Unveiling water dynamics in fuel cells from time-resolved tomographic microscopy data

Minna Bührer,Federica Marone,Jan Sijbers,Jens Eller,Marco Stampanoni,Hong Xu

doi:10.1038/s41598-020-73036-w

Abstract

X-ray dynamic tomographic microscopy offers new opportunities in the volumetric investigation of dynamic processes. Due to data complexity and their sheer amount, extraction of comprehensive quantitative information remains challenging due to the intensive manual interaction required. Particularly for dynamic investigations, these intensive manual requirements significantly extend the total data post-processing time, limiting possible dynamic analysis realistically to a few samples and time steps, hindering full exploitation of the new capabilities offered at dedicated time-resolved X-ray tomographic stations. In this paper, a fully automatized iterative tomographic reconstruction pipeline (rSIRT-PWC-DIFF) designed to reconstruct and segment dynamic processes within a static matrix is presented. The proposed algorithm includes automatic dynamic feature separation through difference sinograms, a virtual sinogram step for interior tomography datasets, time-regularization extended to small sub-regions for increased robustness and an automatic stopping criterion. We demonstrate the advantages of our approach on dynamic fuel cell data, for which the current data post-processing pipeline heavily relies on manual labor. The proposed approach reduces the post-processing time by at least a factor of 4 on limited computational resources. Full independence from manual interaction additionally allows straightforward up-scaling to efficiently process larger data, extensively boosting the possibilities in future dynamic X-ray tomographic investigations.

Highlights

X-ray dynamic tomographic microscopy offers new opportunities in the volumetric investigation of dynamic processes
We propose a rSIRT-piecewise constant functions (PWC)-DIFF algorithm extended for interior tomography datasets, designed to reconstruct and segmented noisy datasets in a highly automatized manner enabling efficient scalability to large data volumes
To apply the proposed rSIRT-PWC-DIFF algorithm, we propose the following protocol described in Fig. 2: 1. Align and subtract sinograms to extract the dynamic changes (“Sinogram alignment” section)

Summary

Introduction

X-ray dynamic tomographic microscopy offers new opportunities in the volumetric investigation of dynamic processes. For dynamic investigations, these intensive manual requirements significantly extend the total data post-processing time, limiting possible dynamic analysis realistically to a few samples and time steps, hindering full exploitation of the new capabilities offered at dedicated time-resolved X-ray tomographic stations. Standard analytical reconstruction algorithms, such as filtered back-projection (FBP)[8] and Gridrec[9,10], consider each time frame of a dynamic sequence independently without exploiting the additional information across the time sequence In this way, they have limited ability to cope with undersampled and noisy datasets, leading to challenges in further data analysis and post-processing and so, increasing the necessary data analysis time significantly. The resulting images are strongly denoised and superior in quality compared to standard filtered back-projection

Methods

Results

Conclusion