Demystifying electron ptychography with the PtychoScopy tool

Electron ptychography is a powerful computational imaging method offering exceptional resolution and contrast and emerging as a valuable tool in biological imaging. However, setting up successful experiment and reconstruction requires careful tuning of multiple parameters and selecting appropriate reconstruction algorithms. In this work, we present ptychoScopy, a Python-based tool developed to simplify the choice of these parameters and aid successful experimental design. Using an SmB₆ sample, we demonstrate the impact of variables such as probe convergence angle, defocus, dose, and sampling strategies on reconstruction quality. By visualizing trade-offs and optimizing design choices, ptychoScopy supports researchers from life sciences and beyond in leveraging this advanced imaging technique.