Leiden University Scholarly Publications

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Graas, A.B.M. (2026)

Faster X-ray computed tomography in real-world dynamic applications

Doctoral Thesis
This dissertation investigates how the efficiency of Computed Tomography (CT) can be improved for dynamic scientific and industrial applications. Examples of such applications are (i) rapidly moving processes, in which each instant in time effectively constitutes an individual reconstruction problem; (ii) iterative and learning-based algorithms, which generally demand more computational resources than conventional algorithms; and (iii) applications that employ CT repetitively, for example for detecting production defects on conveyor belts. This thesis comprises four chapters, each corresponding to a research project and publication. Chapter 2 discusses a set-up for high-speed tomography of fluidized beds. Chapter 3 presents an on-line deep learning method for real-time tomography. Chapter 4 introduces a new software package that enables tailored implementations of CT algorithms on graphics processing units (GPUs). Chapter 5 lastly pioneers a self-supervised deep learning method...Show moreThis dissertation investigates how the efficiency of Computed Tomography (CT) can be improved for dynamic scientific and industrial applications. Examples of such applications are (i) rapidly moving processes, in which each instant in time effectively constitutes an individual reconstruction problem; (ii) iterative and learning-based algorithms, which generally demand more computational resources than conventional algorithms; and (iii) applications that employ CT repetitively, for example for detecting production defects on conveyor belts. This thesis comprises four chapters, each corresponding to a research project and publication. Chapter 2 discusses a set-up for high-speed tomography of fluidized beds. Chapter 3 presents an on-line deep learning method for real-time tomography. Chapter 4 introduces a new software package that enables tailored implementations of CT algorithms on graphics processing units (GPUs). Chapter 5 lastly pioneers a self-supervised deep learning method for noise removal of radiographs in scenarios where neither ground truths nor secondary noisy examples can be acquired.
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Computed Tomography
Tomographic reconstruction
X-ray imaging
Self-supervised Learning
Dynamical systems
Fluidized beds
Real-time tomography
On-line learning
GPU programming
All authors
Graas, A.B.M.
Supervisor
Batenburg, K.J.
Co-supervisor
Lucka, F.
Committee
Bonsangue, M.M.; Verbeek, F.J.; Cao, L.; Kolehmainen, V.; Viganò, N.
Qualification
Doctor (dr.)
Awarding Institution
Leiden Institute of Advanced Computer Science (LIACS), Faculty of Science, Leiden University
Date
2026-02-04