Compared with walking, the relatively short duration of ground contact and relatively long length of strides in running seem to blunt the effect of high peak joint loads, such that the PUD loads are no higher than that in walking. Waveform features other than or in addition to the peak value should be considered when studying joint loading and injuries.
Accurately assessing the dynamic kinematics of the skeletal wrist could advance our understanding of the normal and pathological wrist. Biplane videoradiography (BVR) has allowed investigators to study dynamic activities in the knee, hip, and shoulder joint; however, currently, BVR has not been utilized for the wrist joint because of the challenges associated with imaging multiple overlapping bones. Therefore, our aim was to develop a BVR procedure and to quantify its accuracy for evaluation of wrist kinematics. BVR was performed on six cadaveric forearms for one neutral static and six dynamic tasks, including flexion-extension, radial-ulnar deviation, circumduction, pronation, supination, and hammering. Optical motion capture (OMC) served as the gold standard for assessing accuracy. We propose a feedforward tracking methodology, which uses a combined model of metacarpals (second and third) for initialization of the third metacarpal (MC3). BVR-calculated kinematic parameters were found to be consistent with the OMCcalculated parameters, and the BVR/OMC agreement had submillimeter and sub-degree biases in tracking individual bones as well as the overall joint's rotation and translation. All dynamic tasks (except pronation task) showed a limit of agreement within 1.5° for overall rotation, and within 1.3 mm for overall translations. Pronation task had a 2.1° and 1.4 mm limit of agreement for rotation and translation measurement. The poorest precision was achieved in calculating the pronationsupination angle, and radial-ulnar and volar-dorsal translational components, although they were sub-degree and submillimeter. The methodology described herein may assist those interested in examining the complexities of skeletal wrist function during dynamic tasks.
Osteophytes are associated with later stage osteoarthritis and are most commonly described using semiquantitative radiographic grading systems. A detailed understanding of osteophyte formation is, in part, limited by the ability to quantify bone pathology. Osteophytes can be quantified relative to pre‐osteoarthritic bone, or to the contralateral bone if it is healthy; however, in many cases, neither are available as references. We present a method for computing three‐dimensional (3D) osteophyte models using a library of healthy control bones. An existing data set containing the computed tomography scans of 90 patients with first carpometacarpal osteoarthritis (OA) and 46 healthy subjects were utilized. A healthy bone that best fit each OA subject's bone was determined using a dissimilarity‐excluding Procrustes registration technique (DEP) that minimized the influence of dissimilar features (ie, osteophytes). The osteophyte model was then computed through Boolean subtraction of the reference bone model from the OA bone model. DEP reference bones conformed significantly better to the OA bones (P < .0001) than by finite difference iterative closest point registration (root mean squared distances, 0.33 ± 0.05 and 0.41 ± 0.16 mm, respectively). The effect of library size on dissimilarity measure was investigated by leave‐k‐out cross‐validation randomly reducing k from 46 to 1. A library of n ≥ 31 resulted in less than 10% difference from the theoretical minimum value. The proposed method enables quantification of osteophytes when the disease‐free bone or the healthy contralateral bone is not available for any 3D data set. Quantifying osteophyte formation and growth may aid in understating the associated mechanisms in OA.
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