Background: Dynamic medical imaging can determine the cause of rotational impairment in the forearm. However, it has drawbacks depending on the image modality used, related to radiation dose, the need for specialized equipment, and the labor intensity involved in the analysis. Because the forearm rotation axis is static, we hypothesize that an axis based on bony landmarks is comparable to an axis calculated from dynamic imaging. Methods: Eight post-mortem human forearms were scanned using CT in seven rotational positions from maximum supination to maximum pronation. Three rotation axes were calculated: the landmark, average helical, and circle fit axes. The primary outcome is the difference between the axes expressed as the angle and the minimal distance between them. Secondary outcomes are the orientation errors when modeling pose using the three found axes. Findings: The mean difference between the landmark and average helical axes was 0.38 degrees and 0.51 mm. The mean difference between the landmark and circle fit axes was 0.40 degrees and 0.51 mm. When modeling the pose of the radius using one of the three axes, the difference between the modeled radius and the scanned radius was in each direction below 2 mm and 1 degree. Interpretation: The rotation axis of the forearm can be accurately calculated using automatically placed bony landmarks. These findings indicate that determining the forearm rotation axis does not require multiple static images or dynamic imaging. This knowledge should be applied to clinical data to assess its applicability in practice.

Objective: Analyzing population trends of bone shape variation can provide valuable insights into growth processes. This review aims to overview state-of-the-art spatiotemporal statistical shape modeling techniques, emphasizing their application to 3D skeletal structures during healthy growth. Methods: We searched PubMed and Scopus for articles on statistical shape modeling using a pediatric spatiotemporal dataset of 3D healthy bone models. Dataset characteristics and details on the shape models' development, analyses, and potential clinical use were extracted. Results: Fourteen studies were found eligible, modeling one or multiple lower limb bones, the mandible, the skull, and vertebrae. The majority applied Principal Component Analysis on point distribution models to create a statistical shape model. Shape variation was analyzed based on shape modes, representing a specific shape change as a part of the overall variance. Unscaled models resulted in a more compact statistical shape model than scaled models. The latter represented more subtle shape variations due to the absence of size differences between the bone models. Four studies reported a significant correlation between the first shape mode and age, indicating a relationship between that type of shape variation and growth. Three studies reconstructed 3D models using prediction features of statistical shape modeling. Measuring difference between predicted and actual anatomy resulted in Root Mean Squared Errors below 3 mm. Conclusion: Spatiotemporal statistical shape modeling provides insight into modes of shape variation during growth. Such a model can be used to find predictive factors, like age or sex, and deploy these characteristics to predict someone's bone geometry.

Background: For bone morphology and biomechanics analysis, landmarks are essential to define position, orientation, and shape. These landmarks define bone and joint coordinate systems and are widely used in these research fields. Currently, no method is known for automatically identifying landmarks on virtual 3D bone models of the radius and ulna. This paper proposes a knowledge-based method for locating landmarks and calculating a coordinate system for the radius, ulna, and combined forearm bones, which is essential for measuring forearm function. This method does not rely on pre-labeled data. Validation: The algorithm is validated by comparing the landmarks placed by the algorithm with the mean position of landmarks placed by a group of experts on cadaveric specimens regarding distance and orientation. Results: The median Euclidean distance differences between all the automated and reference landmarks range from 0.4 to 1.8 millimeters. The median angular differences of the coordinate system of the radius and ulna range from -1.4 to 0.6 degrees. The forearm coordinate system's median errors range from -0.2 to 2.0 degrees. The median error in calculating the rotational position of the radius relative to the ulna is 1.8 degrees. Conclusion: The automatic method's applicability depends on the use context and desired accuracy. However, the current method is a validated first step in the automatic analysis of the three-dimensional forearm anatomy.