Estimability of spatio-temporal activation in fMRI

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

1 Citation (Scopus)

Abstract

Event-related functional magnetic resonance imaging (fMRI) is considered as an estimation and reconstruction problem. A linear model of the fMRI system based on the Fourier sampler (k-space) approximation is introduced and used to examine what parameters of the activation are estimable, i.e. can be accurately reconstructed in the noisefree limit. Several possible spatio-temporal representations of the activation are decomposed into null and measurement components. A causal representation of the activation using generalized Laguerre polynomials is introduced.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages259-271
Number of pages13
Volume2082
ISBN (Print)3540422455, 9783540422457
StatePublished - 2001
Event17th International Conference on Information Processing in Medical Imaging, IPMI 2001 - Davis, United States
Duration: Jun 18 2001Jun 22 2001

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2082
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other17th International Conference on Information Processing in Medical Imaging, IPMI 2001
CountryUnited States
CityDavis
Period6/18/016/22/01

Fingerprint

Estimability
Functional Magnetic Resonance Imaging
Activation
Chemical activation
K-space
Laguerre Polynomials
Generalized Polynomials
Imaging System
Imaging systems
Null
Linear Model
Polynomials
Approximation
Magnetic Resonance Imaging

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Lehovich, A., Barrett, H. H., Clarkson, E. W., & Gmitro, A. F. (2001). Estimability of spatio-temporal activation in fMRI. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2082, pp. 259-271). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2082). Springer Verlag.

Estimability of spatio-temporal activation in fMRI. / Lehovich, Andre; Barrett, Harrison H; Clarkson, Eric W; Gmitro, Arthur F.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 2082 Springer Verlag, 2001. p. 259-271 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2082).

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

Lehovich, A, Barrett, HH, Clarkson, EW & Gmitro, AF 2001, Estimability of spatio-temporal activation in fMRI. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 2082, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2082, Springer Verlag, pp. 259-271, 17th International Conference on Information Processing in Medical Imaging, IPMI 2001, Davis, United States, 6/18/01.
Lehovich A, Barrett HH, Clarkson EW, Gmitro AF. Estimability of spatio-temporal activation in fMRI. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 2082. Springer Verlag. 2001. p. 259-271. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
Lehovich, Andre ; Barrett, Harrison H ; Clarkson, Eric W ; Gmitro, Arthur F. / Estimability of spatio-temporal activation in fMRI. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 2082 Springer Verlag, 2001. pp. 259-271 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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