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

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.

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.

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.

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.