Reconstruction of highly under-sampled dynamic MRI using sparse representation of 1D temporal snippets

Esben Plenge, Mitchell A. Cooper, Martin R. Prince, Yi Wang, Pascal Spincemaille, Michael Elad

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

Abstract

This paper introduces a new empirical model for dynamic MRI and shows its application to reconstruction of highly under-sampled dynamic MRI. The model proposes that short 1D signals, so-called snippets, along the image's temporal dimension are sparse under non-linear transformation using a compact dictionary trained on the data itself. We employ this model to the problem of reconstructing dynamic abdominal MRI and validate its efficacy on a dynamic computational phantom and on an in vivo dynamic MRI sequence. We show how the approach extends and outperforms a state-of-the-art reconstruction algorithm.

Original languageEnglish
Title of host publication2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015
Pages1240-1243
Number of pages4
ISBN (Electronic)9781479923748
DOIs
StatePublished - 21 Jul 2015
Event12th IEEE International Symposium on Biomedical Imaging, ISBI 2015 - Brooklyn, United States
Duration: 16 Apr 201519 Apr 2015

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2015-July

Conference

Conference12th IEEE International Symposium on Biomedical Imaging, ISBI 2015
Country/TerritoryUnited States
CityBrooklyn
Period16/04/1519/04/15

Keywords

  • Abdominal MRI
  • Compressed Sensing
  • Dictionary Learning
  • Dynamic MRI
  • Sparse Coding

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

Fingerprint

Dive into the research topics of 'Reconstruction of highly under-sampled dynamic MRI using sparse representation of 1D temporal snippets'. Together they form a unique fingerprint.

Cite this