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 multi-scale 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

基于模型的学习迭代重建方法最近已被证明优于经典重建算法。然而，这些方法对大规模逆问题的适用性受到用于训练的可用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。作为对这些限制的可能解决方案，我们提出了一种多尺度学习迭代重建方案，该方案计算在分辨率增加的离散化上的迭代。此过程不仅减少了内存需求，还显着加快了重建和训练时间，但最重要的是可扩展到具有非平凡正向算子的大规模逆问题，例如在许多 3D 断层扫描应用中出现的问题。特别是，我们提出了一种混合网络，它将多尺度迭代方法与特别富有表现力的网络架构相结合，在 3D 中表现出出色的可扩展性。从有机体模的真实测量数据中证明了该算法的适用性，用于 3D 锥形束计算机断层扫描。此外，我们与已建立的二维人体模型低剂量计算机断层扫描的学习重建方法相比，检查了可扩展性和重建质量。