用于叠层成像重建的剂量高效自动微分方法
Dose-efficient Automatic Differentiation for Ptychographic Reconstruction
AI总结：
针对低剂量、低光子统计和低重叠率叠层成像的相位恢复难题，提出无需倒易空间约束的DAP自动微分框架，以多种前向模型抑制重建伪影并经模拟与实测数据验证。
摘要：
叠层成像（Ptychography）作为一种强大的无透镜成像方法，经过数十年发展，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/或快速扫描的能力为叠层成像测量提供了显著优势（例如在测量辐射敏感样品时），但也会导致低光子统计特性，使后续相位恢复具有挑战性。在此，我们展示了一种用于低光子统计和低重叠率条件下叠层成像重建的剂量高效自动微分框架（DAP）。由于该DAP框架不需要倒易空间约束，基于多种前向模型的该框架在上述条件下表现出优越性能。它有效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。我们使用模拟数据集和实测数据集验证了该方法的有效性和鲁棒性。
英文摘要：
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.
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