Automatic localization of vertebral levels in x-ray fluoroscopy using 3D–2D registration: a tool to reduce wrong-site surgery
Open accessAbstract
Surgical targeting of the incorrect vertebral level (wrong-level surgery) is among the more common wrong-site surgical errors, attributed primarily to the lack of uniquely identifiable radiographic landmarks in the mid-thoracic spine. The conventional localization method involves manual counting of vertebral bodies under fluoroscopy, is prone to human error and carries additional time and dose. We propose an image registration and visualization system (referred to as LevelCheck), for decision support in spine surgery by automatically labeling vertebral levels in fluoroscopy using a GPU-accelerated, intensity-based 3D-2D (namely CT-to-fluoroscopy) registration. A gradient information (GI) similarity metric and a CMA-ES optimizer were chosen due to their robustness and inherent suitability for parallelization. Simulation studies involved ten patient CT datasets from which 50 000 simulated fluoroscopic images were generated from C-arm poses selected to approximate the C-arm operator and positioning variability. Physical experiments used an anthropomorphic chest phantom imaged under real fluoroscopy. The registration accuracy was evaluated as the mean projection distance (mPD) between the estimated and true center of vertebral levels. Trials were defined as successful if the estimated position was within the projection of the vertebral body (namely mPD <5 mm). Simulation studies showed a success rate of 99.998% (1 failure in 50 000 trials) and computation time of 4.7 s on a midrange GPU. Analysis of failure modes identified cases of false local optima in the search space arising from longitudinal periodicity in vertebral structures. Physical experiments demonstrated the robustness of the algorithm against quantum noise and x-ray scatter. The ability to automatically localize target anatomy in fluoroscopy in near-real-time could be valuable in reducing the occurrence of wrong-site surgery while helping to reduce radiation exposure. The method is applicable beyond the specific case of vertebral labeling, since any structure defined in pre-operative (or intra-operative) CT or cone-beam CT can be automatically registered to the fluoroscopic scene.
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GI and GC metrics have been shown previously to be useful when handling partial content mismatch.
“The GI and GC metrics have been shown previously (Penney et al 1998, Pluim et al 2000, Otake et al 2012b) to be useful when handling partial content mismatch.”
From 3D–2D image registration for target localization in spine surgery: investigation of similarity metrics providing robustness to content mismatch (2016) · cites this paper as Otake et al 2012b
Methods §2.1 says GI is robust to image-content mismatches, including surgical tools visible in fluoroscopy but absent from the preoperative CT. The Introduction identifies GC as a local-intensity metric and describes that category as generally robust in 3D–2D registration, but the paper does not specifically establish GC’s usefulness for partial content mismatch. Page numbers were not provided in our copy. (LLM, conf=0.86)
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- Authors
- Otake, Y., Schafer, S., Stayman, J. W., Zbijewski, W., Kleinszig, G., Graumann, R., Khanna, A. J., & Siewerdsen, J. H.
- Affiliations
- Johns Hopkins University (US)
- Siemens Healthineers (Germany) (DE)
- Venue
- Physics in Medicine and Biology
- Publisher
- IOP Publishing
- Published
- 2012-08-03
- Volume
- 57
- Issue
- 17
- Pages
- 5485-5508
- ISSN
- 0031-9155 (ISSN-L)
- 1361-6560
- PMC
- PMC3429949
- OpenAlex
- W2070914374
- Cited by
- 79
- References
- 53
- Citations by year
- 2026: 2 · 2025: 1 · 2024: 4 · 2023: 8 · 2022: 7 · 2021: 6 · 2020: 4 · 2019: 4 · 2018: 8 · 2017: 10 · 2016: 5 · 2015: 11 · 2014: 6 · 2013: 2 · 2012: 1
- FWCI
- 3.65
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- Physics in Medicine and Biology (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Johns Hopkins University
- National Institutes of Health
- National Cancer Institute (R01CA127444)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Surgery › Spinal Fractures and Fixation Techniques
- Keywords
- wrong-site surgery, X-ray fluoroscopy, 2D-3D registration, spinal surgery, surgical decision support, anthropomorphic chest phantom, radiation dose reduction, cone-beam computed tomography
- MeSH terms
- Automation, Fluoroscopy, Humans, Spine, Tomography, X-Ray Computed, Phantoms, Imaging, Imaging, Three-Dimensional, Surgery, Computer-Assisted
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