An unsupervised spatio-temporal regularization for perfusion MRI deconvolution in acute stroke

Mathilde Giacalone, Carole Frindel, David Rousseau

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

1 Citation (Scopus)

Abstract

We consider the ill-posed inverse problem encountered in perfusion magnetic resonance imaging (MRI) analysis due to the necessity of eliminating, via a deconvolution process, the imprint of the arterial input function on the MR signals. Until recently, this deconvolution process was realized independently voxel by voxel with a sole temporal regularization despite the knowledge that the ischemic lesion in acute stroke can reasonably be considered piecewise continuous. A new promising algorithm incorporating a spatial regularization to avoid spurious spatial artifacts and preserve the shape of the lesion was introduced [1]. So far, the optimization of the spatio-temporal regularization parameters of the deconvolution algorithm was supervised. In this communication, we evaluate the potential of the L-hypersurface method in selecting the spatio-temporal regularization parameters in an unsupervised way and discuss the possibility of automating this method. This is demonstrated quantitatively with an in silico approach using digital phantoms simulated with realistic lesion shapes.

Original languageEnglish
Title of host publication2016 24th European Signal Processing Conference, EUSIPCO 2016
PublisherEuropean Signal Processing Conference, EUSIPCO
Pages1708-1712
Number of pages5
ISBN (Electronic)9780992862657
DOIs
Publication statusPublished - 2016 Nov 28
Externally publishedYes
Event24th European Signal Processing Conference, EUSIPCO 2016 - Budapest, Hungary
Duration: 2016 Aug 282016 Sep 2

Publication series

NameEuropean Signal Processing Conference
Volume2016-November
ISSN (Print)2219-5491

Other

Other24th European Signal Processing Conference, EUSIPCO 2016
CountryHungary
CityBudapest
Period16/8/2816/9/2

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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