Model-based learned iterative reconstruction methods have recently been shown to outperform classical reconstruction algorithms. Applicability of these methods to large scale inverse problems is however limited by the available memory for training and extensive training times, the latter due to computationally expensive forward models. As a possible solution to these restrictions we propose a multi-scale learned iterative reconstruction scheme that computes iterates on discretisations of increasing resolution. This procedure does not only reduce memory requirements, it also considerably speeds up reconstruction and training times, but most importantly is scalable to large scale inverse problems with non-trivial forward operators, such as those that arise in many 3D tomographic applications. In particular, we propose a hybrid network that combines the multiscale iterative approach with a particularly expressive network architecture which in combination exhibits excellent scalability in 3D. Applicability of the algorithm is demonstrated for 3D cone beam computed tomography from real measurement data of an organic phantom. Additionally, we examine scalability and reconstruction quality in comparison to established learned reconstruction methods in two dimensions for low dose computed tomography on human phantoms.

中文翻译：

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多尺度学习迭代重建。

最近已显示出基于模型的学习型迭代重建方法优于传统的重建算法。但是，这些方法在大规模逆问题中的适用性受到训练可用的内存和大量训练时间的限制，后者是由于计算量大的正向模型。作为对这些限制的一种可能的解决方案，我们提出了一种多尺度的学习型迭代重构方案，该方案可对分辨率提高的离散化进行迭代计算。此过程不仅减少了内存需求，还大大加快了重建和训练时间，而且最重要的是可扩展到具有非平凡前向算子（例如在许多3D层析成像应用程序中出现的那些算子）的大规模逆问题。特别是，我们提出了一种混合网络，该网络将多尺度迭代方法与特别具有表现力的网络体系结构相结合，该体系结构在3D中具有出色的可扩展性。从有机体模的真实测量数据证明了该算法对3D锥形束计算机断层扫描的适用性。此外，与人类幻影上低剂量计算机断层扫描的二维已建立学习重建方法相比，我们研究了可伸缩性和重建质量。