Exploring Large Quantities of Secondary Data from High-Resolution Synchrotron X-ray Computed Tomography Scans Using AccuStripes

TL;DR

This paper introduces AccuStripes, a visualization technique for effectively analyzing large secondary datasets from high-resolution synchrotron X-ray CT scans, enabling detailed insights into particle distributions and characteristics.

Contribution

The work presents a novel visualization method, AccuStripes, for intuitive analysis of secondary data from 3D scans, supporting adaptive binning and interactive visual representation.

Findings

AccuStripes effectively visualizes complex particle distribution data.

The method provides detailed insights into particle shape and homogeneity.

It is demonstrated on a large dataset with over 20 million particles.

Abstract

The analysis of secondary quantitative data extracted from high-resolution synchrotron X-ray computed tomography scans represents a significant challenge for users. While a number of methods have been introduced for processing large three-dimensional images in order to generate secondary data, there are only a few techniques available for simple and intuitive visualization of such data in their entirety. This work employs the AccuStripes visualization technique for that purpose, which enables the visual analysis of secondary data represented by an ensemble of univariate distributions. It supports different schemes for adaptive histogram binnings in combination with several ways of rendering aggregated data and it allows the interactive selection of optimal visual representations depending on the data and the use case. We demonstrate the usability of AccuStripes on a high-resolution synchrotron scan of a particle-reinforced metal matrix composite sample, containing more than 20 million particles. Through AccuStripes, detailed insights are facilitated into distributions of derived particle characteristics of the entire sample. Furthermore, research questions such as how the overall shape of the particles is or how homogeneously they are distributed across the sample can be answered.