Total variation regularization in measurement and image space for PET reconstruction

Burger M, Mueller J, Papoutsellis E, Schoenlieb CB (2014)

Publication Language: English

Publication Type: Journal article

Publication year: 2014

Journal

Inverse Problems Institute of Physics: Hybrid Open Access

Book Volume: 30

Issue: 10

DOI: 10.1088/0266-5611/30/10/105003

Abstract

© 2014 IOP Publishing Ltd. The aim of this paper is to test and analyse 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.

Authors with CRIS profile

Martin Burger

Involved external institutions

University of Cambridge GB United Kingdom (GB) Westfälische Wilhelms-Universität (WWU) Münster DE Germany (DE) Universitätsklinikum Münster DE Germany (DE)

How to cite

APA:

Burger, M., Mueller, J., Papoutsellis, E., & Schoenlieb, C.B. (2014). Total variation regularization in measurement and image space for PET reconstruction. Inverse Problems, 30. https://doi.org/10.1088/0266-5611/30/10/105003

MLA:

Burger, Martin, et al. "Total variation regularization in measurement and image space for PET reconstruction." Inverse Problems 30 (2014).

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