Automatic prediction of myocardial contractility improvement in stress MRI using shape morphometrics with independent component analysis

A. Suinesiaputra; A. F. Frangi; H. J. Lamb; J. H.C. Reiber; B. P.F. Lelieveldt

doi:10.1007/11505730_27

Automatic prediction of myocardial contractility improvement in stress MRI using shape morphometrics with independent component analysis

A. Suinesiaputra, A. F. Frangi, H. J. Lamb, J. H.C. Reiber, B. P.F. Lelieveldt

Leiden University Medical Center

Research output: Contribution to journal › Conference article › peer-review

Abstract

An important assessment in patients with ischemic heart disease is whether myocardial contractility may improve after treatment. The prediction of myocardial contractility improvement is generally performed under physical or pharmalogical stress conditions. In this paper, we present a technique to build a statistical model of healthy myocardial contraction using independent component analysis. The model is used to detect regions with abnormal contraction in patients both during rest and stress.

Original language	English
Pages (from-to)	321-332
Number of pages	12
Journal	Lecture notes in computer science
Volume	3565
DOIs	https://doi.org/10.1007/11505730_27
Publication status	Published - 2005
Event	19th International Conference on Information Processing in Medical Imaging, IPMI 2005 - Glenwood Springs, CO, United States Duration: 10 Jul 2005 → 15 Jul 2005

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10.1007/11505730_27

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title = "Automatic prediction of myocardial contractility improvement in stress MRI using shape morphometrics with independent component analysis",

abstract = "An important assessment in patients with ischemic heart disease is whether myocardial contractility may improve after treatment. The prediction of myocardial contractility improvement is generally performed under physical or pharmalogical stress conditions. In this paper, we present a technique to build a statistical model of healthy myocardial contraction using independent component analysis. The model is used to detect regions with abnormal contraction in patients both during rest and stress.",

author = "A. Suinesiaputra and Frangi, {A. F.} and Lamb, {H. J.} and Reiber, {J. H.C.} and Lelieveldt, {B. P.F.}",

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journal = "Lecture notes in computer science",

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AU - Suinesiaputra, A.

AU - Frangi, A. F.

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AU - Reiber, J. H.C.

AU - Lelieveldt, B. P.F.

PY - 2005

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N2 - An important assessment in patients with ischemic heart disease is whether myocardial contractility may improve after treatment. The prediction of myocardial contractility improvement is generally performed under physical or pharmalogical stress conditions. In this paper, we present a technique to build a statistical model of healthy myocardial contraction using independent component analysis. The model is used to detect regions with abnormal contraction in patients both during rest and stress.

AB - An important assessment in patients with ischemic heart disease is whether myocardial contractility may improve after treatment. The prediction of myocardial contractility improvement is generally performed under physical or pharmalogical stress conditions. In this paper, we present a technique to build a statistical model of healthy myocardial contraction using independent component analysis. The model is used to detect regions with abnormal contraction in patients both during rest and stress.

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DO - 10.1007/11505730_27

M3 - Conference article

C2 - 17354706

AN - SCOPUS:34047203284

SN - 0302-9743

VL - 3565

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EP - 332

JO - Lecture notes in computer science

JF - Lecture notes in computer science

T2 - 19th International Conference on Information Processing in Medical Imaging, IPMI 2005

Y2 - 10 July 2005 through 15 July 2005

ER -

Automatic prediction of myocardial contractility improvement in stress MRI using shape morphometrics with independent component analysis

Abstract

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