Sparsity-based pet image reconstruction using MRI learned dictionaries

Jing Tang, Yanhua Wang, Rutao Yao, Leslie Ying

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Incorporating anatomical information obtained by magnetic resonance (MR) imaging has shown its promises to improve the positron emission tomography (PET) imaging quality. In this paper, we propose a novel maximum a posteriori (MAP) PET image reconstruction technique using a sparse prior whose dictionary is learned from the corresponding MR images. Specifically, a PET image is divided into three-dimensional overlapping patches which are expected to be sparsely represented over a redundant dictionary. With the assumption that the PET and MR images of a patient can be sparsified under a common dictionary, the dictionary is learned from the MR image to involve anatomical measurement in PET image reconstruction. The PET image and its sparse representation are updated alternately in the iterative reconstruction process. We evaluated the performance of the proposed method quantitatively, using a realistic simulation with the Brain Web database phantoms. Noticeable improvement on the noise versus bias tradeoff has been demonstrated in images reconstructed from the proposed method, compared to that from the conventional smoothness MAP method.

Original languageEnglish (US)
Title of host publication2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1087-1090
Number of pages4
ISBN (Electronic)9781467319591
DOIs
StatePublished - Jul 29 2014
Event2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014 - Beijing, China
Duration: Apr 29 2014May 2 2014

Publication series

Name2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014

Other

Other2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014
CountryChina
CityBeijing
Period4/29/145/2/14

Keywords

  • Dictionary learning
  • MRI prior
  • PET image reconstruction
  • Sparse representation

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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