  This work is concerned with the following fundamental question in scientific
machine learning: Can deep-learning-based methods solve noise-free inverse
problems to near-perfect accuracy? Positive evidence is provided for the first
time, focusing on a prototypical computed tomography (CT) setup. We demonstrate
that an iterative end-to-end network scheme enables reconstructions close to
numerical precision, comparable to classical compressed sensing strategies. Our
results build on our winning submission to the recent AAPM DL-Sparse-View CT
Challenge. Its goal was to identify the state-of-the-art in solving the
sparse-view CT inverse problem with data-driven techniques. A specific
difficulty of the challenge setup was that the precise forward model remained
unknown to the participants. Therefore, a key feature of our approach was to
initially estimate the unknown fanbeam geometry in a data-driven calibration
step. Apart from an in-depth analysis of our methodology, we also demonstrate
its state-of-the-art performance on the open-access real-world dataset LoDoPaB
CT.
