Regression-based cardiac motion prediction from single-phase CTA
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Regression-based cardiac motion prediction from single-phase CTA. / Metz, C.T.; Baka, N.; Kirisli, H.; Schaap, M.; Klein, S.; Neefjes, L.A.; Mollet, N.R.; Lelieveldt, B.; de Bruijne, Marleen; Niessen, W.J.; Walsum, T. van.
In: I E E E Transactions on Medical Imaging, Vol. 31, No. 6, 2012, p. 1311-1325 .Research output: Contribution to journal › Journal article › Research › peer-review
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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 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 3D images. Estimating the shape of the cardiac structures throughout the entire cardiac cycle from a 3D image is therefore useful in applications such as the alignment of preoperative 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 4D computed tomography angiography (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.
AB - State of the art cardiac 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 3D images. Estimating the shape of the cardiac structures throughout the entire cardiac cycle from a 3D image is therefore useful in applications such as the alignment of preoperative 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 4D computed tomography angiography (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.
U2 - 10.1109/TMI.2012.2190938
DO - 10.1109/TMI.2012.2190938
M3 - Journal article
C2 - 22438512
VL - 31
SP - 1311
EP - 1325
JO - I E E E Transactions on Medical Imaging
JF - I E E E Transactions on Medical Imaging
SN - 0278-0062
IS - 6
ER -
ID: 38289944