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      Intraoperative evaluation of device placement in spine surgery using known-component 3D-2D image registration

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          Abstract

          Purpose

          Intraoperative x-ray radiography/fluoroscopy is commonly used to assess the placement of surgical devices in the operating room (e.g., spine pedicle screws), but qualitative interpretation can fail to reliably detect suboptimal delivery and/or breach of adjacent critical structures. We present a 3D-2D image registration method wherein intraoperative radiographs are leveraged in combination with prior knowledge of the patient and surgical components for quantitative assessment of device placement and more rigorous quality assurance (QA) of the surgical product.

          Methods

          The algorithm is based on known-component registration (KC-Reg) in which patient-specific preoperative CT and parametric component models are used. The registration performs optimization of gradient similarity, removes the need for offline geometric calibration of the C-arm, and simultaneously solves for multiple component bodies, thereby allowing QA in a single step (e.g., spinal construct with 4–20 screws). Performance was tested in a spine phantom, and first clinical results are reported for QA of transpedicle screws delivered in a patient undergoing thoracolumbar spine surgery.

          Results

          Simultaneous registration of 10 pedicle screws (5 contralateral pairs) demonstrated mean target registration error (TRE) of 1.1 ± 0.1 mm at the screw tip and 0.7 ± 0.4° in angulation when a prior geometric calibration was used. The calibration-free formulation, with the aid of component collision constraints, achieved TRE of 1.4 ± 0.6 mm. In all cases, a statistically significant improvement (p < 0.05) was observed for the simultaneous solutions in comparison to previously reported sequential solution of individual components. Initial application in clinical data in spine surgery demonstrated TRE of 2.7 ± 2.6 mm and 1.5 ± 0.8°.

          Conclusions

          The KC-Reg algorithm offers an independent check and quantitative QA of the surgical product using radiographic / fluoroscopic views acquired within standard OR workflow. Such intraoperative assessment could improve quality and safety, provide the opportunity to revise suboptimal constructs in the OR, and reduce the frequency of revision surgery.

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          Author and article information

          Journal
          0401220
          6459
          Phys Med Biol
          Phys Med Biol
          Physics in medicine and biology
          0031-9155
          1361-6560
          28 March 2018
          24 February 2017
          21 April 2017
          21 April 2018
          : 62
          : 8
          : 3330-3351
          Affiliations
          [1 ]Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA
          [2 ]Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA
          [3 ]Siemens Healthcare XP Division, Erlangen, Germany
          [4 ]Department of Orthopaedic Surgery, Johns Hopkins Medical Institute, Baltimore, MD 21287, USA
          [5 ]Department of Neurological Surgery, Johns Hopkins Medical Institute, Baltimore, MD 21287, USA
          Article
          PMC5901722 PMC5901722 5901722 nihpa924738
          10.1088/1361-6560/aa62c5
          5901722
          28233760
          6a8b92be-81a4-47a0-aba1-f450dd36731e
          History
          Categories
          Article

          quality assurance,spine surgery,3D-2D image registration,image-guided surgery,x-ray fluoroscopy

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