Statistical Shape Model Based Femur Kinematics from Biplane Fluoroscopy

TitleStatistical Shape Model Based Femur Kinematics from Biplane Fluoroscopy
Publication TypeJournal Article
Year of Publication2012
AuthorsBaka, N, de Bruijne, M, van Walsum, T, Kaptein, BL, Giphart, JE, Schaap, M, Niessen, WJ, Lelieveldt, BPF
JournalIEEE transactions on medical imaging
Start Page1573

Studying joint kinematics is of interest to improve prosthesis design and to characterize post-operative motion. State of the art techniques register bones segmented from prior CT or MR 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 1.480.41 mm. The resulting tracking precision was 1-1.5 millimeter, with the largest errors occurring in the rotation around the femoral shaft (about 2.5.precision).

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Alternate JournalIEEE Trans Med Imaging
PubMed ID22547454