• ABSTRACT
    • Four-dimensional computed tomography (4DCT) can be used to quantify joint mechanics during dynamic tasks. Post-processing of 3D anatomical renderings permits calculation of joint surface arthrokinematics. Given the complexity and high dimensionality of arthrokinematic data, as well as variations in interparticipant morphology, sophisticated analytical techniques permitting comparisons between participants and throughout motion cycles are needed. Statistical parametric mapping (SPM) has been applied to biomechanical and imaging datasets to overcome this challenge; however, applications to 4DCT remain under-explored. The proposed pipeline is demonstrated at the distal radioulnar joint (DRUJ). This application explores relationships between wrist position (a continuous variable) and arthrokinematic interosseous proximities (a surface-based measure) in two contexts. Static-neutral CT data from 30 normative participants are used in the first application. Bilateral 4DCT pronosupination arcs from two participants with unilateral injury are included in the second application. A canonical joint was created using a multi-level, multi-object statistical shape model, on which the articular surface of interest was defined. Participant-specific joint surfaces were predicted from the canonical template via non-linear morphing. SPM regression and generalized linear models were used to explore relationships between wrist position, injury status, and interosseous proximities. The analysis captured position-related differences in interosseous proximities, demonstrating the application of this pipeline to 3DCT- and 4DCT-derived arthrokinematics. As hypothesized, increased pronation was associated with increased proximities at the sigmoid notch volar margin. The proposed pipeline can be applied to other joints, functional tasks, experimental conditions, or injury states to quantify how arthrokinematics are related to both continuous and categorical variables.