@mastersthesis {2433,
	title = {Advances in Monte Carlo simulations for the design of X-ray phase contrast imaging systems},
	volume = {PhD in Science: Physics},
	year = {2026},
	type = {PhD thesis},
	author = {Jonathan Sanctorum}
}
@mastersthesis {2415,
	title = {Edge illumination X-ray phase contrast imaging: Advancements in simulations and inline acquisitions},
	volume = {PhD in Sciences: Physics},
	year = {2025},
	type = {PhD thesis},
	author = {Nicholas Francken}
}
@mastersthesis {2430,
	title = {Improved Diagnosis and Prognosis of Mild Traumatic Brain Injury via Harmonised Diffusion MRI},
	year = {2025},
	type = {PhD thesis},
	author = {Maira Siqueira Pinto}
}
@mastersthesis {2416,
	title = {Shaping the future: 3D knee morphology analysis and predictive models for data-driven patient selection},
	volume = {PhD in Medicine and Health Sciences},
	year = {2025},
	type = {PhD thesis},
	author = {Jonas Grammens}
}
@mastersthesis {2414,
	title = {Towards adaptive multi-resolution edge illumination X-ray phase contrast imaging},
	volume = {PhD in Sciences: Physics},
	year = {2025},
	type = {PhD thesis},
	author = {Pieter-Jan Vanthienen}
}
@mastersthesis {2330,
	title = {Automating the planning of transcatheter Mitral Valve Interventions},
	volume = {Engineering Science},
	year = {2024},
	type = {PhD thesis},
	author = {Lopes, Patricia}
}
@mastersthesis {2386,
	title = {Development of spoiled gradient echo sequences for MRI relaxometry},
	volume = {PhD in Biomedical Sciences},
	year = {2024},
	type = {PhD thesis},
	author = {Marco Andrea Zampini}
}
@mastersthesis {2413,
	title = {Ensuring Flawlessness in Additive Manufacturing: Advances in X-ray Inspection Techniques for Efficient Defect Detection},
	volume = {PhD in Sciences},
	year = {2024},
	type = {PhD thesis},
	author = {Domenico Iuso}
}
@mastersthesis {2363,
	title = {Improved 4DCT reconstruction algorithms for the imaging of foam microstructure formation},
	volume = {PhD in Sciences},
	year = {2024},
	type = {PhD thesis},
	author = {Jens Renders}
}
@mastersthesis {2326,
	title = {Improved X-ray CT reconstruction techniques with non-linear imaging models},
	year = {2024},
	type = {PhD thesis},
	author = {Nathana{\"e}l Six}
}
@mastersthesis {2356,
	title = {Technological breakthroughs towards motion-robust super-resolution quantitative magnetic resonance imaging for improved detection of brain diseases},
	year = {2024},
	month = {07/2024},
	pages = {268},
	school = {University of Antwerp},
	type = {PhD Thesis},
	abstract = {Magnetic resonance imaging (MRI) is one of the most widely used neuroimaging techniques, thanks to its exceptional soft tissue contrast and intrinsic safety for patients. Unfortunately, signal intensities in conventional MRI images are expressed in relative units that depend on scanner hardware and software. While this poses no issue for visual inspection of anatomy, it complicates the quantitative comparison of signal intensities within a scan, between successive scans, and among different subjects. In contrast, quantitative MRI (qMRI) generates quantitative maps of absolute biophysical tissue parameters by voxel-wise fitting a biophysical signal model to a series of contrast-weighted scans. Increasing evidence suggests that qMRI can detect and quantify subtle microscopic tissue damage, aiding in the early detection and accurate diagnosis of various neurodegenerative diseases such as Alzheimer{\textquoteright}s or multiple sclerosis. The impact of these diseases is growing in our rapidly aging society. However, current qMRI techniques require long scan times due to the need for multiple contrast-weighted scans with high-resolution and high signal-to-noise ratio (SNR). Long scan times increase the risk of motion artifacts, reduce patient throughput, and decrease patient comfort, hindering the clinical adoption of qMRI. This work therefore investigates the use of model-based super-resolution reconstruction (SRR) for qMRI, aiming to optimize the trade-off between spatial resolution, SNR, and scan time. SRR enables the reconstruction of a high-resolution scan or parameter map from a series of low-resolution MRI scans, with typically shorter scan time per image. As an additional innovation in this thesis, patient movement is jointly estimated, which is crucial to avoid motion artifacts in the reconstruction. The new SRR framework is extensively tested and compared with existing SRR methods, both in computer simulations and using real brain MRI data. Furthermore, the new framework uses Bayesian statistics to incorporate prior knowledge about noise and brain tissue characteristics into the reconstruction process. This allows for the reconstruction of a 3D high-resolution quantitative tissue parameter map from motion-affected low-resolution MRI scans. Application to real brain MRI shows that the proposed framework significantly improves the accuracy of tissue parameter quantification compared to methods with a separate motion correction step. Finally, the SRR framework is extended to perfusion MRI for improved quantification of cerebral blood flow (CBF), in combination with Arterial Spin Labeling (ASL). As a result, a high-resolution quantitative CBF parameter map can be directly reconstructed from low-resolution ASL MRI scans, significantly improving SNR and CBF accuracy over traditional ASL methods.},
	doi = {10.63028/10067/2069070151162165141},
	url = {https://repository.uantwerpen.be/desktop/irua},
	author = {Quinten Beirinckx}
}
@mastersthesis {2339,
	title = {Hyperspectral imaging for automated inspection of offshore wind infrastructure},
	year = {2023},
	type = {PhD thesis},
	author = {Thomas De Kerf}
}
@mastersthesis {2290,
	title = {Parametric Fiber Analysis for Glass Fiber-reinforced Composite Tomographic Images},
	year = {2023},
	type = {PhD thesis},
	author = {Tim Elberfeld}
}
@mastersthesis {2277,
	title = {Surface and image-based registration methods with statistical modeling for biomedical applications},
	volume = {PhD},
	year = {2023},
	type = {PhD thesis},
	author = {Jeroen Van Houtte}
}
@mastersthesis {2338,
	title = {Unsupervised machine/deep learning mapping (clustering) for single/multi-source remote sensing data },
	year = {2023},
	type = {PhD thesis},
	author = {Kasra Rafiezadeh Shahi}
}
@mastersthesis {2280,
	title = {3D X-ray radiography-based inspection of manufactured objects},
	volume = {PhD},
	year = {2022},
	type = {PhD thesis},
	author = {Alice Presenti}
}
@mastersthesis {2278,
	title = {Advances in biplanar X-ray imaging: calibration and 2D/3D registration},
	volume = {PhD},
	year = {2022},
	type = {PhD thesis},
	author = {Van Nguyen}
}
@mastersthesis {2218,
	title = {Computational anatomy strategies for characterization of brain patterns associated with Alzheimer{\textquoteright}s disease},
	volume = {PhD in Science},
	year = {2022},
	type = {PhD thesis},
	author = {Diana Giraldo}
}
@mastersthesis {2271,
	title = {Quantitative assessment of 3D foot shape using statistical shape analysis},
	volume = {PhD in Sciences},
	year = {2022},
	month = {10/2022},
	type = {PhD thesis},
	author = {Kristina Stankovi{\'c}}
}
@mastersthesis {2140,
	title = {The impact of long-duration spaceflight on brain structure and function},
	volume = {Doctor of Science},
	year = {2021},
	school = {University of Antwerp},
	type = {PhD thesis},
	abstract = {In over half a century of crewed missions to space, many different effects of spaceflight on the human body have been uncovered so far. However, little focus has been directed to investigating how space stressors affect the human brain. The largest body of work in this dissertation describes pioneering findings on brain structural and functional changes after spaceflight in Roscosmos cosmonauts by means of multi-modal magnetic resonance imaging (MRI) in a longitudinal and prospective design. Structural MRI modalities, such as T1-weighted and diffusion MRI, were used to unravel macroscopic volume and microstructural brain tissue composition changes. We found a widespread redistribution of the cerebrospinal fluid (CSF) with secondary mechanistic effects on the grey matter (GM) tissue. We also revealed increased neural tissue volume in motor regions of the brain that suggest evidence for structural brain adaptations, also known as neuroplasticity, associated with altered motor strategies in space. Most CSF changes after spaceflight were still detectable more than half a year after return to Earth, while the GM changes after spaceflight partially reversed in the long term. In addition, functional MRI data was acquired in these cosmonauts to study functional reorganisation of the brain after spaceflight, showing numerous functional connectivity (FC) alterations after spaceflight. Some of these changes persisted in the longer-term, whereas other changes returned back to pre-flight levels. Furthermore, this work also describes the experimental work and preliminary analyses of several Earth-based models. One is a longitudinal MRI pilot study in hindlimb-unloaded (HLU) mice, inducing fluid shifts to the head region, in order to better understand the consequence of these fluid shifts on the brain. A second study was performed in fighter pilots as a model for exposure to high g-levels and sensory conflicts, in which FC was compared to that in a control group. This work rendered a vast increase in available information on structural and functional brain changes after spaceflight compared to several years ago. In the future, the underlying mechanisms of the observed findings need to be understood in more detail. Ultimately, we aim to characterise the effects space stressors have on the brain, to then attempt to mitigate these changes through countermeasures and characterise beneficial coping mechanisms that we can enhance, in order to be fully prepared for future exploration missions into deep space.},
	author = {Steven Jillings}
}
@mastersthesis {2145,
	title = {Strategies for efficient acquisition and reconstruction of structural and quantitative MRI},
	volume = {PhD in Biomedical Sciences},
	year = {2021},
	type = {PhD thesis},
	author = {Maarten Naeyaert}
}
@mastersthesis {2069,
	title = {Accurate and precise perfusion parameter estimation in pseudo-continuous arterial spin labeling MRI},
	volume = {PhD in Sciences/Physics},
	year = {2020},
	type = {PhD thesis},
	author = {Piet Bladt}
}
@mastersthesis {2341,
	title = { Development of advanced hyperspectral unmixing methods },
	year = {2020},
	type = {PhD thesis},
	author = {Bikram Koirala}
}
@mastersthesis {2340,
	title = {Hyperspectral image mixture analysis using notions of sparsity, nonlinearity and decision fusion },
	year = {2020},
	type = {PhD thesis},
	author = {Vera Andrejchenko}
}
@mastersthesis {2342,
	title = {Analysis of hyperspectral images for high-throughput plant phenotyping },
	year = {2019},
	type = {PhD thesis},
	author = {Shahrimie Mohd. Asaari}
}
@mastersthesis {1930,
	title = {The Development of 3D Statistical Shape Models for Diverse Applications},
	volume = {PhD in Sciences},
	year = {2019},
	type = {PhD thesis},
	author = {Femke Danckaers}
}
@mastersthesis {1976,
	title = {Quantitative atom detection from atomic-resolution transmission electron microscopy images},
	volume = {Doctor of Science/Physics},
	year = {2019},
	type = {PhD thesis},
	author = {J Fatermans}
}
@mastersthesis {1867,
	title = {Advances in X-ray reconstruction algorithms for limited data problems in conventional and non-conventional projection geometries},
	volume = {PhD in Sciences / Physics},
	year = {2018},
	type = {PhD thesis},
	author = {Eline Janssens}
}
@mastersthesis {1832,
	title = {Improved MRI Relaxometry through Statistical Signal Processing},
	volume = { Doctor of Science},
	year = {2018},
	month = {02/2018},
	school = {University of Antwerp},
	type = {PhD thesis},
	address = {Antwerp},
	abstract = {Magnetic Resonance Imaging (MRI) relaxometry is a quantitative MRI modality that deals with the estimation of the spin-lattice, T1, and the spin-spin, T2, relaxation times. Both relaxation times are fundamental parameters that describe the spin dynamics within a tissue during the relaxation process of the Nuclear Magnetic Resonance (NMR) phenomenon. During the last decades, spatial T1 and T2 maps have been analyzed to study and monitor the states of a multitude of human diseases. Those studies have shown that MRI relaxometry holds the promise of generating robust, objective image-based biomarkers for central nervous system pathologies, cardiovascular diseases and beyond. Unfortunately, quantitative biomarkers derived from MRI relaxometry are not yet sufficiently specific, sensitive, and robust to be routinely used in clinical practice. On top of that, high-resolution relaxation maps demand a clinically unfeasible long scanning time.

This PhD thesis tries to reduce the obstacles that preclude MRI relaxometry from being a fast, accurate, and precise quantitative MRI modality for clinical use by improving the way MR relaxometry data are acquired, processed and analyzed. In particular, by adopting a statistical signal processing approach, three contributions that address common problems of the field are given. Firstly, we present a unified relaxometry-based processing framework where the T1 map estimation and motion correction are accounted for in a synergistic manner, being both the T1 map and the motion parameters simultaneously estimated with a Maximum-Likelihood estimator. It is demonstrated that substantially more accurate T1 maps are obtained with our proposed integrated approach in comparison to the yet typical but suboptimal two-step approach: T1 model fitting after image registration.  Secondly, we developed a fast, robust, T1 estimator for Variable Flip Angle (VFA) T1 mapping that can provide statistically optimal T1 estimates with unprecedentedly short computation time, thereby enabling optimal, real-time, VFA T1 mapping.  Finally, we were able to reduce the long overall scanning time of MRI relaxometry studies using our novel k-space reconstruction technique that permits the reconstruction of individual MR images with less number of samples than it is commonly required.},
	author = {Gabriel Ramos-Llord{\'e}n}
}
@mastersthesis {1911,
	title = {Robust estimation of diffusion tensor and diffusion kurtosis imaging parameters},
	volume = {PhD in Sciences/Physics},
	year = {2018},
	month = {Oct/2018},
	school = {University of Antwerp},
	type = {PhD thesis},
	address = {Antwerp},
	author = {Quinten Collier}
}
@mastersthesis {1758,
	title = {The application of 3D anthropometry for the development of headgear: a case study on the design of ergonometric brain-computer interfaces},
	year = {2017},
	type = {PhD thesis},
	author = {Daniel Lacko}
}
@mastersthesis {1777,
	title = {Improved reliability of fiber orientation estimation and graph theoretical analysis of structural brain networks with diffusion MRI},
	volume = {Doctor of Science},
	year = {2017},
	school = {University of Antwerp},
	type = {PhD thesis},
	author = {Timo Roine}
}
@mastersthesis {1738,
	title = {Iterative reconstruction for mobile chest tomosynthesis},
	volume = {PhD in Sciences/Physics},
	year = {2017},
	type = {PhD thesis},
	author = {Jeroen Cant}
}
@mastersthesis {1821,
	title = {Model-based reconstruction algorithms for dynamic X-ray CT},
	volume = {PhD in Sciences/Physics},
	year = {2017},
	type = {PhD thesis},
	author = {Vincent Van Nieuwenhove}
}
@mastersthesis {1783,
	title = {Online quality control of fruit and vegetables using X-ray imaging},
	volume = {Doctor of Bioscience Engineering},
	year = {2017},
	type = {PhD thesis},
	author = {Mattias Van Dael}
}
@mastersthesis {1926,
	title = {Optimal statistical experiment design for detecting and locating light atoms using quantitative high resolution (scanning) transmission electron microscopy},
	volume = {PhD in Sciences/Physics},
	year = {2017},
	type = {PhD thesis},
	author = {Julie Gonnissen}
}
@mastersthesis {2367,
	title = {Subpixel analysis of hyperspectral remote sensing images},
	year = {2016},
	type = {PhD thesis},
	author = {Awais Muhammad Akhter}
}
@mastersthesis {1713,
	title = {Super-resolution estimation of quantitative MRI parameters},
	volume = {Doctor of Science},
	year = {2016},
	school = {University of Antwerp},
	type = {PhD thesis},
	author = {Gwendolyn Van Steenkiste}
}
@mastersthesis {2078,
	title = {Full-field X-ray orientation imaging using convex optimization and a discrete representation of six-dimensional position - orientation space},
	year = {2015},
	type = {PhD thesis},
	author = {Nicola Roberto Vigano}
}
@mastersthesis {1631,
	title = {Model-based iterative reconstruction algorithms for computed tomography},
	volume = {PhD in Sciences},
	year = {2015},
	month = {12/2015},
	type = {PhD thesis},
	author = {Van Eyndhoven, Geert}
}
@mastersthesis {1782,
	title = {Optical techniques for real-time morphology measurement of the tympanic membrane},
	year = {2015},
	type = {PhD thesis},
	author = {Sam Van der Jeught}
}
@mastersthesis {1632,
	title = {Towards In Loco X-ray Computed Tomography},
	year = {2015},
	month = {12/2015},
	type = {PhD thesis},
	author = {Andrei Dabravolski}
}
@mastersthesis {2368,
	title = {Advances in unmixing of hyperspectral remote sensing imagery},
	year = {2014},
	type = {PhD thesis},
	author = {Dzevdet Burazerovic}
}
@mastersthesis {2369,
	title = {Hyperspectral data fusion for classification and visualization in remote sensing},
	year = {2013},
	type = {PhD thesis},
	author = {Zahid Mahmood}
}
@mastersthesis {1428,
	title = {Local Prior Knowledge in Tomography},
	volume = {PhD in Sciences: Physics},
	year = {2013},
	month = {11/2013},
	type = {PhD thesis},
	author = {Roelandts, Tom}
}
@mastersthesis {1427,
	title = {Optimal estimation of diffusion MRI parameters},
	volume = {PhD in Sciences: Physics},
	year = {2013},
	month = {10/2013},
	type = {PhD thesis},
	author = {Jelle Veraart}
}
@mastersthesis {1339,
	title = {Advances in the quality and resolution improvement of Magnetic Resonance images},
	volume = {PhD in Sciences: Physics},
	year = {2012},
	month = {03/2012},
	type = {PhD thesis},
	author = {Zhenhua Mai}
}
@mastersthesis {1333,
	title = {Contextual classification of hyperspectral remote sensing images - Application in vegetation monitoring},
	year = {2012},
	pages = {156},
	school = {University of Antwerp},
	type = {PhD thesis},
	address = {Antwerp, Belgium},
	author = {Guy Thoonen}
}
@mastersthesis {1378,
	title = {Estimation and removal of noise from single and multiple coil  Magnetic Resonance images},
	year = {2012},
	month = {11/2012},
	type = {PhD Thesis},
	author = {Jeny Rajan}
}
@mastersthesis {1337,
	title = {Improved analysis of brain connectivity using high angular resolution diffusion MRI},
	volume = {PhD in Sciences},
	year = {2012},
	month = {03/2012},
	pages = {208},
	school = {University of Antwerp},
	type = {PhD thesis},
	keywords = {complex WM architecture, constrained spherical deconvolution, crossing fibers, CSD, Diffusion MRI, diffusion tensor imaging, DTI, HARDI, high angular resolution diffusion imaging, Magnetic Resonance Imaging, MRI, probabilistic tractography, Spherical deconvolution, Tractography, white matter, WM},
	author = {Ben Jeurissen}
}
@mastersthesis {1327,
	title = {Tomographic segmentation and discrete tomography for quantitative analysis of transmission tomography data},
	year = {2012},
	pages = {190},
	school = {University of Antwerp},
	type = {PhD thesis},
	address = {Antwerp, Belgium},
	author = {Wim Van Aarle}
}
@mastersthesis {1284,
	title = {A Decision Support System for the Assessment and Stenting of Tracheal Stenosis},
	year = {2010},
	month = {18/11/2010},
	school = {University of Antwerp},
	type = {PhD thesis},
	address = {Antwerp},
	abstract = {This thesis sets forth a decision support system that proposes a method for automatic assessment of tracheal stenosis and prediction of stent length and diameter from chest CT scans. The main idea behind the proposed method is to estimate the shape of the trachea of a patient as if stenosis were not present. This shape can be used the by the physician for surgery planning and is the basis for the automatic assessment of the stenosis and the prediction of patient-specific stents.
},
	author = {R{\^o}mulo Pinho}
}
@mastersthesis {1290,
	title = {Advances in the reconstruction and statistical processing of Magnetic Resonance images},
	volume = {PhD in Sciences: Physics},
	year = {2009},
	type = {PhD Thesis},
	author = {Dirk H J Poot}
}
@mastersthesis {1271,
	title = {Improved Processing for Diffusion Tensor Magnetic Resonance Images for Coregistration, Atlas Construction, and Voxel Based Analysis},
	year = {2009},
	school = {University of Antwerp},
	type = {PhD thesis},
	address = {Antwerp},
	author = {Wim Van Hecke}
}
@mastersthesis {1172,
	title = {Parameterization and Correspondence for Improved Modeling, Analysis, and Visualization of Tubular Surfaces.},
	year = {2009},
	school = {University of Antwerp},
	type = {PhD thesis},
	address = {Antwerp},
	author = {Toon Huysmans}
}
@mastersthesis {1270,
	title = {Towards accurate image reconstruction from truncated X-ray CT projections},
	year = {2009},
	school = {University of Antwerp},
	type = {PhD thesis},
	address = {Antwerp},
	author = {Gert Van Gompel}
}
@mastersthesis {1291,
	title = {Data-driven methods for the analysis of time-resolved mental chronometry fMRI data sets},
	year = {2007},
	type = {PhD thesis},
	author = {Alain Smolders}
}
@mastersthesis {1345,
	title = {Information Extraction from Hyperspectral Images Applied to Vegetation},
	volume = {doctor in physics},
	year = {2007},
	type = {PhD thesis},
	author = {Pieter Kempeneers}
}
@mastersthesis {1273,
	title = {Modeling and Processing of Diffusion Tensor Magnetic Resonance Images for Improved Analysis of Brain Connectivity},
	year = {2006},
	school = {University of Antwerp},
	type = {PhD thesis},
	address = {Antwerp},
	author = {Alexander Leemans}
}
@mastersthesis {1272,
	title = {Reconstructie van Transversaal Getrunceerde Cone Beam Projecties in Micro-Tomografie},
	year = {2006},
	school = {University of Antwerp},
	type = {PhD thesis},
	address = {Antwerp},
	author = {G. Tisson}
}
@mastersthesis {1289,
	title = {Model-based approach for Beam Hardening Correction and Resolution Measurements in Microtomography},
	year = {2004},
	school = {University of Antwerp},
	type = {PhD thesis},
	address = {Antwerp},
	author = {Elke Van de Casteele}
}
@mastersthesis {1285,
	title = {On the wavefront aberrations of the human eye and the search for their origins},
	year = {2004},
	school = {University of Antwerp},
	type = {PhD thesis},
	address = {Antwerp},
	author = {Jos Rozema}
}
@mastersthesis {1286,
	title = {Feature Selection and Classifier Ensembles: A Study on Hyperspectral Remote Sensing Data},
	year = {2003},
	school = {University of Antwerp},
	type = {PhD thesis},
	address = {Antwerp},
	author = {Shixin Yu}
}
@mastersthesis {1278,
	title = {Unsupervised Pattern Recognition - Dimensionality Reduction and Classification},
	year = {2002},
	school = {University of Antwerp},
	type = {PhD thesis},
	address = {Antwerp},
	author = {Steve De Backer}
}
@mastersthesis {1279,
	title = {Infotree - A study of its foundations},
	year = {2001},
	school = {University of Antwerp},
	type = {PhD thesis},
	address = {Antwerp},
	author = {Bart De Boeck}
}
@mastersthesis {1288,
	title = {Pattern Recognition and Complex Systems},
	year = {2000},
	school = {University of Antwerp},
	type = {PhD thesis},
	address = {Antwerp},
	author = {Ing Ren Tsang}
}
@mastersthesis {1287,
	title = {Pattern Recognition, Neighborhood Codes, and Lattice Animals},
	year = {2000},
	school = {University of Antwerp},
	type = {PhD thesis},
	address = {Antwerp},
	author = {Ing Jyh Tsang}
}
@mastersthesis {1277,
	title = {Peak Characterization using Parameter Estimation Methods},
	year = {1999},
	school = {University of Antwerp},
	type = {PhD thesis},
	address = {Antwerp},
	author = {E. Bettens}
}
@mastersthesis {1302,
	title = {Estimation of Signal and Noise from Magnitude Magnetic Resonance Images},
	year = {1998},
	type = {PhD thesis},
	author = {Jan Sijbers}
}
@mastersthesis {1281,
	title = {Image Analysis for Material Characterisation},
	year = {1998},
	school = {University of Antwerp},
	type = {PhD thesis},
	address = {Antwerp},
	author = {S. Livens}
}
