Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Current advancements and future prospects in the biorefinery and hyper-production of astaxanthin from <i>Haematococcus pluvialis</i>.

BioImpacts : BI·2026
Same author

Deep learning-based femoral reconstruction from intraoperative point clouds for enhanced knee arthroplasty registration.

International journal of computer assisted radiology and surgery·2026
Same author

Characterizing forearm skeletal muscle composition and function in breast cancer-related lymphedema using B-mode ultrasonography.

Clinical physiology and functional imaging·2026
Same author

From Speech to Sonography: Spectral Networks for Ultrasound Microstructure Classification.

IEEE transactions on bio-medical engineering·2025
Same author

Grounding DINO-US-SAM: Text-Prompted Multiorgan Segmentation in Ultrasound With LoRA-Tuned Vision-Language Models.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control·2025
Same author

DINOMotion: Advanced Robust Tissue Motion Tracking With DINOv2 in 2D-Cine MRI-Guided Radiotherapy.

IEEE transactions on bio-medical engineering·2025

Related Experiment Video

Updated: Jan 13, 2026

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration
05:05

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Published on: November 23, 2019

8.4K

Statistical shape model-based estimation of registration error in computer-assisted total knee arthroplasty.

Behnaz Gheflati1, Morteza Mirzaei2, Joel Zuhars2

  • 1Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada. b_ghefla@encs.concordia.ca.

International Journal of Computer Assisted Radiology and Surgery
|January 6, 2026
PubMed
Summary

Patient-specific femur shape significantly impacts registration accuracy in computer-assisted total knee arthroplasty (TKA). Understanding bone geometry variability enhances precision for orthopedic surgery.

Keywords:
Computer-assisted surgeryFemurStatistical shape modelingSurface registration errorTotal knee arthroplasty

More Related Videos

The Use of Mixed Reality in Custom-Made Revision Hip Arthroplasty: A First Case Report
07:45

The Use of Mixed Reality in Custom-Made Revision Hip Arthroplasty: A First Case Report

Published on: August 4, 2022

3.8K
Individualized Stem-positioning in Calcar-guided Short-stem Total Hip Arthroplasty
09:31

Individualized Stem-positioning in Calcar-guided Short-stem Total Hip Arthroplasty

Published on: February 27, 2018

12.4K

Related Experiment Videos

Last Updated: Jan 13, 2026

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration
05:05

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Published on: November 23, 2019

8.4K
The Use of Mixed Reality in Custom-Made Revision Hip Arthroplasty: A First Case Report
07:45

The Use of Mixed Reality in Custom-Made Revision Hip Arthroplasty: A First Case Report

Published on: August 4, 2022

3.8K
Individualized Stem-positioning in Calcar-guided Short-stem Total Hip Arthroplasty
09:31

Individualized Stem-positioning in Calcar-guided Short-stem Total Hip Arthroplasty

Published on: February 27, 2018

12.4K

Area of Science:

  • Orthopedic Surgery
  • Medical Imaging
  • Biomechanical Engineering

Background:

  • Computer-assisted surgical navigation enhances total knee arthroplasty (TKA) precision.
  • Surface registration is a critical step but a significant source of error in TKA.
  • Patient anatomy variability can affect the accuracy of navigation systems.

Purpose of the Study:

  • Investigate the influence of patient-specific femoral bone geometry on registration accuracy.
  • Improve the reliability and consistency of computer-assisted orthopedic procedures.
  • Identify geometric factors affecting surface registration in TKA.

Main Methods:

  • Utilized 18 3D-printed femur models for simulated intraoperative digitization.
  • Registered surface points to CT-derived models using an iterative closest point (ICP) algorithm.
  • Correlated registration errors with shape coefficients from a statistical shape model (SSM) derived from 114 CT femurs.

Main Results:

  • Significant correlations found between specific shape coefficients and registration errors (p < 0.05).
  • Third and fourth principal shape modes strongly associated with rotational misalignments (flexion-extension, varus-valgus).
  • Distal femur geometric variability, particularly condylar morphology, critically impacts registration stability and accuracy.

Conclusions:

  • Patient-specific bone geometry is a major determinant of registration errors in TKA.
  • Statistical shape model features can predict registration performance.
  • Quantifying anatomical variability offers insights into surgical precision in computer-assisted TKA.