In this contribution, a methodology for particle tracing based on computed tomography and digital image processing is presented. It enables the tracing of particles in opaque structures using laboratory X-ray microcomputed tomography (μCT) systems that are not capable of time-resolved particle tracking. Through the development, it becomes apparent that an X-ray source with a cone beam geometry and the ability to perform fast, dynamic scans is a prerequisite for generating parabolic motion artefacts. Moreover, experimental tests are used to acquire data from simple particle sedimentations as well as from self-developed filter structures based on deep bed filtration. These experiments confirm that the particle position is located at the apex of the motion artefacts. Following the data assessment, multiple options for the particle coordinate extraction are discussed, and strategies thoroughly examined. A combination of random sample consensus (RANSAC) and the least squares method proves to be the most useful for determining the particle position. Besides, the developed methodology is validated using artificially generated data in which the motion artefact parameters of size, spatial orientation, and curvature, as well as noise, are varied. Supplementary, data is analysed manually in order to draw a comparison. In addition, to the presentation and discussion of the application of the methodology, a comparison with an artificial neural network (ANN) and the advantages and disadvantages of both methods are discussed. Finally, a first comparison of an extracted particle trace with a flow simulation through the complex structure is carried out, which shows that the particle trace follows the flow.:Table of Contents
List of Figures ............................................................................................................................. i
List of Tables ............................................................................................................................. vi
List of Formula Symbols ......................................................................................................... vii
List of Abbreviations ................................................................................................................. x
1 Introduction ....................................................................................................................... 1
2 Fundamentals .................................................................................................................... 5
2.1 Methods for Particle Tracking and Tracing .............................................................. 5
2.2 Computed Tomography .......................................................................................... 10
2.2.1 Tomography Design and Functional Principle ................................................ 10
2.2.2 Data Reconstruction ......................................................................................... 15
2.3 Digital Image Processing .......................................................................................... 18
3 Material............................................................................................................................. 30
3.1 Laboratory X-ray Tomography System TomoTU ................................................... 30
3.2 Experimental Setup .................................................................................................. 33
3.3 Choice of Particles and Medium ............................................................................. 34
4 Method development ...................................................................................................... 36
4.1 Characterisation of the Motion Artefacts ............................................................... 38
4.2 Method Consideration ............................................................................................. 45
4.3 Pre-processing .......................................................................................................... 46
4.4 Combination of Random Sample Consensus and Least Squares Method.......... 48
4.5 Multiple Particle Tracing .......................................................................................... 51
4.6 Coordinate Processing ............................................................................................. 53
4.7 Method Validation .................................................................................................... 53
5 Results and Discussion .................................................................................................... 59
5.1 Evaluation experimental data ................................................................................. 59
5.2 Comparison with Computational Fluid Dynamics (CFD) ....................................... 68
5.3 Comparison of Artificial Neural Networks with the Developed Classical Digital Image Processing Approach ............................................................................................... 70
6 Summary, Conclusion and Outlook ............................................................................... 74
7 References ........................................................................................................................ 76 / Die vorliegende Arbeit stellt eine auf Computertomografie und digitaler Bildverarbeitung basierte Methodik für die Partikelverfolgung dar. Diese ermöglicht es, mittels Labor- Microcomputertomografie (μCT) Anlagen, welche nicht dazu in der Lage sind, zeitaufgelöste Partikelverfolgung zu realisieren, Partikel in opaken Strukturen zu verfolgen. Durch die Methodenentwicklung ergibt sich, dass eine Röntgenquelle mit Kegelstrahlgeometrie sowie die Durchführungsmöglichkeit von schnellen, dynamischen Scans Voraussetzungen sind, um parabelförmige Bewegungsartefakte zu erzeugen. Dafür werden durch experimentelle Untersuchungen Daten erzeugt, die sowohl von einfachen Partikelsedimentationen als auch von eigens entwickelten Filterstrukturen, die sich an der Tiefenfiltration orientieren, abgeleitet werden. Diese Experimente bestätigen, dass sich die Partikelposition am Scheitelpunkt der Bewegungsartefakte befindet. Auf Grundlage der ersten Messungen werden verschiedene Möglichkeiten für die Partikelkoordinatenbestimmung diskutiert und Ansätze kritisch betrachtet. Dabei hat sich eine Kombination aus dem Random Sample Consensus (RANSAC) Algorithmus und der Methode der kleinsten Quadrate als am sinnvollsten für die Bestimmung der Partikelposition ergeben. Zudem wird die entwickelte Methodik anhand von künstlich erzeugten Daten validiert, bei welchen die Bewegungsartefakt-Parameter Größe, Raumorientierung und Krümmung sowie Rauschen variiert werden. Zusätzlich werden auch Daten manuell ausgewertet, um einen Vergleich ziehen zu können. Neben der Präsentation und Diskussion der Anwendung der Methodik wird außerdem ein Vergleich zu künstlichen neuronalen Netzen (KNNs) und die Vor- und Nachteile beider Methoden diskutiert. Abschließend wird ein erster Vergleich einer extrahierten Partikelspur mit einer Strömungssimulation durch die komplexe Struktur durchgeführt, welche zeigt, dass die Partikelspur der Strömung folgt.:Table of Contents
List of Figures ............................................................................................................................. i
List of Tables ............................................................................................................................. vi
List of Formula Symbols ......................................................................................................... vii
List of Abbreviations ................................................................................................................. x
1 Introduction ....................................................................................................................... 1
2 Fundamentals .................................................................................................................... 5
2.1 Methods for Particle Tracking and Tracing .............................................................. 5
2.2 Computed Tomography .......................................................................................... 10
2.2.1 Tomography Design and Functional Principle ................................................ 10
2.2.2 Data Reconstruction ......................................................................................... 15
2.3 Digital Image Processing .......................................................................................... 18
3 Material............................................................................................................................. 30
3.1 Laboratory X-ray Tomography System TomoTU ................................................... 30
3.2 Experimental Setup .................................................................................................. 33
3.3 Choice of Particles and Medium ............................................................................. 34
4 Method development ...................................................................................................... 36
4.1 Characterisation of the Motion Artefacts ............................................................... 38
4.2 Method Consideration ............................................................................................. 45
4.3 Pre-processing .......................................................................................................... 46
4.4 Combination of Random Sample Consensus and Least Squares Method.......... 48
4.5 Multiple Particle Tracing .......................................................................................... 51
4.6 Coordinate Processing ............................................................................................. 53
4.7 Method Validation .................................................................................................... 53
5 Results and Discussion .................................................................................................... 59
5.1 Evaluation experimental data ................................................................................. 59
5.2 Comparison with Computational Fluid Dynamics (CFD) ....................................... 68
5.3 Comparison of Artificial Neural Networks with the Developed Classical Digital Image Processing Approach ............................................................................................... 70
6 Summary, Conclusion and Outlook ............................................................................... 74
7 References ........................................................................................................................ 76
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:93348 |
| Date | 30 October 2024 |
| Creators | Siebert, Judith Marie Undine |
| Contributors | Odenbach, Stefan, Hampel, Uwe, Technische Universität Dresden |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, doc-type:doctoralThesis, info:eu-repo/semantics/doctoralThesis, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | info:eu-repo/grantAgreement/Deutsche Forschungsgemeinschaft/GRK 1860/211137567//Mikro-, meso- und makroporöse nicht metallische Materialien: Grundlage und Anwendung/MIMENIMA |
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