3D–2D registration in mobile radiographs: algorithm development and preliminary clinical evaluation
Open accessAbstract
An image-based 3D–2D registration method is presented using radiographs acquired in the uncalibrated, unconstrained geometry of mobile radiography. The approach extends a previous method for six degree-of-freedom (DOF) registration in C-arm fluoroscopy (namely ‘LevelCheck’) to solve the 9-DOF estimate of geometry in which the position of the source and detector are unconstrained. The method was implemented using a gradient correlation similarity metric and stochastic derivative-free optimization on a GPU. Development and evaluation were conducted in three steps. First, simulation studies were performed that involved a CT scan of an anthropomorphic body phantom and 1000 randomly generated digitally reconstructed radiographs in posterior–anterior and lateral views. A median projection distance error (PDE) of 0.007 mm was achieved with 9-DOF registration compared to 0.767 mm for 6-DOF. Second, cadaver studies were conducted using mobile radiographs acquired in three anatomical regions (thorax, abdomen and pelvis) and three levels of source-detector distance (~800, ~1000 and ~1200 mm). The 9-DOF method achieved a median PDE of 0.49 mm (compared to 2.53 mm for the 6-DOF method) and demonstrated robustness in the unconstrained imaging geometry. Finally, a retrospective clinical study was conducted with intraoperative radiographs of the spine exhibiting real anatomical deformation and image content mismatch (e.g. interventional devices in the radiograph that were not in the CT), demonstrating a PDE = 1.1 mm for the 9-DOF approach. Average computation time was 48.5 s, involving 687 701 function evaluations on average, compared to 18.2 s for the 6-DOF method. Despite the greater computational load, the 9-DOF method may offer a valuable tool for target localization (e.g. decision support in level counting) as well as safety and quality assurance checks at the conclusion of a procedure (e.g. overlay of planning data on the radiograph for verification of the surgical product) in a manner consistent with natural surgical workflow.
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In previous work on vertebral level localization in spine surgery, the challenge due to content mismatch was mitigated by manually defined polygonal masks that excluded extraneous regions during similarity metric evaluation.
“However, the challenge due to content mismatch was mitigated by manually defined polygonal masks that excluded extraneous regions during similarity metric evaluation.”
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 2015
The Patient study section (page unavailable) says a weighting mask can down-weight regions with deformation or instrumentation so they contribute less to the similarity metric. It says such masks could potentially be made manually, but does not describe polygonal masks or establish that manually defined masks were used to mitigate content mismatch in this study. (LLM, conf=0.85)
Experimental — accuracy not yet measuredPrevious work on vertebral level localization in spine surgery showed promising results with GC similarity.
“For example, previous work on vertebral level localization in spine surgery (Lo et al 2015, Otake et al 2015) showed promising results with GC similarity.”
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 2015
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References 31 · Cited by 41
Research works printed but not linked: 24
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- Authors
- Otake, Y., Wang, A. S., Uneri, A., Kleinszig, G., Vogt, S., Aygun, N., Lo, S.-F. L., Wolinsky, J.-P., Gokaslan, Z. L., & Siewerdsen, J. H.
- Affiliations
- Johns Hopkins University (US)
- Siemens Healthineers (Germany) (DE)
- Johns Hopkins University
- Corresponding author
- Siewerdsen, Jeffrey H
- ORCID
- Venue
- Physics in Medicine & Biology
- Publisher
- IOP Publishing
- Published
- 2015-02-12
- Volume
- 60
- Issue
- 5
- Pages
- 2075-2090
- ISSN
- 0031-9155 (ISSN-L)
- 1361-6560
- PMC
- PMC4640192
- OpenAlex
- W2031954532
- Cited by
- 41
- References
- 31
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 1 · 2023: 6 · 2022: 3 · 2021: 4 · 2020: 2 · 2019: 3 · 2018: 4 · 2017: 8 · 2016: 4 · 2015: 1
- FWCI
- 4.92
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Physics in Medicine and Biology (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of Biomedical Imaging and Bioengineering (R01EB017226)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiation Dose and Imaging
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
- Keywords
- 2D-3D registration, mobile radiography, derivative-free optimization, GPU acceleration, spine, surgical target localization, cadaveric study, quality assurance
- MeSH terms
- Abdomen, Algorithms, Cadaver, Computer Simulation, Fluoroscopy, Humans, Pelvis, Retrospective Studies, Spine, Radiography, Thoracic, Tomography, X-Ray Computed, Phantoms, Imaging, Imaging, Three-Dimensional
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