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Abstract #2966

Fully-automated motion correction and probability-based segmentation of myocardial perfusion MRI data

Cian Michael Scannell1, Adriana Villa1, Jack Lee1, Marcel Breeuwer2,3, and Amedeo Chiribiri1

1School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom, 2Imaging Systems - MR, Philips Healthcare, Best, Netherlands, 3Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands

This work presents a fully-automated framework for the pre-processing of free-breathing myocardial perfusion MRI data. Image series are first split into low-rank and sparse components using RPCA. This allows estimation of the deformation fields required to motion correct the image series, in the absence of dynamic contrast enhancement. Once motion corrected, pixels are clustered into anatomically relevant clusters using perfusion-superpixels which groups nearby pixels that have similar time dynamics. A LDA classifier is trained which allows the generation of myocardial probability maps and active contours are fit to the high probability regions to give a delineation of the myocardium.

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