Unsupervised inline analysis of cardiac perfusion MRI

Hui Xue, Sven Zuehlsdorff, Peter Kellman, Andrew Arai, Sonia Nielles-Vallespin, Christophe Chefdhotel, Christine H. Lorenz, Jens Guehring

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

Abstract

In this paper we first discuss the technical challenges preventing an automated analysis of cardiac perfusion MR images and subsequently present a fully unsupervised workflow to address the problems. The proposed solution consists of key-frame detection, consecutive motion compensation, surface coil inhomogeneity correction using proton density images and robust generation of pixel-wise perfusion parameter maps. The entire processing chain has been implemented on clinical MR systems to achieve unsupervised inline analysis of perfusion MRI. Validation results are reported for 260 perfusion time series, demonstrating feasibility of the approach.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages741-749
Number of pages9
Volume5762 LNCS
EditionPART 2
DOIs
StatePublished - 2009
Externally publishedYes
Event12th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2009 - London, United Kingdom
Duration: Sep 20 2009Sep 24 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume5762 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other12th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2009
Country/TerritoryUnited Kingdom
CityLondon
Period9/20/099/24/09

ASJC Scopus subject areas

  • General Computer Science
  • Theoretical Computer Science

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