Regression-based cardiac motion prediction from single-phase CTA

TY - JOUR

T1 - Regression-based cardiac motion prediction from single-phase CTA

AU - Metz, C.T.

AU - Baka, N.

AU - Kirisli, H.

AU - Schaap, M.

AU - Klein, S.

AU - Neefjes, L.A.

AU - Mollet, N.R.

AU - Lelieveldt, B.

AU - de Bruijne, Marleen

AU - Niessen, W.J.

AU - Walsum, T. van

PY - 2012

Y1 - 2012

N2 - 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\pm 0.5 mm, compared to values of 2.7\pm 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.

AB - 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\pm 0.5 mm, compared to values of 2.7\pm 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.

U2 - 10.1109/TMI.2012.2190938

DO - 10.1109/TMI.2012.2190938

M3 - Journal article

C2 - 22438512

SN - 0278-0062

VL - 31

SP - 1311

EP - 1325

JO - I E E E Transactions on Medical Imaging

JF - I E E E Transactions on Medical Imaging

IS - 6

ER -