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<p>Electron ptychography is a rapidly growing diffractive imaging technique providing superior image contrast, high resolution, efficient dose usage and versatile imaging conditions. Originally coming from the physical sciences, it has also become a method of interest in the life sciences. Regardless of the scientific field, the successful ptychographic reconstruction relies on various combinations of adjustable experimental parameters, along with the choice of the reconstruction algorithm. In this work we present <i>ptychoScopy</i>, a Python-based tool developed to simplify the selection of these parameters and aid successful experimental design. Using a <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="41598_2025_9871_Article_IEq1.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="42" /> </InlineMediaObject> <EquationSource Format="TEX">\(\hbox {SmB}_{6}\)</EquationSource> </InlineEquation> sample, we show the influence of key parameters such as probe convergence angle, defocus and electron dose on the reconstruction quality using direct and iterative reconstruction algorithms. We investigate the influence of real and reciprocal space sampling and their influence on the speed and quality of ptychographic reconstructions. PtychoScopy simplifies experimental design and guides the researchers across diverse scientific fields in setting up successful ptychographic experiments.</p>

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Ptychoscopy: a user friendly experimental design tool for ptychography

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月10日

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  • Radim Skoupy,
  • Elisabeth Müller,
  • Timothy J. Pennycook,
  • Manuel Guizar-Sicairos,
  • Emiliana Fabbri,
  • Emiliya Poghosyan

摘要

Electron ptychography is a rapidly growing diffractive imaging technique providing superior image contrast, high resolution, efficient dose usage and versatile imaging conditions. Originally coming from the physical sciences, it has also become a method of interest in the life sciences. Regardless of the scientific field, the successful ptychographic reconstruction relies on various combinations of adjustable experimental parameters, along with the choice of the reconstruction algorithm. In this work we present ptychoScopy, a Python-based tool developed to simplify the selection of these parameters and aid successful experimental design. Using a \(\hbox {SmB}_{6}\) sample, we show the influence of key parameters such as probe convergence angle, defocus and electron dose on the reconstruction quality using direct and iterative reconstruction algorithms. We investigate the influence of real and reciprocal space sampling and their influence on the speed and quality of ptychographic reconstructions. PtychoScopy simplifies experimental design and guides the researchers across diverse scientific fields in setting up successful ptychographic experiments.