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 |
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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 |
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Period | 01/01/2003 → … |