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.
|Title of host publication||Functional Imaging and Modeling of the Heart, FIMH 2003|
|Publication status||Published - 2003|
|Event||Functional Imaging and Modeling of the Heart, FIMH 2003 - |
Duration: 1 Jan 2003 → …
|Conference||Functional Imaging and Modeling of the Heart, FIMH 2003|
|Period||01/01/2003 → …|