论文标题
通过深度学习，近距离恢复层析成像的逆问题
Near-Exact Recovery for Tomographic Inverse Problems via Deep Learning
论文作者
论文摘要
这项工作与科学机器学习中的以下基本问题有关：基于深度学习的方法是否可以解决无噪声逆问题来近乎完美的准确性？首次提供了积极的证据，重点是原型计算机断层扫描（CT）设置。我们证明，迭代的端到端网络方案可以实现接近数值精度的重建，与经典的压缩传感策略相当。我们的结果是基于我们对最近AAPM DL-SPARSE-VIEW CT挑战的获胜提交的基础。它的目标是确定用数据驱动技术解决稀疏视图CT反面问题的最先进。挑战设置的特定困难是，参与者的精确前向模型仍然未知。因此，我们方法的关键特征是最初在数据驱动的校准步骤中估算未知的粉丝几何形状。除了对我们的方法的深入分析外，我们还证明了其在开放式现实世界数据集Lodopab CT上的最先进性能。
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.
