title: Regression-based cardiac motion prediction from single-phase CTA.
abstract: State of the art cardiac computed tomography (CT) enables the acquisition of imaging data of the heart over the entire cardiac cycle at concurrent high spatial and temporal resolution. However, in clinical practice, acquisition is increasingly limited to 3-D images. Estimating the shape of the cardiac structures throughout the entire cardiac cycle from a 3-D image is therefore useful in applications such as the alignment of preoperative computed tomography angiography (CTA) to intra-operative X-ray images for improved guidance in coronary interventions. We hypothesize that the motion of the heart is partially explained by its shape and therefore investigate the use of three regression methods for motion estimation from single-phase shape information. Quantitative evaluation on 150 4-D CTA images showed a small, but statistically significant, increase in the accuracy of the predicted shape sequences when using any of the regression methods, compared to shape-independent motion prediction by application of the mean motion. The best results were achieved using principal component regression resulting in point-to-point errors of  2.3±0.5  mm, compared to values of  2.7±0.6 mm for shape-independent motion estimation. Finally, we showed that this significant difference withstands small variations in important parameter settings of the landmarking procedure.
authors.affiliation: Departments of Medical Informatics and Radiology, Erasmus MC-University Medical Center Rotterdam, 3000 CA Rotterdam, The Netherlands. c.metz@erasmusmc.nl
authors.forename: Coert T
authors.initials: CT
authors.lastname: Metz
authors.forename: Nora
authors.initials: N
authors.lastname: Baka
authors.forename: Hortense
authors.initials: H
authors.lastname: Kirisli
authors.forename: Michiel
authors.initials: M
authors.lastname: Schaap
authors.forename: Stefan
authors.initials: S
authors.lastname: Klein
authors.forename: Lisan A
authors.initials: LA
authors.lastname: Neefjes
authors.forename: Nico R
authors.initials: NR
authors.lastname: Mollet
authors.forename: Boudewijn
authors.initials: B
authors.lastname: Lelieveldt
authors.forename: Marleen
authors.initials: M
authors.lastname: de Bruijne
authors.forename: Wiro J
authors.initials: WJ
authors.lastname: Niessen
authors.forename: Theo
authors.initials: T
authors.lastname: van Walsum
mesh_terms: D000465:Algorithms; D055424:Cardiac-Gated Imaging Techniques / Q000379:methods*; D017023:Coronary Angiography / Q000379:methods*; D003324:Coronary Artery Disease / Q000000981:diagnostic imaging*; D006801:Humans; D009038:Motion; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D012044:Regression Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*