Statistical shape model-based femur kinematics from biplane fluoroscopy
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Statistical shape model-based femur kinematics from biplane fluoroscopy. / Baka, N.; de Bruijne, Marleen; Walsum, T. van; Kaptein, B. L.; Giphart, J.E.; Schaap, M.; Niessen, W. J.; Lelieveldt, B. P. F.
In: I E E E Transactions on Medical Imaging, Vol. 31, No. 8, 2012, p. 1573-1583.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Statistical shape model-based femur kinematics from biplane fluoroscopy
AU - Baka, N.
AU - de Bruijne, Marleen
AU - Walsum, T. van
AU - Kaptein, B. L.
AU - Giphart, J.E.
AU - Schaap, M.
AU - Niessen, W. J.
AU - Lelieveldt, B. P. F.
PY - 2012
Y1 - 2012
N2 - Studying joint kinematics is of interest to improve prosthesis design and to characterize postoperative motion. State of the art techniques register bones segmented from prior computed tomography or magnetic resonance scans with X-ray fluoroscopic sequences. Elimination of the prior 3D acquisition could potentially lower costs and radiation dose. Therefore, we propose to substitute the segmented bone surface with a statistical shape model based estimate. A dedicated dynamic reconstruction and tracking algorithm was developed estimating the shape based on all frames, and pose per frame. The algorithm minimizes the difference between the projected bone contour and image edges. To increase robustness, we employ a dynamic prior, image features, and prior knowledge about bone edge appearances. This enables tracking and reconstruction from a single initial pose per sequence. We evaluated our method on the distal femur using eight biplane fluoroscopic drop-landing sequences. The proposed dynamic prior and features increased the convergence rate of the reconstruction from 71% to 91%, using a convergence limit of 3 mm. The achieved root mean square point-to-surface accuracy at the converged frames was 1.48 ± 0.41 mm.The resulting tracking precision was 1-1.5 mm, with the largest errors occurring in the rotation around the femoral shaft (about 2.5° precision).
AB - Studying joint kinematics is of interest to improve prosthesis design and to characterize postoperative motion. State of the art techniques register bones segmented from prior computed tomography or magnetic resonance scans with X-ray fluoroscopic sequences. Elimination of the prior 3D acquisition could potentially lower costs and radiation dose. Therefore, we propose to substitute the segmented bone surface with a statistical shape model based estimate. A dedicated dynamic reconstruction and tracking algorithm was developed estimating the shape based on all frames, and pose per frame. The algorithm minimizes the difference between the projected bone contour and image edges. To increase robustness, we employ a dynamic prior, image features, and prior knowledge about bone edge appearances. This enables tracking and reconstruction from a single initial pose per sequence. We evaluated our method on the distal femur using eight biplane fluoroscopic drop-landing sequences. The proposed dynamic prior and features increased the convergence rate of the reconstruction from 71% to 91%, using a convergence limit of 3 mm. The achieved root mean square point-to-surface accuracy at the converged frames was 1.48 ± 0.41 mm.The resulting tracking precision was 1-1.5 mm, with the largest errors occurring in the rotation around the femoral shaft (about 2.5° precision).
U2 - 10.1109/TMI.2012.2195783
DO - 10.1109/TMI.2012.2195783
M3 - Journal article
VL - 31
SP - 1573
EP - 1583
JO - I E E E Transactions on Medical Imaging
JF - I E E E Transactions on Medical Imaging
SN - 0278-0062
IS - 8
ER -
ID: 38289857