Optimal sampling geometries for TV-norm reconstruction of fMRI data

Oliver M. Jeromin, Vince D. Calhoun, Marios S. Pattichis

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

Abstract

This study explores the ability to reconstruct functional magnetic resonance imaging (fMRI) brain slices from a limited number of K-space samples. We use compressed sensing methods to reconstruct brain imaging activity using different K-space sampling geometries. To determine the optimal sampling geometry, we compute the reconstruction error. Here, for each geometry, we also estimate the optimal weighting parameters for the total variation (TV) norm and L-2 norm penalty functions. Initial results show that the optimal sampling geometry varies significantly as a function of the required reduction in K-space sampling density (for 60% to 90% reduction). Furthermore, the reconstructed fMRI slices can be used to accurately detect regions of neural activity from a largely reduced number of K-space samples.

Original languageEnglish (US)
Title of host publication2008 42nd Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2008
Pages1397-1401
Number of pages5
DOIs
StatePublished - Dec 1 2008
Externally publishedYes
Event2008 42nd Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2008 - Pacific Grove, CA, United States
Duration: Oct 26 2008Oct 29 2008

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)1058-6393

Other

Other2008 42nd Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2008
Country/TerritoryUnited States
CityPacific Grove, CA
Period10/26/0810/29/08

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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