An Unsupervised Fluoroscopic Analysis of Knee Joint Kinematics

Scott, Crystal; Smith, Elisa H. Barney

doi:10.1109/cbms.2006.42

Cited by 1 publication

(2 citation statements)

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“…Skin marker-based kinematics on the other hand is prone to soft-tissue motion resulting in errors larger than 10 mm (Garling et al, 2007; Stagni et al, 2005). More accurate kinematics can be obtained with model-based tracking in fluoroscopy (Fregly et al, 2005; Kitagawa et al, 2010; Li et al, 2008; Muhit et al, 2010; Nakajima et al, 2007; Pickering et al, 2009; Scott and Barney Smith, 2006; Tsai et al, 2010; You et al, 2001). These methods align a 3D bone model, segmented from CT or MRI, with calibrated fluoroscopic sequences.…”

Section: Introductionmentioning

confidence: 99%

“…These methods align a 3D bone model, segmented from CT or MRI, with calibrated fluoroscopic sequences. Alignment is achieved by minimizing either an image intensity distance through calculation of digitally reconstructed radiographs (DRR) (Anderst et al, 2009; Bey et al, 2008; Dennis et al, 2005; Mahfouz et al, 2003; Muhit et al, 2010; Nakajima et al, 2007; Pickering et al, 2009; Scott and Barney Smith, 2006; You et al, 2001), or an image edge to bone model silhouette distance (Defrate et al, 2006; Fregly et al, 2005; Gollmer et al, 2007; Hanson et al, 2006; Hirokawa et al, 2008; Kitagawa et al, 2010; Li et al, 2008; Tersi et al, 2012; Torry et al, 2011; Tsai et al, 2010). Kinematic analysis not requiring the subject-specific 3D model would, however, be preferred, as it would lower analysis costs and eliminate the prior 3D acquisition resulting in lower radiation dose in the case of CT.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Baka

Kaptein

Giphart

et al. 2014

Journal of Biomechanics

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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 SSM-s for computing knee kinematics from biplane fluoroscopic sequences was explored. Kinematic precision with an edge based automated bone tracking method using SSM-s was evaluated on 6 cadaver 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 (0.48–0.81 mm) and approximately one degree (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 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 the overall 3D shape accuracy.

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