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

Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

5 Citations (SciVal)

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 languageEnglish
Title of host publication2014 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC
Place of PublicationU. S. A.
PublisherIEEE
ISBN (Electronic)9781479960972
DOIs
Publication statusPublished - 10 Mar 2016
EventIEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2014 - Seattle, USA United States
Duration: 8 Nov 201415 Nov 2014

Conference

ConferenceIEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2014
Country/TerritoryUSA United States
CitySeattle
Period8/11/1415/11/14

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

Fingerprint

Dive into the research topics of 'Joint reconstruction of PET-MRI by parallel level sets'. Together they form a unique fingerprint.

Cite this