Institutional approval from the Imperial College Healthcare, London, United Kingdom, was retrieved. The hospital’s Picture Archiving and Communication System (PACS) was accessed. In patients with no injured femurs, we randomly selected ten consecutive lower extremities CT images performed for major trauma victims from January to December 2018.
Contrast-enhanced CTA scans were performed using a 256-slice Philips Brilliance CT scanner (Koninklijke Philips N.V., Amsterdam, The Netherlands). Gantry was AirGlide, Aperture 700 mm, Focus-isocenter distance 570 mm, and Focus-detector distance 1040 mm. A rotation time of 0.27 s, and Collimation of 2 × 128 × 0.625 mm was used. A field of view (FOV) 200–500 mm and matrix 512 was used. As contrast medium, 70-mL volumes of Omnipaque (General Electric Healthcare, Chicago, IL, USA) were administered intravenously. The iDose4 Premium Package Filter was used. The average tube voltage used was 100 kV, tube current 89–134 mAs, and Dose 520–920 mGy cm.
Images were downloaded as DICOM files, and all files were anonymized, coded, and transferred to a research server. Images were reconstructed in 3D using a 3D Trauma package (Sectra, Linköping, Sweden) creating STL files. Right and left femurs were segmented. The left femur was mirrored using available applications in the 3D Trauma package.
Images of the right femur and mirrored images of left femur were saved (Fig. 1). The software uses two parameters to find the optimal segmentation; first, the user indicates what is the pelvis and what is the femur by clicking on their respective surface in the software. Second, the HU values are used in combination with these clicks to find where one bone ends and another starts (i.e., optimal segmentation). Analyses of images were done using CTMA which is a software that very precisely can find the relative movement of an object between two different CT stacks. This is done by first in both CT stacks randomly spreading up to 100,000 measurement points on the surface of the object of interest. Thereafter, the software rotates and translates the object in the second CT stack to match that in the first CT stack as closely as possible (Fig. 2). This is done by minimizing the distance between the two groups of points. Since the used surfaces are much larger than any artifact areas, this means that artifacts have limited impact on the matching process. The process is done first for a reference object with which the frame of reference is created. Thereafter, the movement of the object of interest is measured in the same way. The process has been previously described in greater detail [1].
Based on our previous experience using this software, 10,000 points with a mean distance difference between meshes of 0.5 mm or less was chosen [1]. No smoothing was used in the CTMA software.
The proximal part of the right femurs including the head of the femur, the greater trochanter, and the lesser trochanter of each STL created 3D volume were merged with the mirrored contralateral side (Fig. 2). These merged images were saved as static, or nonmoving parts, and were used as reference volumes. Furthermore, the distal part of the femurs including both condyles, the inter-condylar sulcus, and the supra-condylar area were merged.
The CTMA package offers translational and rotational changes in three different Euler axes (X, Y, and Z) [7, 8]. These axes and the rotations were defined as per DICOM standard; axis X from the patient’s left to the right, axis Y from the patient´s front to back, and axis Z from the patient’s feet to head. Positive rotation was defined as clockwise when looking along the positive axis direction. Translational changes were reported either for the entire volume of an object based on Centre Of Mass (COM) of the 10,000 points spread on the surface. This COM was similar but not identical to the mathematical centre of the geometric volume on which the points were spread out. Rotation was reported for the entire geometrical volume.
Statistics: accuracy was analyzed as per root-mean-square error (RMSE) with mean, median, and 95% confidence interval (CI) of the mean [10]. Shapiro–Wilk and Kolmogorov–Smirnov tests were used to test the distribution of normality. Statistical analysis was performed using IBM SPSS Statistics version 25 for Windows. A p value < 0.05 was considered statistically significant.