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Operating Room Quality Assurance (ORQA) for Spine Surgery Using Known-Component 3D-2D Image Registration

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A Uneri

A Uneri1*, T De Silva1 , J Goerres1 , M Jacobson1 , M Ketcha1 , S Reaungamornrat1 , G Kleinszig2 , S Vogt3 , A Khanna4 , J Wolinsky5 , J Siewerdsen1 , (1) Johns Hopkins University, Baltimore, MD, (2) Siemens Healthcare, Erlangen, Bayern, (3) Siemens Healthcare , Malvern, PA, (4) Johns Hopkins Health Care and Surgery Center, Bethesda, MD, (5) The Johns Hopkins Hospital, Baltimore, MD


WE-AB-BRA-7 (Wednesday, August 3, 2016) 7:30 AM - 9:30 AM Room: Ballroom A

Purpose:Intraoperative x-ray radiography/fluoroscopy is commonly used to qualitatively assess delivery of surgical devices (e.g., spine pedicle screws) but can fail to reliably detect suboptimal placement (e.g., breach of adjacent critical structures). We present a method wherein prior knowledge of the patient and surgical components is leveraged to match preoperative CT and intraoperative radiographs for quantitative assessment of 3D pose. The method presents a new means of operating room quantitative quality assurance (ORQA) that could improve quality and safety, and reduce the frequency of revision surgeries.

Methods:The algorithm (known-component registration, KC-Reg) uses patient-specific preoperative CT and parametrically defined surgical component models within a robust 3D-2D registration method to iteratively optimize gradient similarity using the covariance matrix adaptation evolution strategy. Advances from previous work address key challenges to clinical translation: i) absolving the need for offline geometric calibration of the C-arm; and ii) solving multiple component bodies simultaneously, thereby allowing QA in a single step (e.g., spinal construct with 4–20 screws), rather than sequential QA of each component. Performance was tested in a spine phantom with 10 pedicle screws, and first results from clinical studies are reported.

Results:Phantom experiments demonstrated median target registration error (TRE) of (1.0±0.3) mm at the screw tip and (0.7°±0.4°) in angulation. The simultaneous multi-body registration approach improved TRE from the previous (sequential) method by 42%, reduced outliers, and fits into the natural workflow. Initial application of KC-Reg in clinical data shows TRE of (2.5±4.5) mm and (4.7°±0.5°).

Conclusion:The KC-Reg algorithm offers a potentially valuable method for quantitative QA of the surgical product, using radiographic systems that are already within the surgical arsenal. For spine surgery, the method offers a near-real-time independent check on the quality of surgical product, facilitating immediate revision if necessary and potentially avoiding postoperative morbidity and/or revision surgery.

Funding Support, Disclosures, and Conflict of Interest: Gerhard Kleinszig and Sebastian Vogt are employees of Siemens Healthcare.

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