The relationship between the morphological axis and the kinematic axis of the proximal radius

Streekstra

et al. 2022

Preprint

Distal radio-ulnar joint (DRUJ) motion analysis using dynamic CT is gaining popularity. Following scanning and segmentation, 3D bone models are registered to (4D-)CT target frames. Imaging errors like low signal-to-noise ratio (SNR), limited Z-coverage and motion artefacts influence registration, causing misinterpretation of joint motion. This necessitates quantification of the methodological error. A cadaver arm and dynamic phantom were subjected to multiple 4D-CT scans, while varying tube charge-time product and phantom angular velocity, to evaluate the effects of SNR and motion artefacts on registration accuracy and precision. 4D-CT Z-coverage is limited by the scanner. To quantify the effects of different Z-coverages on registration accuracy and precision, 4D-CT was simulated by acquiring multiple spiral 3D-CT scans of the cadaver arm. Z-coverage was varied by clipping the 3D bone models prior to registration. The radius position relative to the ulna was obtained from the segmentation image. Apparent relative displacement seen in the target images is caused by registration errors. Worst-case translations were 0.45-, 0.08- and 1.1 mm for SNR-, Z-coverage- and motion-related errors respectively. Worst-case rotations were 0.41-, 0.13- and 6.0 degrees. This study showed that quantification of the methodological error enables composition of accurate and precise DRUJ motion scanning protocols.

Section: Discussionmentioning

confidence: 99%

Section: Error Parametersmentioning

confidence: 99%

Quantification of the methodological error in kinematic evaluation of the DRUJ using dynamic CT

Streekstra

et al. 2022

Preprint

“…registration errors), and can be affected by SNR, z-coverage or motion. With regard to the latter, the phantom was designed in such a way that the center of rotation at the level of the DRUJ was equal to the center of rotation found in literature regarding DRUJ motion 14,15 . This results in motion artefacts at the edge of the radius, being farther away from the axis of rotation, while limiting motion artefacts at the ulna which is similar to the physiological situation.…”

Section: Quantificationmentioning

confidence: 99%

Quantification of the methodological error in kinematic evaluation of the DRUJ using dynamic CT

Strackee

et al. 2023

Sci Rep

Distal radio-ulnar joint (DRUJ) motion analysis using dynamic CT is gaining popularity. Following scanning and segmentation, 3D bone models are registered to (4D-)CT target frames. Imaging errors like low signal-to-noise ratio (SNR), limited Z-coverage and motion artefacts influence registration, causing misinterpretation of joint motion. This necessitates quantification of the methodological error. A cadaver arm and dynamic phantom were subjected to multiple 4D-CT scans, while varying tube charge-time product and phantom angular velocity, to evaluate the effects of SNR and motion artefacts on registration accuracy and precision. 4D-CT Z-coverage is limited by the scanner. To quantify the effects of different Z-coverages on registration accuracy and precision, 4D-CT was simulated by acquiring multiple spiral 3D-CT scans of the cadaver arm. Z-coverage was varied by clipping the 3D bone models prior to registration. The radius position relative to the ulna was obtained from the segmentation image. Apparent relative displacement seen in the target images is caused by registration errors. Worst-case translations were 0.45, 0.08 and 1.1 mm for SNR-, Z-coverage- and motion-related errors respectively. Worst-case rotations were 0.41, 0.13 and 6.0 degrees. This study showed that quantification of the methodological error enables composition of accurate and precise DRUJ motion scanning protocols.

“…A significant amount of work has been previously done to accurately establish the kinematics of the forearm from three-dimensional (3-D) and four-dimensional (4-D) medical images (Kazama et al., 2016; King et al., 1986; Matsuki et al., 2010; Nakamura et al., 1999; Oki et al., 2019; Shakoor et al., 2019; Tay et al., 2008, 2010). However, these studies have used various imaging techniques, pseudo-dynamic imaging or a limited number of 3-D image frames to visualize and quantify forearm motion.…”

Section: Introductionmentioning

confidence: 99%

Kinematic analysis of forearm rotation using four-dimensional computed tomography

Strijkers

et al. 2022

J Hand Surg Eur Vol

This study aimed to quantify forearm kinematics with a focus on the forearm rotation axis. Ten healthy volunteers were included in the study. One three-dimensional computed tomographic scan and two four-dimensional computed tomographic scans were done in all the arms to capture forearm joint motion. After image processing, the rotation axis and the movement of the radius with respect to various axes were quantified. The rotation axis was calculated using finite helical axis analysis and a circle fitting approach. The mean error of the rotation axis found through circle fitting was 0.2 mm (SD 0.1) distally and 0.1 mm (SD 0.1) proximally, indicating an improvement in precision over the finite helical axis approach. The translations of the radius along the ulnar axis and the forearm rotation axis were 2.6 (SD 0.8) and 0.6 mm (SD 0.9), respectively. The rotation of the radius around the radial axis was 7.2°. The techniques presented provide a detailed description of forearm kinematics.