theoretical proof of existence, uniqueness, and stability of the model.

Numerical results show better performance with combined regularization.

Abstract

The aim of this paper is to test and analyze a novel technique for image reconstruction in positron emission tomography, which is based on (total variation) regularization on both the image space and the projection space. We formulate our variational problem considering both total variation penalty terms on the image and on an idealized sinogram to be reconstructed from a given Poisson distributed noisy sinogram. We prove existence, uniqueness and stability results for the proposed model and provide some analytical insight into the structures favoured by joint regularization. For the numerical solution of the corresponding discretized problem we employ the split Bregman algorithm and extensively test the approach in comparison to standard total variation regularization on the image. The numerical results show that an additional penalty on the sinogram performs better on reconstructing images with thin structures.