Related Experiment Video
Updated: Sep 9, 2025
Four-Dimensional CT Analysis Using Sequential 3D-3D Registration
Published on: November 23, 2019
Automatic analysis of carpal angles using dynamic CT imaging: Reference values in healthy wrists and assessment of
Maranda Haenen1, Erin Teule2, Stefan Hummelink3
1Department of Plastic and Reconstructive Surgery, Radboud University Medical Centre, Nijmegen, the Netherlands; Department of Plastic and Reconstructive Surgery, Jeroen Bosch Hospital, 's-Hertogenbosch, the Netherlands; Department of Medical Imaging, Radboud University Medical Centre, Nijmegen, the Netherlands.
Introduction:
Dynamic CT imaging is a promising modality for evaluating wrist pathologies like scapholunate ligament (SL) injuries. The primary objective of this study is to extract carpal angles from dynamic CT datasets using an automated motion analysis algorithm to provide reference values for healthy wrist motion. Secondly, the feasibility of this automatic method to detect SL ligament pathology was evaluated.
Methods:
Dynamic CT scans of healthy wrists and wrists with arthroscopically-confirmed complete SL injuries (Geissler IV) were analysed. Each scan consisted of one static image and two dynamic imaging sequences: wrist radial-ulnar deviation (RUD) and flexion-extension (FE). Bones were automatically segmented, and the radioscaphoid (RSA), scapholunate (SLA), capitolunate (CLA), and radiolunate (RLA) angles were automatically determined in each wrist position. A linear mixed model was applied to compare carpal angles between the two groups (p < 0.05).
Results:
A total of 84 wrists scans were analysed, of which 73 healthy and 11 injured. Reference values for healthy wrists were provided, with an average and maximum 95% CI width during all movements of 5°and 7°, respectively. Feasibility analysis showed that the SLA, CLA, and RLA were different between the healthy and injured groups during all movements. No differences were found for the RSA.
Conclusion:
Reference values of the moving wrist of healthy participants were automatically extracted. Furthermore, our results suggest that the RLA, CLA, and SLA may be useful parameters for distinguishing wrists with complete SL injuries from healthy ones, making the automatic approach feasible.
More Related Videos
06:40Screening of Axonal Degeneration in Carpal Tunnel Syndrome Using Ultrasonography and Nerve Conduction Studies
Published on: January 11, 2019
10:07Measurement of Dynamic Scapular Kinematics Using an Acromion Marker Cluster to Minimize Skin Movement Artifact
Published on: February 10, 2015
