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.
Metz
Coert
C
Dept. of Rad. and Med. Informatics, Erasmus MC, Rotterdam, The Netherlands.
Baka
Nora
N
Kirisli
Hortense
H
Schaap
Michiel
M
van Walsum
Theo
T
Klein
Stefan
S
Neefjes
Lisan
L
Mollet
Nico
N
Lelieveldt
Boudewijn
B
de Bruijne
Marleen
M
Niessen
Wiro
W
D000465:Algorithms; D003198:Computer Simulation; D006321:Heart / Q000000981:diagnostic imaging* / Q000502:physiology*; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008953:Models, Anatomic; D008955:Models, Cardiovascular; D015233:Models, Statistical; D009068:Movement / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*