Evidence map›Paper›PMID 37718452›Full record

ArticleBMC medical imaging2023

Unsupervised registration of 3D knee implant components to biplanar X-ray images.

Dac Cong Tai Nguyen, Said Benameur, Max Mignotte, Frédéric Lavoie

Abstract read
In one paragraph

Article in BMC medical imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. Article
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

4 authors.

Dac Cong Tai NguyenDépartement d'Informatique et de Recherche Opérationnelle (DIRO), Université de Montréal, Montréal, Québec, Canada. dac.cong.tai.nguyen@umontreal.ca.ORCID http://orcid.org/0000-0002-7689-5512
Said BenameurEiffel Medtech Inc., Montréal, Québec, Canada.
Max MignotteDépartement d'Informatique et de Recherche Opérationnelle (DIRO), Université de Montréal, Montréal, Québec, Canada.
Frédéric LavoieEiffel Medtech Inc., Montréal, Québec, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundRegistration of three-dimensional (3D) knee implant components to radiographic images provides the 3D position of the implants which aids to analyze the component alignment after total knee arthroplasty.

methodsWe present an automatic 3D to two-dimensional (2D) registration using biplanar radiographic images based on a hybrid similarity measure integrating region and edge-based information. More precisely, this measure is herein defined as a weighted combination of an edge potential field-based similarity, which represents the relation between the external contours of the component projections and an edge potential field estimated on the two radiographic images, and an object specificity property, which is based on the distinction of the region-label inside and outside of the object.

resultsThe accuracy of our 3D/2D registration algorithm was assessed on a sample of 64 components (32 femoral components and 32 tibial components). In our tests, we obtained an average of the root mean square error (RMSE) of 0.18 mm, which is significantly lower than that of both single similarity methods, supporting our hypothesis of better stability and accuracy with the proposed approach.

conclusionOur method, which provides six accurate registration parameters (three rotations and three translations) without requiring any fiducial markers, makes it possible to perform the important analyses on the rotational alignment of the femoral and tibial components on a large number of cases. In addition, this method can be extended to register other implants or bones.

Indexed as

Arthroplasty, Replacement, KneeKnee ProsthesisAlgorithmsFemurHumansX-Rays3D/2D registrationKnee implant componentsOrthopaedic implantsX-ray images

Identifiers

PMID37718452
PMCPMC10506289

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.