Jan Sijbers | University of Antwerp (original) (raw)

Papers by Jan Sijbers

Polymers

The properties of fiber reinforced polymers are strongly related to the length and orientation of... more The properties of fiber reinforced polymers are strongly related to the length and orientation of the fibers within the polymer matrix, the latter of which can be studied using X-ray computed tomography (XCT). Unfortunately, resolving individual fibers is challenging because they are small compared to the XCT voxel resolution and because of the low attenuation contrast between the fibers and the surrounding resin. To alleviate both problems, anisotropic dark field tomography via grating based interferometry (GBI) has been proposed. Here, the fiber orientations are extracted by applying a Funk-Radon transform (FRT) to the local scatter function. However, the FRT suffers from a low angular resolution, which complicates estimating fiber orientations for small fiber crossing angles. We propose constrained spherical deconvolution (CSD) as an alternative to the FRT to resolve fiber orientations. Instead of GBI, edge illumination phase contrast imaging is used because estimating fiber orie...

The Visual Computer

In X-ray computed tomography, discrete tomography (DT) algorithms have been successful at reconst... more In X-ray computed tomography, discrete tomography (DT) algorithms have been successful at reconstructing objects composed of only a few distinct materials. Many DT-based methods rely on a divide-and-conquer procedure to reconstruct the volume in parts, which improves their run-time and reconstruction quality. However, this procedure is based on static rules, which introduces redundant computation and diminishes the efficiency. In this work, we introduce an update strategy framework that allows for dynamic rules and increases control for divide-and-conquer methods for DT. We illustrate this framework by introducing Tabu-DART, which combines our proposed framework with the Discrete Algebraic Reconstruction Technique (DART). Through simulated and real data reconstruction experiments, we show that our approach yields similar or improved reconstruction quality compared to DART, with substantially lower computational complexity.

International Journal of Computer Assisted Radiology and Surgery

Purpose The registration of a 3D atlas image to 2D radiographs enables 3D pre-operative planning ... more Purpose The registration of a 3D atlas image to 2D radiographs enables 3D pre-operative planning without the need to acquire costly and high-dose CT-scans. Recently, many deep-learning-based 2D/3D registration methods have been proposed which tackle the problem as a reconstruction by regressing the 3D image immediately from the radiographs, rather than registering an atlas image. Consequently, they are less constrained against unfeasible reconstructions and have no possibility to warp auxiliary data. Finally, they are, by construction, limited to orthogonal projections. Methods We propose a novel end-to-end trainable 2D/3D registration network that regresses a dense deformation field that warps an atlas image such that the forward projection of the warped atlas matches the input 2D radiographs. We effectively take the projection matrix into account in the regression problem by integrating a projective and inverse projective spatial transform layer into the network. Results Comprehen...

Digital X-ray laminography is a technique for generating sectional images of an object, using an ... more Digital X-ray laminography is a technique for generating sectional images of an object, using an X-ray source and detector that move in opposite directions in planes above and below the object. However, motion of the X-ray source and detector during acquisition causes blurring in the projection images, which in turn leads to blurred reconstructions. To prevent these motion artifacts, both the source and detector need to be still during the X-ray pulse which leads to longer acquisition times. In this paper, we consider a system for continuous digital laminography in which the X-ray source is continuously moving and emitting, which would allow for a higher rotation speed. The inherent motion related blurring in the projections is modeled in the reconstruction algorithm. A preliminary simulation experiment comparing the classical digital laminography with the proposed continuous technique indicates that a higher reconstruction quality can be achieved near the rotation center and less streak artifacts are present, at the cost of a decreasing tangential resolution with increasing distance from the rotation center.

Applied Sciences, 2021

Beam hardening and scattering effects can seriously degrade image quality in polychromatic X-ray ... more Beam hardening and scattering effects can seriously degrade image quality in polychromatic X-ray CT imaging. In recent years, polychromatic image reconstruction techniques and scatter estimation using Monte Carlo simulation have been developed to compensate for beam hardening and scattering CT artifacts, respectively. Both techniques require knowledge of the X-ray tube energy spectrum. In this work, Monte Carlo simulations were used to calculate the X-ray energy spectrum of FleXCT, a novel prototype industrial micro-CT scanner, enabling beam hardening and scatter reduction for CT experiments. Both source and detector were completely modeled by Monte Carlo simulation. In order to validate the energy spectra obtained via Monte Carlo simulation, they were compared with energy spectra obtained via a second method. Here, energy spectra were calculated from empirical measurements using a step wedge sample, in combination with the Maximum Likelihood Expectation Maximization (MLEM) method. ...

IEEE Transactions on Image Processing, 2017

In computed tomography (CT), motion and deformation during the acquisition lead to streak artefac... more In computed tomography (CT), motion and deformation during the acquisition lead to streak artefacts and blurring in the reconstructed images. To remedy these artefacts, we introduce an efficient algorithm to estimate and correct for global affine deformations directly on the cone beam projections. The proposed technique is data driven and thus removes the need for markers and/or a tracking system. A relationship between affine transformations and the cone beam transform is proved and used to correct the projections. The deformation parameters that describe deformation perpendicular to the projection direction are estimated for each projection by minimizing a plane-based inconsistency criterion. The criterion compares each projection of the main scan with all projections of a fast reference scan, which is acquired prior or posterior to the main scan. Experiments with simulated and experimental data show that the proposed affine deformation estimation method is able to substantially reduce motion artefacts in cone beam CT images.

Optics Express, 2016

Object reconstruction from a series of projection images, such as in computed tomography (CT), is... more Object reconstruction from a series of projection images, such as in computed tomography (CT), is a popular tool in many different application fields. Existing commercial software typically provides sufficiently accurate and convenient-to-use reconstruction tools to the end-user. However, in applications where a non-standard acquisition protocol is used, or where advanced reconstruction methods are required, the standard software tools often are incapable of computing accurate reconstruction images. This article introduces the ASTRA Toolbox. Aimed at researchers across multiple tomographic application fields, the ASTRA Toolbox provides a highly efficient and highly flexible open source set of tools for tomographic projection and reconstruction. The main features of the ASTRA Toolbox are discussed and several use cases are presented.

Diffusion Tensor Imaging, 2016

Diffusion kurtosis imaging (DKI) is a recent imaging method that probes the diffusion of water mo... more Diffusion kurtosis imaging (DKI) is a recent imaging method that probes the diffusion of water molecules. Whereas diffusion tensor imaging (DTI) models the diffusion as a 3D Gaussian function, DKI takes it one step further by additionally quantifying the degree of non-Gaussian diffusion. DKI diffusion parameters have been shown to yield clinically relevant information that is not captured by a more conventional DTI model. Thanks to the increase of clinical applications, DKI is becoming increasingly popular in neuroimaging. In this chapter, we will review the basics of DKI. Furthermore, we explain how DKI parameters can be estimated with the highest precision and accuracy. Finally, we discuss some applications of DKI.

Journal of Alzheimer's disease : JAD, Jan 22, 2015

The purpose of this explorative study was to investigate whether diffusion tensor imaging (DTI) a... more The purpose of this explorative study was to investigate whether diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) parameter changes are reliable measures of white matter integrity changes in Alzheimer's disease (AD) patients using a whole brain voxel-based analysis (VBA). Therefore, age- and gender-matched patients with mild cognitive impairment (MCI) due to AD (n = 18), dementia due to AD (n = 19), and age-matched cognitively healthy controls (n = 14) were prospectively included. The magnetic resonance imaging protocol included routine structural brain imaging and DKI. Datasets were transformed to a population-specific atlas space. Groups were compared using VBA. Differences in diffusion and mean kurtosis measures between MCI and AD patients and controls were shown, and were mainly found in the splenium of the corpus callosum and the corona radiata. Hence, DTI and DKI parameter changes are suggestive of white matter changes in AD.

In recent years,‭ ‬the resolution of high-end X-ray tomography scanners has increased considerabl... more In recent years,‭ ‬the resolution of high-end X-ray tomography scanners has increased considerably,‭ ‬to the point where details smaller than‭ ‬1‭ ‬micron can now be observed routinely.‭ ‬At these small pixel sizes,‭ ‬small perturbations in the imaging equipment or in the object itself can cause significant alignment artifacts in the‭ ‬3D reconstruction.‭ ‬Besides high-end experimental equipment,‭ ‬accurate alignment methods are needed to fully exploit the available projection data.‭ ‬In this paper,‭ ‬we introduce an alignment method that aims at estimating the reconstruction and the alignment parameters simultaneously,‭ ‬resulting in an aligned reconstruction that matches optimally with the observed projection data.‭ ‬Since the underlying system of equations is heavily underdetermined and ill-posed,‭ ‬standard optimization algorithms have convergence problems.‭ ‬Therefore,‭ ‬correct scaling of the alignment parameters,‭ ‬together with a numerically stable approach for computing gra...

Proceedings of the 10th IEEE International Conference on Information Technology and Applications in Biomedicine, 2010

... image registration Wolfgang Jacquet, Edgard Nyssen, Pieter de Groen and Jan Sijbers ... 1. Si... more ... image registration Wolfgang Jacquet, Edgard Nyssen, Pieter de Groen and Jan Sijbers ... 1. Sijbers is with the Department of Physics, Vision Lab, Uni versity of Antwerp, Universiteitsplein I, B-2610 Wilrijk, Belgium Jan.Sijbers@ua.ac.be ... [11] and Maes et al. ...

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB), 2014

Many image processing methods applied to magnetic resonance (MR) images directly or indirectly re... more Many image processing methods applied to magnetic resonance (MR) images directly or indirectly rely on prior knowledge of the statistical data distribution that characterizes the MR data. Also, data distributions are key in many parameter estimation problems and strongly relate to the accuracy and precision with which parameters can be estimated. This review paper provides an overview of the various distributions that occur when dealing with MR data, considering both single-coil and multiple-coil acquisition systems. The paper also summarizes how knowledge of the MR data distributions can be used to construct optimal parameter estimators and answers the question as to what precision may be achieved ultimately from a particular MR image.

Diffusion-weighted (DW) magnetic resonance imaging (MRI) is a non-invasive imaging method, which ... more Diffusion-weighted (DW) magnetic resonance imaging (MRI) is a non-invasive imaging method, which can be used to investigate neural tracts in the white matter (WM) of the brain. Significant partial volume effects (PVEs) are present in the DW signal due to relatively large voxel sizes. These PVEs can be caused by both non-WM tissue, such as gray matter (GM) and cerebrospinal fluid (CSF), and by multiple non-parallel WM fiber populations. High angular resolution diffusion imaging (HARDI) methods have been developed to correctly characterize complex WM fiber configurations, but to date, many of the HARDI methods do not account for non-WM PVEs. In this work, we investigated the isotropic PVEs caused by non-WM tissue in WM voxels on fiber orientations extracted with constrained spherical deconvolution (CSD). Experiments were performed on simulated and real DW-MRI data. In particular, simulations were performed to demonstrate the effects of varying the diffusion weightings, signal-to-noise ratios (SNRs), fiber configurations, and tissue fractions. Our results show that the presence of non-WM tissue signal causes a decrease in the precision of the detected fiber orientations and an increase in the detection of false peaks in CSD. We estimated 35-50% of WM voxels to be affected by non-WM PVEs. For HARDI sequences, which typically have a relatively high degree of diffusion weighting, these adverse effects are most pronounced in voxels with GM PVEs. The non-WM PVEs become severe with 50% GM volume for maximum spherical harmonics orders of 8 and below, and already with 25% GM volume for higher orders. In addition, a low diffusion weighting or SNR increases the effects. The non-WM PVEs may cause problems in connectomics, where reliable fiber tracking at the WM-GM interface is especially important. We suggest acquiring data with high diffusion-weighting 2500-3000 s/mm 2 , reasonable SNR (∼30) and using lower SH orders in GM contaminated regions to minimize the non-WM PVEs in CSD.

Journal of Structural Biology, 2011

Iterative reconstruction algorithms are becoming increasingly important in electron tomography of... more Iterative reconstruction algorithms are becoming increasingly important in electron tomography of biological samples. These algorithms, however, impose major computational demands. Parallelization must be employed to maintain acceptable running times. Graphics Processing Units (GPUs) have been demonstrated to be highly cost-effective for carrying out these computations with a high degree of parallelism. In a recent paper by Xu et al. [1], a GPU implementation strategy was presented that obtains a speedup of an order of magnitude over a previously proposed GPU-based electron tomography implementation. In this technical note, we demonstrate that by making alternative design decisions in the GPU implementation, an additional speedup can be obtained, again of an order of magnitude. By carefully considering memory access locality when dividing the workload among blocks of threads, the GPU's cache is used more efficiently, making more effective use of the available memory bandwidth.

Biological Psychiatry, 2011

Repeated exposure to mild stressors induces anhedonia-a core symptom of major depressive disorder... more Repeated exposure to mild stressors induces anhedonia-a core symptom of major depressive disorder-in up to 70% of the stress-exposed rats, whereas the remaining show resilience to stress. This chronic mild stress (CMS) model is well documented as an animal model of major depressive disorder. We examined the morphological, microstructural, and metabolic characteristics of the hippocampus in anhedonic and stress resilient rats that may mark the differential behavioral outcome. Anhedonic (n = 8), resilient (n = 8), and control (n = 8) rats were subjected to in vivo diffusion kurtosis imaging, high-resolution three-dimensional magnetic resonance imaging and proton magnetic resonance spectroscopy. Diffusion kurtosis parameters were decreased in both CMS-exposed groups. A significant inward displacement in the ventral part of the right hippocampus was apparent in the resilient subjects and an increase of the glutamate:total creatine ratio and N-acetylaspartylglutamate:total creatine was observed in the anhedonic subjects. Diffusion kurtosis imaging discloses subtle substructural changes in the hippocampus of CMS-exposed animals irrespective of their anhedonic or resilient nature. In contrast, proton magnetic resonance spectroscopy and magnetic resonance imaging-based shape change analysis of the hippocampus allowed discrimination of these two subtypes of stress sensitivity. Although the precise mechanism discriminating their behavior is yet to be elucidated, the present study underlines the role of the hippocampus in the etiology of depression and the induction of anhedonia. Our results reflect the potency of noninvasive magnetic resonance methods in preclinical settings with key translational benefit to and from the clinic.

IEEE Transactions on Image Processing, 2011

In this paper, we present an iterative reconstruction algorithm for discrete tomography, called D... more In this paper, we present an iterative reconstruction algorithm for discrete tomography, called DART (Discrete Algebraic Reconstruction Technique). DART can be applied if the scanned object is known to consist of only a few different compositions, each corresponding to a constant grey value in the reconstruction. Prior knowledge of the grey values for each of the compositions is exploited to steer the current reconstruction towards a reconstruction that contains only these grey values. Based on experiments with both simulated CT data and experimental µCT data, it is shown that DART is capable of computing more accurate reconstructions from a small number of projection images, or from a small angular range, than alternative methods. It is also shown that DART can deal effectively with noisy projection data and that the algorithm is robust with respect to errors in the estimation of the grey values.