Motion-compensation of Cardiac Perfusion MRI using a Statistical Texture Ensemble

Mikkel Bille Stegmann; Henrik B. W. Larsson

Motion-compensation of Cardiac Perfusion MRI using a Statistical Texture Ensemble

Mikkel Bille Stegmann, Henrik B. W. Larsson

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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Abstract

This paper presents a novel method for segmentation of cardiac perfusion MRI. By performing complex analyses of variance and clustering in an annotated training set off-line, the presented method provide real-time segmentation in an on-line setting. This renders the method feasible for e.g. analysis of large image databases or for live motion-compensation in modern MR scanners. Changes in image intensity during the bolus passage is modelled by an Active Appearance Model is augmented with a cluster analysis of the training set and priors on pose and shape. Preliminary validation of the method is carried out using 250 MR perfusion images, acquired without breath-hold from five subjects. Results show high accuracy, given the limited number of subjects.

Original language	English
Title of host publication	Functional Imaging and Modeling of the Heart, FIMH 2003
Publisher	Springer Verlag
Publication date	2003
Pages	151-161
Publication status	Published - 2003
Event	Functional Imaging and Modeling of the Heart, FIMH 2003 - Duration: 1 Jan 2003 → …

Conference

Conference	Functional Imaging and Modeling of the Heart, FIMH 2003
Period	01/01/2003 → …

Access to Document

Imm1255Final published version, 303 KB

http://www.imm.dtu.dk/pubdb/p.php?1255

Cite this

@inproceedings{b5b3b295034c40449599f7bf1ef19e55,

title = "Motion-compensation of Cardiac Perfusion MRI using a Statistical Texture Ensemble",

abstract = "This paper presents a novel method for segmentation of cardiac perfusion MRI. By performing complex analyses of variance and clustering in an annotated training set off-line, the presented method provide real-time segmentation in an on-line setting. This renders the method feasible for e.g. analysis of large image databases or for live motion-compensation in modern MR scanners. Changes in image intensity during the bolus passage is modelled by an Active Appearance Model is augmented with a cluster analysis of the training set and priors on pose and shape. Preliminary validation of the method is carried out using 250 MR perfusion images, acquired without breath-hold from five subjects. Results show high accuracy, given the limited number of subjects.",

keywords = "myocardial perfusion imaging, segmentation, clustering, active appearance models",

author = "Stegmann, {Mikkel Bille} and Larsson, {Henrik B. W.}",

year = "2003",

language = "English",

pages = "151--161",

booktitle = "Functional Imaging and Modeling of the Heart, FIMH 2003",

publisher = "Springer Verlag",

address = "Germany",

note = "Functional Imaging and Modeling of the Heart, FIMH 2003 ; Conference date: 01-01-2003",

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TY - GEN

T1 - Motion-compensation of Cardiac Perfusion MRI using a Statistical Texture Ensemble

AU - Stegmann, Mikkel Bille

AU - Larsson, Henrik B. W.

PY - 2003

Y1 - 2003

N2 - This paper presents a novel method for segmentation of cardiac perfusion MRI. By performing complex analyses of variance and clustering in an annotated training set off-line, the presented method provide real-time segmentation in an on-line setting. This renders the method feasible for e.g. analysis of large image databases or for live motion-compensation in modern MR scanners. Changes in image intensity during the bolus passage is modelled by an Active Appearance Model is augmented with a cluster analysis of the training set and priors on pose and shape. Preliminary validation of the method is carried out using 250 MR perfusion images, acquired without breath-hold from five subjects. Results show high accuracy, given the limited number of subjects.

AB - This paper presents a novel method for segmentation of cardiac perfusion MRI. By performing complex analyses of variance and clustering in an annotated training set off-line, the presented method provide real-time segmentation in an on-line setting. This renders the method feasible for e.g. analysis of large image databases or for live motion-compensation in modern MR scanners. Changes in image intensity during the bolus passage is modelled by an Active Appearance Model is augmented with a cluster analysis of the training set and priors on pose and shape. Preliminary validation of the method is carried out using 250 MR perfusion images, acquired without breath-hold from five subjects. Results show high accuracy, given the limited number of subjects.

KW - myocardial perfusion imaging

KW - segmentation

KW - clustering

KW - active appearance models

M3 - Article in proceedings

SP - 151

EP - 161

BT - Functional Imaging and Modeling of the Heart, FIMH 2003

PB - Springer Verlag

T2 - Functional Imaging and Modeling of the Heart, FIMH 2003

Y2 - 1 January 2003

ER -