Joint reconstruction of PET-MRI by parallel level sets

Matthias J. Ehrhardt; Kris Thielemans; Luis Pizarro; Pawel Markiewicz; David Atkinson; Sebastien Ourselin; Brian F. Hutton; Simon R. Arridge

doi:10.1109/NSSMIC.2014.7430895

Joint reconstruction of PET-MRI by parallel level sets

Matthias J. Ehrhardt, Kris Thielemans, Luis Pizarro, Pawel Markiewicz, David Atkinson, Sebastien Ourselin, Brian F. Hutton, Simon R. Arridge

Institute for Mathematical Innovation (IMI)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (Scopus)

Abstract

Combined positron emission tomography (PET) and magnetic resonance imaging (MRI) scanners acquire simultaneously functional PET and anatomical or functional MRI data. As the data of both modalities are likely to show similar structures we aim to exploit this by joint reconstruction of PET and MRI. In a Bayesian formulation, this can be achieved by adding prior information encoding that the images of the two modalities are not independent. Structural similarity can be modeled by the alignment of the image gradients or equivalently their level sets being parallel. Therefore we can combine the objective functions of both modalities and penalize image pairs which do not have parallel level sets. Our results show that combining the reconstruction from heavily under-sampled MRI and noisy PET data can lead to less under-sampling artifacts in MRI images and better defined PET images.

Original language	English
Title of host publication	2014 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC
Place of Publication	U. S. A.
Publisher	IEEE
ISBN (Electronic)	9781479960972
DOIs	https://doi.org/10.1109/NSSMIC.2014.7430895
Publication status	Published - 10 Mar 2016
Event	IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2014 - Seattle, USA United States Duration: 8 Nov 2014 → 15 Nov 2014

Conference

Conference	IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2014
Country	USA United States
City	Seattle
Period	8/11/14 → 15/11/14

Keywords

compressed sensing
image reconstruction
magnetic resonance imaging
positron emission tomography

ASJC Scopus subject areas

Nuclear and High Energy Physics
Radiology Nuclear Medicine and imaging

Access to Document

10.1109/NSSMIC.2014.7430895

Link to publication in Scopus

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Cite this

Joint reconstruction of PET-MRI by parallel level sets. / Ehrhardt, Matthias J.; Thielemans, Kris; Pizarro, Luis; Markiewicz, Pawel; Atkinson, David; Ourselin, Sebastien; Hutton, Brian F.; Arridge, Simon R.

2014 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC . U. S. A. : IEEE, 2016. 7430895.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Ehrhardt, MJ, Thielemans, K, Pizarro, L, Markiewicz, P, Atkinson, D, Ourselin, S, Hutton, BF & Arridge, SR 2016, Joint reconstruction of PET-MRI by parallel level sets. in 2014 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC ., 7430895, IEEE, U. S. A., IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2014, Seattle, USA United States, 8/11/14. https://doi.org/10.1109/NSSMIC.2014.7430895

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abstract = "Combined positron emission tomography (PET) and magnetic resonance imaging (MRI) scanners acquire simultaneously functional PET and anatomical or functional MRI data. As the data of both modalities are likely to show similar structures we aim to exploit this by joint reconstruction of PET and MRI. In a Bayesian formulation, this can be achieved by adding prior information encoding that the images of the two modalities are not independent. Structural similarity can be modeled by the alignment of the image gradients or equivalently their level sets being parallel. Therefore we can combine the objective functions of both modalities and penalize image pairs which do not have parallel level sets. Our results show that combining the reconstruction from heavily under-sampled MRI and noisy PET data can lead to less under-sampling artifacts in MRI images and better defined PET images.",

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AU - Pizarro, Luis

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AU - Ourselin, Sebastien

AU - Hutton, Brian F.

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AB - Combined positron emission tomography (PET) and magnetic resonance imaging (MRI) scanners acquire simultaneously functional PET and anatomical or functional MRI data. As the data of both modalities are likely to show similar structures we aim to exploit this by joint reconstruction of PET and MRI. In a Bayesian formulation, this can be achieved by adding prior information encoding that the images of the two modalities are not independent. Structural similarity can be modeled by the alignment of the image gradients or equivalently their level sets being parallel. Therefore we can combine the objective functions of both modalities and penalize image pairs which do not have parallel level sets. Our results show that combining the reconstruction from heavily under-sampled MRI and noisy PET data can lead to less under-sampling artifacts in MRI images and better defined PET images.

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