  Many minimally invasive interventional procedures still rely on 2D
fluoroscopic imaging. Generating a patient-specific 3D model from these X-ray
projection data would allow to improve the procedural workflow, e.g. by
providing assistance functions such as automatic positioning. To accomplish
this, two things are required. First, a statistical human shape model of the
human anatomy and second, a differentiable X-ray renderer. In this work, we
propose a differentiable renderer by deriving the distance travelled by a ray
inside mesh structures to generate a distance map. To demonstrate its
functioning, we use it for simulating X-ray images from human shape models.
Then we show its application by solving the inverse problem, namely
reconstructing 3D models from real 2D fluoroscopy images of the pelvis, which
is an ideal anatomical structure for patient registration. This is accomplished
by an iterative optimization strategy using gradient descent. With the majority
of the pelvis being in the fluoroscopic field of view, we achieve a mean
Hausdorff distance of 30 mm between the reconstructed model and the ground
truth segmentation.
