Automatic localization of target vertebrae in spine surgery: clinical evaluation of the LevelCheck registration algorithm
A 3-dimensional-2-dimensional (3D-2D) image registration algorithm, "LevelCheck," was used to automatically label vertebrae in intraoperative mobile radiographs obtained during spine surgery. Accuracy, computation time, and potential failure modes were evaluated in a retrospective study of...
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Published in: | Spine (Philadelphia, Pa. 1976) Vol. 40; no. 8; pp. E476 - E483 |
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Abstract | A 3-dimensional-2-dimensional (3D-2D) image registration algorithm, "LevelCheck," was used to automatically label vertebrae in intraoperative mobile radiographs obtained during spine surgery. Accuracy, computation time, and potential failure modes were evaluated in a retrospective study of 20 patients.
To measure the performance of the LevelCheck algorithm using clinical images acquired during spine surgery.
In spine surgery, the potential for wrong level surgery is significant due to the difficulty of localizing target vertebrae based solely on visual impression, palpation, and fluoroscopy. To remedy this difficulty and reduce the risk of wrong-level surgery, our team introduced a program (dubbed LevelCheck) to automatically localize target vertebrae in mobile radiographs using robust 3D-2D image registration to preoperative computed tomographic (CT) scan.
Twenty consecutive patients undergoing thoracolumbar spine surgery, for whom both a preoperative CT scan and an intraoperative mobile radiograph were available, were retrospectively analyzed. A board-certified neuroradiologist determined the "true" vertebra levels in each radiograph. Registration of the preoperative CT scan to the intraoperative radiograph was calculated via LevelCheck, and projection distance errors were analyzed. Five hundred random initializations were performed for each patient, and algorithm settings (viz, the number of robust multistarts, ranging 50-200) were varied to evaluate the trade-off between registration error and computation time. Failure mode analysis was performed by individually analyzing unsuccessful registrations (>5 mm distance error) observed with 50 multistarts.
At 200 robust multistarts (computation time of ∼26 s), the registration accuracy was 100% across all 10,000 trials. As the number of multistarts (and computation time) decreased, the registration remained fairly robust, down to 99.3% registration accuracy at 50 multistarts (computation time ∼7 s).
The LevelCheck algorithm correctly identified target vertebrae in intraoperative mobile radiographs of the thoracolumbar spine, demonstrating acceptable computation time, compatibility with routinely obtained preoperative CT scans, and warranting investigation in prospective studies.
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AbstractList | STUDY DESIGNA 3-dimensional-2-dimensional (3D-2D) image registration algorithm, "LevelCheck," was used to automatically label vertebrae in intraoperative mobile radiographs obtained during spine surgery. Accuracy, computation time, and potential failure modes were evaluated in a retrospective study of 20 patients.OBJECTIVETo measure the performance of the LevelCheck algorithm using clinical images acquired during spine surgery.SUMMARY OF BACKGROUND DATAIn spine surgery, the potential for wrong level surgery is significant due to the difficulty of localizing target vertebrae based solely on visual impression, palpation, and fluoroscopy. To remedy this difficulty and reduce the risk of wrong-level surgery, our team introduced a program (dubbed LevelCheck) to automatically localize target vertebrae in mobile radiographs using robust 3D-2D image registration to preoperative computed tomographic (CT) scan.METHODSTwenty consecutive patients undergoing thoracolumbar spine surgery, for whom both a preoperative CT scan and an intraoperative mobile radiograph were available, were retrospectively analyzed. A board-certified neuroradiologist determined the "true" vertebra levels in each radiograph. Registration of the preoperative CT scan to the intraoperative radiograph was calculated via LevelCheck, and projection distance errors were analyzed. Five hundred random initializations were performed for each patient, and algorithm settings (viz, the number of robust multistarts, ranging 50-200) were varied to evaluate the trade-off between registration error and computation time. Failure mode analysis was performed by individually analyzing unsuccessful registrations (>5 mm distance error) observed with 50 multistarts.RESULTSAt 200 robust multistarts (computation time of ∼26 s), the registration accuracy was 100% across all 10,000 trials. As the number of multistarts (and computation time) decreased, the registration remained fairly robust, down to 99.3% registration accuracy at 50 multistarts (computation time ∼7 s).CONCLUSIONThe LevelCheck algorithm correctly identified target vertebrae in intraoperative mobile radiographs of the thoracolumbar spine, demonstrating acceptable computation time, compatibility with routinely obtained preoperative CT scans, and warranting investigation in prospective studies.LEVEL OF EVIDENCEN/A. A 3-dimensional-2-dimensional (3D-2D) image registration algorithm, "LevelCheck," was used to automatically label vertebrae in intraoperative mobile radiographs obtained during spine surgery. Accuracy, computation time, and potential failure modes were evaluated in a retrospective study of 20 patients. To measure the performance of the LevelCheck algorithm using clinical images acquired during spine surgery. In spine surgery, the potential for wrong level surgery is significant due to the difficulty of localizing target vertebrae based solely on visual impression, palpation, and fluoroscopy. To remedy this difficulty and reduce the risk of wrong-level surgery, our team introduced a program (dubbed LevelCheck) to automatically localize target vertebrae in mobile radiographs using robust 3D-2D image registration to preoperative computed tomographic (CT) scan. Twenty consecutive patients undergoing thoracolumbar spine surgery, for whom both a preoperative CT scan and an intraoperative mobile radiograph were available, were retrospectively analyzed. A board-certified neuroradiologist determined the "true" vertebra levels in each radiograph. Registration of the preoperative CT scan to the intraoperative radiograph was calculated via LevelCheck, and projection distance errors were analyzed. Five hundred random initializations were performed for each patient, and algorithm settings (viz, the number of robust multistarts, ranging 50-200) were varied to evaluate the trade-off between registration error and computation time. Failure mode analysis was performed by individually analyzing unsuccessful registrations (>5 mm distance error) observed with 50 multistarts. At 200 robust multistarts (computation time of ∼26 s), the registration accuracy was 100% across all 10,000 trials. As the number of multistarts (and computation time) decreased, the registration remained fairly robust, down to 99.3% registration accuracy at 50 multistarts (computation time ∼7 s). The LevelCheck algorithm correctly identified target vertebrae in intraoperative mobile radiographs of the thoracolumbar spine, demonstrating acceptable computation time, compatibility with routinely obtained preoperative CT scans, and warranting investigation in prospective studies. N/A. |
Author | Groves, Mari Witham, Timothy F Puvanesarajah, Varun Wolinsky, Jean-Paul Siewerdsen, Jeffrey H Vogt, Sebastian Uneri, Ali Wang, Adam S Sciubba, Daniel M Kleinszig, Gerhard Kosztowski, Thomas A Elder, Benjamin D Lo, Sheng-Fu L Liauw, Jason A De Silva, Tharindu Aygun, Nafi Gokaslan, Ziya L Otake, Yoshito Goodwin, C Rory Bydon, Ali |
AuthorAffiliation | 3 Siemens Healthcare, Erlangen, Germany 2 Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA 4 Russell H. Morgan Department of Radiology, Johns Hopkins University, Baltimore, MD, USA 1 Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA |
AuthorAffiliation_xml | – name: 3 Siemens Healthcare, Erlangen, Germany – name: 1 Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA – name: 2 Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA – name: 4 Russell H. Morgan Department of Radiology, Johns Hopkins University, Baltimore, MD, USA |
Author_xml | – sequence: 1 givenname: Sheng-Fu L surname: Lo fullname: Lo, Sheng-Fu L organization: Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD †Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD ‡Siemens Healthcare, Erlangen, Germany; and §The Russell H. Morgan Department of Radiology, Johns Hopkins University, Baltimore, MD – sequence: 2 givenname: Yoshito surname: Otake fullname: Otake, Yoshito – sequence: 3 givenname: Varun surname: Puvanesarajah fullname: Puvanesarajah, Varun – sequence: 4 givenname: Adam S surname: Wang fullname: Wang, Adam S – sequence: 5 givenname: Ali surname: Uneri fullname: Uneri, Ali – sequence: 6 givenname: Tharindu surname: De Silva fullname: De Silva, Tharindu – sequence: 7 givenname: Sebastian surname: Vogt fullname: Vogt, Sebastian – sequence: 8 givenname: Gerhard surname: Kleinszig fullname: Kleinszig, Gerhard – sequence: 9 givenname: Benjamin D surname: Elder fullname: Elder, Benjamin D – sequence: 10 givenname: C Rory surname: Goodwin fullname: Goodwin, C Rory – sequence: 11 givenname: Thomas A surname: Kosztowski fullname: Kosztowski, Thomas A – sequence: 12 givenname: Jason A surname: Liauw fullname: Liauw, Jason A – sequence: 13 givenname: Mari surname: Groves fullname: Groves, Mari – sequence: 14 givenname: Ali surname: Bydon fullname: Bydon, Ali – sequence: 15 givenname: Daniel M surname: Sciubba fullname: Sciubba, Daniel M – sequence: 16 givenname: Timothy F surname: Witham fullname: Witham, Timothy F – sequence: 17 givenname: Jean-Paul surname: Wolinsky fullname: Wolinsky, Jean-Paul – sequence: 18 givenname: Nafi surname: Aygun fullname: Aygun, Nafi – sequence: 19 givenname: Ziya L surname: Gokaslan fullname: Gokaslan, Ziya L – sequence: 20 givenname: Jeffrey H surname: Siewerdsen fullname: Siewerdsen, Jeffrey H |
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Snippet | A 3-dimensional-2-dimensional (3D-2D) image registration algorithm, "LevelCheck," was used to automatically label vertebrae in intraoperative mobile... STUDY DESIGNA 3-dimensional-2-dimensional (3D-2D) image registration algorithm, "LevelCheck," was used to automatically label vertebrae in intraoperative... |
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SubjectTerms | Adult Aged Algorithms Automation Female Humans Imaging, Three-Dimensional - methods Intraoperative Care Lumbar Vertebrae Male Middle Aged Retrospective Studies Spine - diagnostic imaging Spine - surgery Surgery, Computer-Assisted - methods Thoracic Vertebrae Tomography, X-Ray Computed |
Title | Automatic localization of target vertebrae in spine surgery: clinical evaluation of the LevelCheck registration algorithm |
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