Literature DB >> 31980316

Evaluation of an intensity-based algorithm for 2D/3D registration of natural knee videofluoroscopy data.

Barbara Postolka1, Renate List2, Benedikt Thelen3, Pascal Schütz4, William R Taylor5, Guoyan Zheng6.   

Abstract

The accurate quantification of in-vivo tibio-femoral kinematics is essential for understanding joint functionality, but determination of the 3D pose of bones from 2D single-plane fluoroscopic images remains challenging. We aimed to evaluate the accuracy, reliability and repeatability of an intensity-based 2D/3D registration algorithm. The accuracy was evaluated using fluoroscopic images of 2 radiopaque bones in 18 different poses, compared against a gold-standard fiducial calibration device. In addition, 3 natural femora and 3 natural tibiae were used to examine registration reliability and repeatability. Both manual fitting and intensity-based registration exhibited a mean absolute error of <1 mm in-plane. Overall, intensity-based registration of the femoral bone model revealed significantly higher translational and rotational errors than manual fitting, while no statistical differences (except for y-axis translation) were found for the tibial bone model. The repeatability of 108 intensity-based registrations showed mean in-plane standard deviations of 0.23-0.56 mm, but out-of-plane position repeatability was lower (mean SD: femur 7.98 mm, tibia 6.96 mm). SDs for rotations averaged 0.77-2.52°. While the algorithm registered some images extremely well, other images clearly required manual intervention. When the algorithm registered the bones repeatably, it was also accurate, suggesting an approach that includes manual intervention could become practical for efficient and accurate registration.
Copyright © 2020. Published by Elsevier Ltd.

Entities:  

Keywords:  2D/3D registration; Knee; Videofluoroscopy; in-vivo kinematics

Mesh:

Year:  2020        PMID: 31980316     DOI: 10.1016/j.medengphy.2020.01.002

Source DB:  PubMed          Journal:  Med Eng Phys        ISSN: 1350-4533            Impact factor:   2.242


  2 in total

1.  Personalised pose estimation from single-plane moving fluoroscope images using deep convolutional neural networks.

Authors:  Florian Vogl; Pascal Schütz; Barbara Postolka; Renate List; William Taylor
Journal:  PLoS One       Date:  2022-06-24       Impact factor: 3.752

2.  Influence of Bone Morphology on In Vivo Tibio-Femoral Kinematics in Healthy Knees during Gait Activities.

Authors:  Sandro Hodel; Barbara Postolka; Andreas Flury; Pascal Schütz; William R Taylor; Lazaros Vlachopoulos; Sandro F Fucentese
Journal:  J Clin Med       Date:  2022-08-30       Impact factor: 4.964

  2 in total

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