HoToPy: A toolbox for X-ray holo-tomography in Python

TL;DR

HoToPy is a versatile Python toolbox that facilitates holographic and tomographic X-ray imaging, offering advanced phase retrieval algorithms, image processing tools, and simulation capabilities for research and development.

Contribution

It introduces a comprehensive, modular Python toolbox with novel phase retrieval algorithms and auxiliary functions for X-ray holography and tomography, enhancing flexibility and usability.

Findings

Demonstrated on a catalytic nanoparticle at PETRA III

Showcased modular design for algorithm development

Enabled integration with other imaging pipelines

Abstract

We present a Python toolbox for holographic and tomographic X-ray imaging. It comprises a collection of phase retrieval algorithms for the deeply holographic and direct contrast imaging regimes, including non-linear approaches and extended choices of regularization, constraint sets, and optimizers, all implemented with a unified and intuitive interface. Moreover, it features auxiliary functions for (tomographic) alignment, image processing, and simulation of imaging experiments. The capability of the toolbox is illustrated by the example of a catalytic nanoparticle, imaged in the deeply holographic regime at the 'GINIX' instrument of the P10 beamline at the PETRA III storage ring (DESY, Hamburg). Due to its modular design, the toolbox can be used for algorithmic development and benchmarking in a lean and flexible manner, or be interfaced and integrated in the reconstruction pipeline of other synchrotron or XFEL instruments for phase imaging based on propagation.