Estimation of 3D rotation of femur in 2D hip radiographs.

Femoral radiographs are affected by the degree of rotation of the femur with respect to the plane of projection. We aimed to determine the 3D rotation of the proximal femur in 2D radiographs. A 3D Statistical Appearance Model (SAM), which was built from CT images of cadaver proximal femurs (n=33) was randomly sampled to form a training set of 500 bones. Nineteen clinical CT images were collected for testing. All CT images were rotated to ±20° in 2° division around the shaft axis, ±10° around medial-lateral axis, and by simultaneous rotation of both axes (±16° and ±8° around shaft and medial-lateral axes). In each orientation, a 2D projection was recorded for generating a 2D SAM. The outcome parameters of the 2D SAM were used as input for a linear regression model and an artificial neural network to predict the rotation. The artificial neural network estimated the rotation more accurately than the linear regression. For artificial neural networks the mean errors were 4.0° and 2.0° around the shaft and medial-lateral axes, respectively. For an individual radiograph, the confidence interval of estimation was still relatively large. However, this method has high potential to differentiate the amount of rotations in two image sets.