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

A Nepal; N M Brisson; T C Wood; G N Duda; J R Reichenbach; M Krämer

doi:10.1016/j.zemedi.2025.06.005

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

Z Med Phys. 2025 Jul 4:S0939-3889(25)00088-1. doi: 10.1016/j.zemedi.2025.06.005. Online ahead of print.

Authors

A Nepal¹, N M Brisson², T C Wood³, G N Duda⁴, J R Reichenbach⁵, M Krämer⁶

Affiliations

¹ Medical Physics Group, Institute of Diagnostic and Interventional Radiology, Jena University Hospital, Friedrich Schiller University Jena, Germany. Electronic address: aayush.nepal@uni-jena.de.
² Julius Wolff Institute, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany; Berlin Movement Diagnostics (BeMoveD), Center for Musculoskeletal Surgery, Charité - Universitätsmedizin Berlin, Berlin, Germany. Electronic address: nicholas.brisson@charite.de.
³ Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK. Electronic address: tobias.wood@kcl.ac.uk.
⁴ Julius Wolff Institute, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany; Berlin Movement Diagnostics (BeMoveD), Center for Musculoskeletal Surgery, Charité - Universitätsmedizin Berlin, Berlin, Germany. Electronic address: georg.duda@charite.de.
⁵ Medical Physics Group, Institute of Diagnostic and Interventional Radiology, Jena University Hospital, Friedrich Schiller University Jena, Germany. Electronic address: Juergen.Reichenbach@med.uni-jena.de.
⁶ Medical Physics Group, Institute of Diagnostic and Interventional Radiology, Jena University Hospital, Friedrich Schiller University Jena, Germany; Institute of Diagnostic and Interventional Radiology, Jena University Hospital, Friedrich Schiller University Jena, Germany. Electronic address: martin.kraemer@med.uni-jena.de.

PMID: 40617724
DOI: 10.1016/j.zemedi.2025.06.005

Abstract

Purpose: Dynamic magnetic resonance imaging (MRI) enables in vivo imaging of bone motion during knee movement, but quantifying joint kinematics from these images remains technically challenging due to image quality trade-offs inherent in dynamic acquisition sequences. We aimed to develop a semi-automated pipeline for tracking femoral and tibial motion from sagittal plane CINE MRI during active knee flexion and extension. The performance of the method was evaluated by quantifying: (i) bone boundary alignment error, (ii) frame segmentation processing time, and (iii) consistency of derived osteokinematic parameters, with the latter two compared against manual segmentation.

Methods: The presented algorithm combines Canny edge detection and connected-component labeling with frame-to-frame transformation optimization to track bone boundaries. The approach was validated in five healthy volunteers performing controlled knee flexion and extension using a dedicated MRI-compatible device. The relative bone displacements measured using the semi-automated approach were qualitatively compared to that from manual segmentation. All bone displacements were defined in the two-dimensional (2D) image coordinate system, with the centroid of the tibial segment tracked relative to the centroid of the femoral segment in the horizontal and vertical directions.

Results: The semi-automated tracking method achieved an average alignment error of 0.40 ± 0.02 mm for both bones, with processing time reduced from approximately 15 minutes for manual segmentation to less than 5 minutes for semi-automated segmentation per dataset. Both approaches showed similar relative bone motion patterns, with horizontal displacement of the tibia with respect to the femur ranging between 8 and 28 mm and vertical displacement remaining relatively constant at around 57 mm through the knee motion cycle. Further analysis revealed that the semi-automated method demonstrated improved precision with smaller standard deviations (SDs) in displacement measurements compared to the manual approach, with horizontal displacements of 1.7-2.7 mm vs. 2.2-3.3 mm and vertical displacements of 0.7-1.2 mm vs. 0.9-1.7 mm.

Conclusion: These results demonstrate the potential of the semi-automated method for reliable and time-efficient quantification of relative bone positions during volitional knee motion in dynamic MRI protocols. The shorter processing time and the demonstrated reliability of the semi-automated method support its utility for analyzing dynamic MRI data.

Keywords: CINE MRI reconstruction; Canny edge detection; Knee osteokinematics; Radial golden-angle acquisition.