Deep learning-based methods have revolutionized the field of imaging inverse problems, yielding state-of-the-art performance across various imaging domains. The best performing networks incorporate the imaging operator within the network architecture, typically in the form of deep unrolling. However, in large-scale problems, such as 3D imaging, most existing methods fail to incorporate the operator in the architecture due to the prohibitive amount of memory required by global forward operators, which hinder typical patching strategies. In this work, we present a domain partitioning strategy and normal operator approximations that enable the training of end-to-end reconstruction models incorporating forward operators of arbitrarily large problems into their architecture. The proposed method achieves state-of-the-art performance on 3D X-ray cone-beam tomography and 3D multi-coil accelerated MRI, while requiring only a single GPU for both training and inference.

翻译：基于深度学习的方法已彻底改变了成像逆问题领域，在各种成像领域取得了最先进的性能。性能最佳的网络通常将成像算子融入网络架构中，其常见形式为深度展开网络。然而，在大规模问题（如三维成像）中，由于全局前向算子所需内存量过大且阻碍了典型的分块策略，现有方法大多无法将算子整合到架构中。本研究提出了一种领域分割策略和正规算子近似方法，使得能够训练端到端重建模型，并将任意大规模问题的前向算子纳入其架构。所提出的方法在三维X射线锥束断层扫描和三维多线圈加速磁共振成像中实现了最先进的性能，且训练和推断仅需单个GPU即可完成。