Ptychography is an advanced technique whose latest developments have allowed for exceptionally high resolution in X-ray microscopy, as well as new setups that can even be lensless; the spatial resolution has already reached < 5 nm in synchrotron beamlines. Still there are certain demanding requirements such as beam coherence and strong involvement of computational methods that make it a rather difficult technique to implement. In this paper we introduce a computational work-flow aiming at refining the individual probe positions of a ptychography scan. Indeed, the precision of those positions is of high importance as it impacts the reconstruction. Special setups using precise sample stages with nanopositioning and advanced interferometers are important; however, positioning errors still remain a problem for many operating laboratories. This paper examines the Structural Similarity Index as a suitable metric for evaluating the registration in alignment of individually reconstructed probes. Eventually it suggests the use of Machine Learning techniques as future direction in probe alignment. It also presents a new software tool for precise manual alignment and visualization. All the software developed during this research project is provided to the scientific community as open source.

Refining scan positions in Ptychography through error minimisation and potential application of Machine Learning

Guzzi, F.
;
Carrato, S.
2018

Abstract

Ptychography is an advanced technique whose latest developments have allowed for exceptionally high resolution in X-ray microscopy, as well as new setups that can even be lensless; the spatial resolution has already reached < 5 nm in synchrotron beamlines. Still there are certain demanding requirements such as beam coherence and strong involvement of computational methods that make it a rather difficult technique to implement. In this paper we introduce a computational work-flow aiming at refining the individual probe positions of a ptychography scan. Indeed, the precision of those positions is of high importance as it impacts the reconstruction. Special setups using precise sample stages with nanopositioning and advanced interferometers are important; however, positioning errors still remain a problem for many operating laboratories. This paper examines the Structural Similarity Index as a suitable metric for evaluating the registration in alignment of individually reconstructed probes. Eventually it suggests the use of Machine Learning techniques as future direction in probe alignment. It also presents a new software tool for precise manual alignment and visualization. All the software developed during this research project is provided to the scientific community as open source.
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http://iopscience.iop.org/article/10.1088/1748-0221/13/06/C06002/pdf
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11368/2927612
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