Dose-efficient Automatic Differentiation for Ptychographic Reconstruction

Ptychography, as a powerful lensless imaging method, has become a popular member of the coherent diffractive imaging family over decades of development. Utilizing low-dose X-rays and/or fast scans plays a critical role 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 (DADP) at low-photon statistics. This DADP framework shows superior performance for ptychographic reconstruction, allowing flexible switching between error metrics based on various forward models while effectively suppressing 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.