Inferring 3D kinematics of carpal bones from single view fluoroscopic sequences

Evaluation of automated statistical shape model based knee kinematics from biplane fluoroscopy

State-of-the-art fluoroscopic knee kinematic analysis methods require the patient-specific bone shapes segmented from CT or MRI. Substituting the patient-specific bone shapes with personalizable models, such as statistical shape models (SSM), could eliminate the CT/MRI acquisitions, and thereby decrease costs and radiation dose (when eliminating CT). SSM based kinematics, however, have not yet been evaluated on clinically relevant joint motion parameters. Therefore, in this work the applicability of SSMs for computing knee kinematics from biplane fluoroscopic sequences was explored. Kinematic precision with an edge based automated bone tracking method using SSMs was evaluated on 6 cadaveric and 10 in-vivo fluoroscopic sequences. The SSMs of the femur and the tibia-fibula were created using 61 training datasets. Kinematic precision was determined for medial-lateral tibial shift, anterior-posterior tibial drawer, joint distraction-contraction, flexion, tibial rotation and adduction. The relationship between kinematic precision and bone shape accuracy was also investigated. The SSM based kinematics resulted in sub-millimeter (0.48-0.81mm) and approximately 1° (0.69-0.99°) median precision on the cadaveric knees compared to bone-marker-based kinematics. The precision on the in-vivo datasets was comparable to that of the cadaveric sequences when evaluated with a semi-automatic reference method. These results are promising, though further work is necessary to reach the accuracy of CT-based kinematics. We also demonstrated that a better shape reconstruction accuracy does not automatically imply a better kinematic precision. This result suggests that the ability of accurately fitting the edges in the fluoroscopic sequences has a larger role in determining the kinematic precision than that of the overall 3D shape accuracy.

Stochastic 3D motion compensation of coronary arteries from monoplane angiograms

Image-based navigation during percutaneous coronary interventions is highly challenging since it involves estimating the 3D motion of a complex topology using 2D angiographic views. A static coronary tree segmented in a pre-operative CT-scan can be overlaid on top of the angiographic frames to outline the coronary vessels, but this overlay does not account for coronary motion, which has to be mentally compensated by the cardiologist. In this paper, we propose a new approach to the motion estimation problem, where the temporal evolution of the coronary deformation over the cardiac cycle is modeled as a stochastic process. The sequence of angiographic frames is interpreted as a probabilistic evidence of the succession of unknown deformation states, which can be optimized using particle filtering. Iterative and non-rigid registration is performed in a projective manner, and relies on a feature-based similarity measure. Experiments show promising results in terms of registration accuracy, learning capability and computation time.

Statistical shape model-based femur kinematics from biplane fluoroscopy

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).