Dynamic computer tomography (CT) is an emerging modality to analyze in-vivo joint kinematics at the bone level, but it requires manual bone segmentation and, in some instances, landmark identification. The objective of this study is to present an automated workflow for the assessment of three-dimensional in vivo joint kinematics from dynamic musculoskeletal CT images. The proposed method relies on a multi-atlas, multi-label segmentation and landmark propagation framework to extract bony structures and detect anatomical landmarks on the CT dataset. The segmented structures serve as regions of interest for the subsequent motion estimation across the dynamic sequence. The landmarks are propagated across the dynamic sequence for the construction of bone embedded reference frames from which kinematic parameters are estimated. We applied our workflow on dynamic CT images obtained from 15 healthy subjects on two different joints: thumb base (n = 5) and knee (n = 10). The proposed method resulted in segmentation accuracies of 0.90 ± 0.01 for the thumb dataset and 0.94 ± 0.02 for the knee as measured by the Dice score coefficient. In terms of motion estimation, mean differences in cardan angles between the automated algorithm and manual segmentation, and landmark identification performed by an expert were below 1°. Intraclass correlation (ICC) between cardan angles from the algorithm and results from expert manual landmarks ranged from 0.72 to 0.99 for all joints across all axes. The proposed automated method resulted in reproducible and reliable measurements, enabling the assessment of joint kinematics using 4DCT in clinical routine.

To quantitatively evaluate the impact of a cardiac acquisition CT mode on motion artifacts in comparison to a conventional cine mode for dynamic musculoskeletal (MSK) imaging. A rotating PMMA phantom with air-filled holes drilled at varying distances from the disk center corresponding to linear hole speeds of 0.75 cm/s, 2.0 cm/s, and 3.6 cm/s was designed. Dynamic scans were obtained in cardiac and cine modes while the phantom was rotating at 48°/s in the CT scanner. An automated workflow to compute the Jaccard distance (JD) was established to quantify degree of motion artifacts in the reconstructed phantom images. JD values between the cardiac and cine scan modes were compared using a paired sample t-test. In addition, three healthy volunteers were scanned with both modes during a cyclic flexion-extension motion of the knee and analysed using the proposed metric. For all hole sizes and speeds, the cardiac scan mode had significantly lower (p-value <0.001) JD values. (0.39 [0.32-0.46]) i.e less motion artifacts in comparison to the cine mode (0.72 [0.68-0.76]). For both modes, a progressive increase in JD was also observed as the linear speed of the holes increased from 0.75 cm/s to 3.6 cm/s. The dynamic images of the three healthy volunteers showed less artifacts when scanned in cardiac mode compared to cine mode, and this was quantitatively confirmed by the JD values. A cardiac scan mode could be used to study dynamic musculoskeletal phenomena especially of fast-moving joints since it significantly minimized motion artifacts.