Joint PET+MRI patch-based dictionary for Bayesian random field PET reconstruction

Viswanath P. Sudarshan, Zhaolin Chen, Suyash P. Awate

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

2 Citations (Scopus)

Abstract

Multimodal imaging combining positron emission tomography (PET) and magnetic resonance imaging (MRI) provides complementary information about metabolism and anatomy. While the appearances of MRI and PET images are distinctive, there are fundamental inter-image dependencies relating structure and function. In PET-MRI imaging, typical PET reconstruction methods use priors to enforce PET-MRI dependencies at the very fine scale of image gradients and, so, cannot capture larger-scale inter-image correlations and intra-image texture patterns. Some recent methods enforce statistical models of MRI-image patches on PET-image patches, risking infusing anatomical features into PET images. In contrast, we propose a novel patch-based joint dictionary model for PET and MRI, learning regularity in individual patches and correlations in spatially-corresponding patches, for Bayesian PET reconstruction using expectation maximization. Reconstructions on simulated and in vivo PET-MRI data show that our method gives better-regularized images with smaller errors, compared to the state of the art.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2018
Subtitle of host publication21st International Conference, Granada, Spain, Spetember 16-20, 2018, Proceedings, Part I
EditorsAlejandro F. Frangi, Julia A. Schnabel, Christos Davatzikos, Carlos Alberola-López, Gabor Fichtinger
Place of PublicationCham, Switzerland
PublisherSpringer
Pages338-346
Number of pages9
Volume1
ISBN (Electronic)9783030009281
ISBN (Print)9783030009274
DOIs
Publication statusPublished - 1 Jan 2018
EventMedical Image Computing and Computer-Assisted Intervention 2018 - Granada, Spain
Duration: 16 Sep 201820 Sep 2018
Conference number: 21st
https://www.miccai2018.org/en/

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11070 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceMedical Image Computing and Computer-Assisted Intervention 2018
Abbreviated titleMICCAI 2018
CountrySpain
CityGranada
Period16/09/1820/09/18
Internet address

Keywords

  • Bayesian Markov random field
  • EM
  • Joint dictionary model
  • Joint generative model
  • Patches
  • PET-MRI
  • Reconstruction
  • Sparsity

Cite this

Sudarshan, V. P., Chen, Z., & Awate, S. P. (2018). Joint PET+MRI patch-based dictionary for Bayesian random field PET reconstruction. In A. F. Frangi, J. A. Schnabel, C. Davatzikos, C. Alberola-López, & G. Fichtinger (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2018: 21st International Conference, Granada, Spain, Spetember 16-20, 2018, Proceedings, Part I (Vol. 1, pp. 338-346). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11070 LNCS). Springer. https://doi.org/10.1007/978-3-030-00928-1_39