Evidence map›Paper›PMID 35877629›Full record

ArticleJournal of imaging2022

A Hybrid 3D-2D Image Registration Framework for Pedicle Screw Trajectory Registration between Intraoperative X-ray Image and Preoperative CT Image.

Roshan Ramakrishna Naik, Anitha Hoblidar, Shyamasunder N Bhat, Nishanth Ampar, Raghuraj Kundangar

Open access · goldAbstract read
In one paragraph

Article in Journal of imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
1.0field-weighted citation impact, top 29% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed, 13 citations in OpenAlex.

  1. Article
  2. Article
  3. Feature-based multimodal 3D/2D and 3D/3D registration framework for pedicle screw registration and evaluation.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 1 institution in 1 country.

Roshan Ramakrishna NaikManipal Institute of Technology, Manipal Academy of Higher Education Manipal, Manipal 576104, India.
Anitha HoblidarManipal Institute of Technology, Manipal Academy of Higher Education Manipal, Manipal 576104, India.
Shyamasunder N BhatKasturba Medical College, Manipal Academy of Higher Education Manipal, Manipal 576104, India.ORCID 0000-0001-9545-4838
Nishanth AmparKasturba Medical College, Manipal Academy of Higher Education Manipal, Manipal 576104, India.ORCID 0000-0002-4696-7167
Raghuraj KundangarKasturba Medical College, Manipal Academy of Higher Education Manipal, Manipal 576104, India.ORCID 0000-0003-3346-7304
Manipal Academy of Higher Education · IN

Funding

Science and Engineering Research Board EMR/2017/004742
6 · The paper itself

Abstract

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.

Indexed as

3D-2D registrationdigitally reconstructed radiographsiterative control point registrationoptimizationpedicle screw insertionsimilarity metric

Identifiers

PMID35877629
PMCPMC9324544
OpenAlexW4283833779

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.