Conditional shape models for cardiac motion estimation
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.
|Persistent URL||dx.doi.org/10.1007/978-3-642-15705-9_55, hdl.handle.net/1765/27967|
|Journal||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
Metz, C.T, Baka, N, Kirisli, H.A, Schaap, M, van Walsum, T.W, Klein, S.K, … Niessen, W.J. (2010). Conditional shape models for cardiac motion estimation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6361 LNCS(PART 1), 452–459. doi:10.1007/978-3-642-15705-9_55