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Knee Joint

Knee Joint

The knee joint is the most complicated joint in the body. It consists of three articulations– two tibiofemoral and one patellofemoral. As is characteristic of synovial joints, the knee joint has a thin articular capsule that partially surrounds this joint cavity. Additionally, several ligaments, muscles, and cartilaginous structures support the movement of the knee.
A total of seven ligaments support the knee joint. The patellar ligament, which is also attached to the quadriceps femoris group...

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Related Experiment Video

Updated: Jul 9, 2026

Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion

Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion

Published on: April 11, 2018

Semi-automated bone tracking in dynamic CINE MRI during controlled knee motion.

A Nepal¹, N M Brisson², T C Wood³

¹Medical Physics Group, Institute of Diagnostic and Interventional Radiology, Jena University Hospital, Friedrich Schiller University Jena, Germany.

Zeitschrift Fur Medizinische Physik

|July 5, 2025

Summary

A new semi-automated method accurately tracks bone motion in dynamic knee MRI scans, significantly reducing processing time compared to manual methods. This technique improves precision for analyzing knee kinematics during movement.

Keywords:

CINE MRI reconstruction Canny edge detection Knee osteokinematics Radial golden-angle acquisition

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Last Updated: Jul 9, 2026

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Published on: April 11, 2018

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Published on: July 2, 2021

Area of Science:

Biomedical Engineering
Radiology
Orthopedics

Background:

Dynamic magnetic resonance imaging (MRI) allows in vivo visualization of bone movement during knee motion.
Quantifying joint kinematics from dynamic MRI is challenging due to image quality limitations.
Accurate assessment of knee joint motion is crucial for understanding biomechanics and diagnosing conditions.

Purpose of the Study:

To develop a semi-automated pipeline for tracking femoral and tibial motion from CINE MRI during knee flexion and extension.
To evaluate the accuracy, processing time, and consistency of the developed method against manual segmentation.

Main Methods:

A semi-automated algorithm combining Canny edge detection, connected-component labeling, and frame-to-frame transformation optimization was developed.
The method was validated on five healthy volunteers using a dedicated MRI-compatible device for controlled knee movements.
Bone displacements were tracked in the 2D image coordinate system, comparing semi-automated results to manual segmentation.

Main Results:

The semi-automated method achieved an average bone boundary alignment error of 0.40 ± 0.02 mm.
Processing time was reduced from ~15 minutes (manual) to <5 minutes (semi-automated) per dataset.
The semi-automated method demonstrated improved precision with smaller standard deviations in displacement measurements compared to manual segmentation.

Conclusions:

The developed semi-automated method offers a reliable and time-efficient approach for quantifying relative bone positions in dynamic MRI.
This technique facilitates the analysis of volitional knee motion, supporting clinical and research applications.
The improved efficiency and reliability make it suitable for analyzing dynamic MRI data.