# Near-Exact Recovery for Tomographic Inverse Problems via Deep Learning Source: [Near-Exact Recovery for Tomographic Inverse Problems via Deep Learning]() Collection index: [llms.txt]() Data updated: 2025-10-28T13:56:54.400Z Authors: Martin Genzel, Ingo G\\"uhring, Jan Macdonald, Maximilian M\\"arz Published: 2022-06-14T10:06:41.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [CT Image Reconstruction on LoDoPaB-CT challenge](): CT Image Reconstruction; LoDoPaB-CT challenge; Mean Position: 2. ## Original sources and implementation [Original paper]() [PDF]()