Near-Exact Recovery for Tomographic Inverse Problems via Deep Learning

• URL: http://arxiv.org/abs/2206.07050v1
• Date: Tue, 14 Jun 2022 10:06:41 GMT
• Title: Near-Exact Recovery for Tomographic Inverse Problems via Deep Learning
• Authors: Martin Genzel and Ingo G\"uhring and Jan Macdonald and Maximilian M\"arz
• Abstract summary: We show that an iterative end-to-end network scheme enables reconstructions close to numerical precision. We also demonstrate our state-of-the-art performance on the open-access real-world dataset LoDoPaB CT.
• Score: 3.441021278275805
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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