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Registration For Orthopaedic Interventions

Z. Yaniv
Published 2016 · Computer Science

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Registration is the process of computing the transformation that relates the coordinates of corresponding points viewed in two different coordinate systems. It is one of the key components in orthopaedic navigation guidance and robotic systems. When assessing the appropriateness of a registration method for clinical use one must consider multiple factors. Among others these include, accuracy, robustness, speed, degree of automation, detrimental effects to the patient, effects on interventional workflow, and associated financial costs. In this chapter we give an overview of registration algorithms, both those available commercially and those that have only been evaluated in the laboratory setting. We introduce the models underlying the algorithms, describe the context in which they are used and assess them using the criteria described above. We show that academic research has primarily focused on improving all aspects of registration while ignoring workflow related issues. On the other hand, commercial systems have found ways of obviating the need for registration resulting in streamlined workflows that are clinically more acceptable, albeit at a cost of being sub-optimal on other criteria. While there is no optimal registration method for all settings, we do have a respectable arsenal from which to choose.
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Estimation of target registration error considering small inhomogeneous and anisotropic bias in fiducial localizer error
Zhe Min (2016)
Interventional 2D/3D Registration with Contextual Pose Update
Wenhao Gu (2019)
10.1007/978-3-030-04747-4_16
Joint Registration of Multiple Generalized Point Sets
Zhe Min (2018)
10.1109/3DV.2018.00029
Joint Alignment of Multiple Generalized Point Sets with Anisotropic Positional Uncertainty Based on Expectation Maximization
Zhe Min (2018)
10.1049/htl.2017.0065
Estimation of surgical tool-tip tracking error distribution in coordinate reference frame involving pivot calibration uncertainty
Zhe Min (2017)
10.1007/978-3-030-59861-7_29
Extended Capture Range of Rigid 2D/3D Registration by Estimating Riemannian Pose Gradients
W. Gu (2020)
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