Conditional shape models for cardiac motion estimation

Authors:
Coert Metz;Nora Baka;Hortense Kirisli;Michiel Schaap;Theox van Walsum;Stefan Klein;Lisan Neefjes;Nico Mollet;Boudewijn Lelieveldt;Marleen de Bruijne;Wiro Niessen
Affiliations:
Dept. of Rad. and Med. Informatics, Erasmus MC, Rotterdam, The Netherlands;Dept. of Rad. and Med. Informatics, Erasmus MC, Rotterdam, The Netherlands and Leiden University Medical Center, Leiden, Netherlands;Dept. of Rad. and Med. Informatics, Erasmus MC, Rotterdam, The Netherlands and Leiden University Medical Center, Leiden, Netherlands;Dept. of Rad. and Med. Informatics, Erasmus MC, Rotterdam, The Netherlands;Dept. of Rad. and Med. Informatics, Erasmus MC, Rotterdam, The Netherlands;Dept. of Rad. and Med. Informatics, Erasmus MC, Rotterdam, The Netherlands;Dept. of Radiology and Cardiology Erasmus MC, Rotterdam, The Netherlands;Dept. of Radiology and Cardiology Erasmus MC, Rotterdam, The Netherlands;Leiden University Medical Center, Leiden, Netherlands and Delft University of Technology, The Netherlands;Dept. of Rad. and Med. Informatics, Erasmus MC, Rotterdam, The Netherlands and University of Copenhagen, Denmark;Dept. of Rad. and Med. Informatics, Erasmus MC, Rotterdam, The Netherlands and Delft University of Technology, The Netherlands
Venue:
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I
Year:
2010

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Abstract

We propose a conditional statistical shape model to predict patient specific cardiac motion from the 3D end-diastolic CTA scan. The model is built from 4D CTA sequences by combining atlas based segmentation and 4D registration. Cardiac motion estimation is, for example, relevant in the dynamic alignment of pre-operative CTA data with intra-operative X-ray imaging. Due to a trend towards prospective electrocardiogram gating techniques, 4D imaging data, from which motion information could be extracted, is not commonly available. The prediction of motion from shape information is thus relevant for this purpose. Evaluation of the accuracy of the predicted motion was performed using CTA scans of 50 patients, showing an average accuracy of 1.1 mm.