CoChat
Sign Up
Cited by 7Knee injuries and reconstruction techniquesMedical Image Segmentation TechniquesLower Extremity Biomechanics and Pathologies

The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.

An in vivo subject-specific 3D functional knee joint model using combined MR imaging

Open access
Chen, Bailiang, Lambrou, Tryphon, Offiah, Amaka C., Gondim Teixeira, Pedro A., Fry, Martin, Todd-Pokropek, Andrew·2013·International Journal of Computer Assisted Radiology and Surgery
DOI 10.1007/s11548-012-0801-7
Open this paper in CoChat — free account

Abstract

Purpose We aim to quantitatively characterise the knee joint function in vivo under body-weight-bearing conditions via subject-specific models extracted from magnetic resonance (MR) data, in order to better understand the knee joint kinematic function in 3D.Methods Six healthy volunteers without any record of knee abnormality were scanned using a combined MR imaging strategy to record quasi-squatting motion and 3D knee anatomy. After a semi-automatic segmentation to delineate tibio-femoral articulation components, motion data were mapped to the anatomical data using a bi-rigid registration in order to achieve six degrees of freedom. The individual knee joint function was characterised by analysing the tibio-femoral articulation contact mechanism based on the reconstructed models in 3D and MR images in 2D. Contact points were extracted and their trajectory was plotted on the tibia plateau.Results The 3D models clearly show the relative rotation and gliding between tibia and femur during global flexion. Within the measured flexion arc, the contact points move less between 30[Formula: see text] and 100[Formula: see text] on both tibial plateaux as compared to that on the rest of the flexion arc. Four out of the six volunteers showed a global pattern of less moving extent of contact points on the medial tibial plateau than on the lateral tibial plateau in both 3D and 2D.Conclusion The proposed subject-specific model is able to characterise knee joint kinematic function. It provides a way to describe knee joint surface kinematics quantitatively, which may help to better understand the knee function and joint derangements.

Overview

Integrity signals

Registry

Crossref · 10.1007/s11548-012-0801-7

Cites

29 of 29 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Show all 29 references

Cited by

Sorted by citations

See all 7

Related

Metadata

Authors
Chen, B., Lambrou, T., Offiah, A. C., Gondim Teixeira, P. A., Fry, M., & Todd-Pokropek, A.
Affiliations
  • Centre Hospitalier Régional et Universitaire de Nancy (FR)
  • University of Lincoln (GB)
  • University of Sheffield (GB)
  • University College London (GB)
Corresponding author
Chen, Bailiang
ORCID
Venue
International Journal of Computer Assisted Radiology and Surgery
Publisher
Springer Science and Business Media LLC
Published
2012-12-02
Volume
8
Issue
5
Pages
741-750
ISSN
  • 1861-6410 (ISSN-L)
  • 1861-6429
DOI
10.1007/s11548-012-0801-7
OpenAlex
W2080682424
Cited by
7
References
29
Citations by year
2021: 2 · 2019: 1 · 2018: 1 · 2017: 1 · 2016: 1 · 2015: 1
FWCI
0.33
Type
journal article
Language
en
Open access
Licence
Locations
  • International Journal of Computer Assisted Radiology and Surgery (publishedVersion)
  • PubMed (publishedVersion)
  • HAL (submittedVersion)
  • UCL Discovery (University College London) (submittedVersion)
  • Figshare (submittedVersion, cc-by)
Topics
  • Health Sciences › Medicine › Surgery › Knee injuries and reconstruction techniques
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
  • Physical Sciences › Engineering › Biomedical Engineering › Lower Extremity Biomechanics and Pathologies
Keywords
knee kinematics, subject-specific knee model, squatting motion
MeSH terms
Adult, Female, Humans, Knee Joint, Magnetic Resonance Imaging, Male, Range of Motion, Articular, Imaging, Three-Dimensional, Healthy Volunteers

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review