  We propose a deep learning method for 3D volumetric reconstruction in
low-dose helical cone-beam computed tomography. Prior machine learning
approaches require reference reconstructions computed by another algorithm for
training. In contrast, we train our model in a fully self-supervised manner
using only noisy 2D X-ray data. This is enabled by incorporating a fast
differentiable CT simulator in the training loop. As we do not rely on
reference reconstructions, the fidelity of our results is not limited by their
potential shortcomings. We evaluate our method on real helical cone-beam
projections and simulated phantoms. Our results show significantly higher
visual fidelity and better PSNR over techniques that rely on existing
reconstructions. When applied to full-dose data, our method produces
high-quality results orders of magnitude faster than iterative techniques.
