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Dynamic Pore-scale Reservoir-condition Imaging of Reaction in Carbonates Using Synchrotron Fast Tomography

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Published on: February 21, 2017

Deep learning based classification of dynamic processes in time-resolved X-ray tomographic microscopy.

Minna Bührer^1,2, Hong Xu³, Allard A Hendriksen⁴

¹Swiss Light Source, Paul Scherrer Institut, Forschungsstrasse 111, 5232, Villigen, Aargau, Switzerland.

Scientific Reports

|December 18, 2021

Summary

This summary is machine-generated.

This study introduces a fast pipeline for analyzing dynamic 3D X-ray tomographic microscopy data. The method uses machine learning to automatically extract features from low signal-to-noise images, speeding up analysis significantly.

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Correlative Microscopy for 3D Structural Analysis of Dynamic Interactions

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Published on: June 24, 2013

Area of Science:

Materials Science
Imaging Science
Computational Science

Background:

Time-resolved X-ray tomographic microscopy enables 3D dynamic process investigation.
Limited signal-to-noise ratio and computational costs hinder quantitative analysis and data processing.
Current automatic reconstruction methods are computationally intensive.

Purpose of the Study:

To develop a computationally efficient pipeline for reconstructing and classifying dynamic features from X-ray tomographic microscopy data.
To overcome the limitations of low signal-to-noise ratios and manual post-processing.
To enable high-throughput analysis of dynamic processes.

Main Methods:

A novel pipeline (SIRT-FBP-MS-D-DIFF) combining algebraic filter approximation and machine learning.
Reconstruction using filtered back-projection with an algebraic filter for efficiency.
Post-processing with a trained convolutional neural network for automatic feature extraction.

Main Results:

The pipeline significantly reduces computational time for reconstruction and classification.
Successfully demonstrated on dynamic fuel cell datasets for training and testing.
Enables automatic processing of hundreds of datasets on a single GPU in one day.

Conclusions:

The proposed pipeline offers a highly efficient and automatic solution for analyzing time-resolved X-ray tomographic microscopy data.
It overcomes previous limitations related to data quality and computational demands.
Facilitates broader application and exploration of dynamic processes in various scientific fields.