Ptychography, as a powerful lensless imaging method, has become a popular member of the coherent diffractive imaging family over decades of development. The ability to utilize low-dose X-rays and/or fast scans offers a big advantage in a ptychographic measurement (for example, when measuring radiation-sensitive samples), but results in low-photon statistics, making the subsequent phase retrieval challenging. Here, we demonstrate a dose-efficient automatic differentiation framework for ptychographic reconstruction (DAP) at low-photon statistics and low overlap ratio. As no reciprocal space constraint is required in this DAP framework, the framework, based on various forward models, shows superior performance under these conditions. It effectively suppresses potential artifacts in the reconstructed images, especially for the inherent periodic artifact in a raster scan. We validate the effectiveness and robustness of this method using both simulated and measured datasets.

中文翻译：

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用于叠层重建的剂量有效自动微分


叠层成像技术作为一种强大的无透镜成像方法，经过数十年的发展，已成为相干衍射成像家族中的热门成员。利用低剂量 X 射线和/或快速扫描的能力在叠层记录测量中（例如，测量辐射敏感样品时）提供了很大的优势，但会导致低光子统计，从而使后续的相位恢复具有挑战性。在这里，我们展示了一种在低光子统计和低重叠率下进行叠层记录重建（DAP）的剂量有效的自动微分框架。由于该 DAP 框架不需要相互空间约束，因此该框架基于各种前向模型，在这些条件下表现出优越的性能。它有效地抑制了重建图像中的潜在伪影，尤其是光栅扫描中固有的周期性伪影。我们使用模拟和测量数据集验证该方法的有效性和鲁棒性。