A Hybrid 3D-2D Image Registration Framework for Pedicle Screw Trajectory Registration between Intraoperative X-ray Image and Preoperative CT Image
Open accessAbstract
Pedicle screw insertion is considered a complex surgery among Orthopaedics surgeons. Exclusively to prevent postoperative complications associated with pedicle screw insertion, various types of image intensity registration-based navigation systems have been developed. These systems are computation-intensive, have a small capture range and have local maxima issues. On the other hand, deep learning-based techniques lack registration generalizability and have data dependency. To overcome these limitations, a patient-specific hybrid 3D-2D registration principled framework was designed to map a pedicle screw trajectory between intraoperative X-ray image and preoperative CT image. An anatomical landmark-based 3D-2D Iterative Control Point (ICP) registration was performed to register a pedicular marker pose between the X-ray images and axial preoperative CT images. The registration framework was clinically validated by generating projection images possessing an optimal match with intraoperative X-ray images at the corresponding control point registration. The effectiveness of the registered trajectory was evaluated in terms of displacement and directional errors after reprojecting its position on 2D radiographic planes. The mean Euclidean distances for the Head and Tail end of the reprojected trajectory from the actual trajectory in the AP and lateral planes were shown to be 0.6–0.8 mm and 0.5–1.6 mm, respectively. Similarly, the corresponding mean directional errors were found to be 4.90 and 20. The mean trajectory length difference between the actual and registered trajectory was shown to be 2.67 mm. The approximate time required in the intraoperative environment to axially map the marker position for a single vertebra was found to be 3 min. Utilizing the markerless registration techniques, the designed framework functions like a screw navigation tool, and assures the quality of surgery being performed by limiting the need of postoperative CT.
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Registration of intraoperative 2D X-rays to 3D preoperative scans is a prerequisite for numerous advanced image guidance techniques, including vertebral level localization, surgical plan reprojection, instrument tracking, motion correction, and surgical robotics.
“The registration of intraoperative 2D X-rays to 3D preoperative scans is a prerequisite for numerous advanced image guidance techniques, including vertebral level localization [11], surgical plan reprojection [12,13], instrument tracking [14], motion correction [15], and surgical robotics [16].”
From Rapid patient-specific neural networks for intraoperative X-ray to volume registration (2025) · cites this paper as [12]
The Literature Survey (§2.3; page unavailable) says some robot-assisted surgery systems integrate with 2D fluoroscopic devices, while most use optical cameras for tracking. The Introduction describes 3D–2D registration for surgical navigation, but the paper does not establish registration of intraoperative X-rays to preoperative scans as a prerequisite for surgical robotics—the part attributed to this source by citation [16]. (LLM, conf=0.82)
Experimental — accuracy not yet measuredSegmentation labels are relatively easy to derive for most structures on CT and MRI thanks to open-source tools such as TotalSegmentator or MOOSE, but other structures like vessels remain difficult to segment.
“While segmentation labels are relatively easy to derive for most structures on CT and MRI thanks to open-source tools such as TotalSegmentator [12] or MOOSE [13,14], other structures like vessels remain difficult to segment.”
From Rapid patient-specific neural networks for intraoperative X-ray to volume registration (2025) · cites this paper as [12]
The Materials and Methods section describes registration between preoperative CT and intraoperative X-rays, not an open-source CT or MRI segmentation tool. The Literature Survey mentions cerebral vessels in the context of registration, but does not discuss whether they are difficult to segment. Searches for MOOSE, segmentation labels, and vessel segmentation found no passage addressing the attributed claim. (LLM, conf=0.76)
Experimental — accuracy not yet measured
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Self-citationRapid patient-specific neural networks for X-ray to volume registration
Gopalakrishnan, V., Chlorogiannis, D., Abumoussa, A. L., Larson, A. M., Haouchine, N., Orbach, D. B., Frisken, S., Dey, N., & Golland, P. (2026) · Nature · doi:10.1038/s41586-026-11045-x · Cited by 7 · see citing works
- Authors
- Naik, R. R., Hoblidar, A., Bhat, S. N., Ampar, N., & Kundangar, R.
- Affiliations
- Manipal Academy of Higher Education (IN)
- Manipal Institute of Technology, Manipal Academy of Higher Education Manipal, Manipal 576104, India
- Corresponding author
- Bhat, Shyamasunder N.
- Venue
- Journal of Imaging
- Publisher
- MDPI AG
- Published
- 2022-07-06
- Volume
- 8
- Issue
- 7
- Pages
- 185
- ISSN
- 2313-433X (ISSN-L)
- PMC
- PMC9324544
- OpenAlex
- W4283833779
- Cited by
- 16
- References
- 49
- Citations by year
- 2026: 5 · 2025: 2 · 2024: 4 · 2023: 5
- FWCI
- 2.63
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Journal of Imaging (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Science and Engineering Board (SERB) (EMR/2017/004742)
- Topics
- Health Sciences › Medicine › Surgery › Spinal Fractures and Fixation Techniques
- Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Keywords
- pedicle screw navigation, preoperative CT, iterative closest point, markerless registration, spinal surgery, directional error
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