For wrist complaints related to motion, a 2-D radiograph or CT scan of the static wrist may not always be considered diagnostic. 3-D motion imaging, i.e., multiple 3DCT scans in time (4DCT), enables quantifying carpal motion and comparing motion patterns of the affected wrist with those of the healthy contralateral side. The accuracy and precision of the method, however, is limited by noise and motion artifacts. Although, the technique is considered promising in existing literature, the accuracy and precision of carpal motion analysis has never been investigated systematically. In this paper, we introduce and evaluate a semi-automatic segmentation- and registration-based method for 3-D carpal motion analysis. We investigate the accuracy and precision of the method, and its dependency on motion and scan parameters (angular velocity, dose, gantry revolution angle for image reconstruction, and scanner type) using a wrist phantom. During standstill the positioning error was ≤ 0.23 mm and ≤ 0.78°. A partial gantry revolution for 3-D reconstruction introduced image deformation, contributing to a positioning error of approx. 0.8 mm. This error increased with reduced dose, and with increasing angular velocity of the wrist phantom. In cases where the phantom was rotating about an axis parallel to the rotation axis of the gantry, and in a direction opposite to the gantry, the positioning error increased, probably because of the apparent increase in angular velocity with respect to the gantry. Slow carpal motion 4DCT analysis is feasible using a regular CT scanner. A partial gantry revolution angle for 3-D reconstruction may introduce image deformation, which decreases the accuracy of carpal motion analysis. Knowing the positioning error in 4DCT imaging with the proposed method is considered valuable when investigating wrist injury since it enables discrimination of actual motion from apparent motion caused by methodological error.

Direct imaging of ligament damage in the wrist remains a challenge. Still, such damage can be assessed indirectly through the analysis of changes in wrist pose and motion pattern. For this purpose we built a statistical reference model that describes healthy motion patterns. We show that such a model can also be used to detect and quantify pathologies. A model that only describes the global translations and rotations of the carpal bones is insufficiently accurate due to size and shape variations of the bones. We present a local statistical motion model that minimizes the influence of size and shape differences by analyzing the coordinate differences of pairs of points on adjacent bone surfaces. These differences are determined in a set of 14 healthy example wrists imaged in a range of poses by means of 4D-RX imaging. The distribution of the differences as a function of the pose form the local statistical motion model (LSMM). Translations of 2 mm and rotations of 20° with respect to the healthy example wrists are detected as outliers in the point pair distributions. An evaluation involving wrists with a damaged ligament between scaphoid and lunate shows that not only joint space widenings can be detected, but also shifts of congruent bone surfaces. The LSMM is also used to perform a virtual reconstruction of the most likely healthy wrist after a simulated perturbation of bones. The reconstruction precision is shown to be about 1 mm. Therefore, the presented 4D statistical model of wrist bone movement may become a valuable clinical tool for diagnosis and surgical planning.

Comparing wrist shapes of different individuals requires alignment of these wrists into the same pose. Unconstrained registration of the carpal bones results in anatomically nonfeasible wrists. In this paper, we propose to constrain the registration using the shapes of adjacent bones, by keeping the width of the gap between adjacent bones constant. The registration is formulated as an optimization involving two terms. One term aligns the wrist bones by minimizing the distances between corresponding bone surfaces. The second term constrains the registration by minimizing the distances between adjacent sliding surfaces. The registration is based on the Iterative Closest Point algorithm. All bones are registered concurrently so that no bias is introduced towards any of the bones. The proposed registration method delivers anatomically correct configurations of the bones. The registration errors are in the order of the voxel size of the acquired CT data (0.3 x 0.3 x 0.3 mm(3)). The standard deviation in the widths of gaps between adjacent bones is in the order of 10% with an insignificant bias. This is a large improvement over the standard deviations of 30%-80% encountered in unconstrained registration. The value of this method is its capability of accurately registering joints in varying poses resulting in physiological joint configurations.