Research team

Vision lab

Expertise

Magnetic resonance imaging (MRI) : development of novel image reconstruction, processing, and analysis methods MRI. In particular: - diffusion magnetic resonance imaging (DTI, DWI) - diffusion kurtosis imaging (DKI) - white matter tractography - functional MRI (fMRI) - noise estimation and reduction X-ray Computed tomography (CT) - iterative and analytic image reconstruction - artefact reduction (beam hardening, ring artefacts, motion artefacts, ...) - discrete tomography - model based reconstruction - dynamic imaging (CT) - dual energy CT - metrology - laminography, tomosynthesis

Optimal experimental design for quantitative super resolution reconstruction MRI. 01/09/2021 - 31/08/2022

Abstract

Magnetic resonance imaging (MRI) is a medical imaging technique that generates excellent soft-tissue contrast and allows for investigating both anatomy and function of tissues noninvasively. In conventional MRI, direct HR acquisition requires long scan times to achieve adequate precision and spatial resolution of the resulting MR image. From a diagnostic perspective, long scan times increase the likelihood of motion artefacts, whereas, from an economical perspective, they reduce the throughput. In addition, long scan times cause discomfort for patients. Multi-slice super-resolution reconstruction (MS-SRR) has the potential to reduce this limitation, improving the inherent trade-off between resolution, SNR, and scan time. MS-SRR consists in estimating a 3D high-resolution (HR) image from a series of 2D multi-slice images with a low through-plane resolution. Two strategies are conventionally adopted to acquire data for an MS-SRR experiment. The first consists in acquiring a set of multi-slice images with parallel orientations, where each image is shifted in the through-plane direction by a different, sub-pixel distance. The second consists in acquiring rotated multi-slice images, where each image is rotated around the frequency and/or phase encoding axis by a different rotation angle. These two strategies will be compared in terms of accuracy and precision of the reconstructed images. MS-SRR estimation is generally an ill-posed problem and the use of regularization has an impact on the SRR estimated image. I will investigate a Bayesian SRR framework in which local correlation information is learnt from MRI images and used to stabilize the SRR estimate. An optimal experimental design framework will be developed in which the Bayesian Mean Squared Error (BMSE) of the MAP estimator is proposed as a performance criterion, to compare the two aforementioned acquisition strategies in the context of regularized MS-SRR. We plan to validate the BMSE-based predictions on simulated and real data. Finally, we aim to extend the MS-SRR optimal experimental design framework to quantitative SRR (qSRR). In qSRR, a high resolution (HR) relaxation parameter map is estimated from a series of weighted multi-slice images with a low through-plane resolution. Each slice of each LR image can be acquired with different weighting settings, thus offering maximum flexibility to optimize the weighting settings for each slice individually. Aiming at the highest attainable precision for a given acquisition time, we will optimize the experiment design of the SRR framework by searching for the optimal acquisition parameters. This research is expected to further improve the trade-off between signal-to-noise, resolution, and scan time in qSRR, by for example allowing precise estimation of HR parameter maps from shorter scans.

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Intra-scan modulation for accelerated diffusion magnetic resonance imaging. 01/09/2021 - 31/08/2022

Abstract

Diffusion magnetic resonance imaging is a powerful, non-invasive technique to investigate the microscopic properties of tissues, based on analyzing the diffusion of water molecules, which is influenced by the tissues' microstructure. However, the clinical application of high resolution diffusion imaging is impeded due to its long scanning time. To reduce scan time, fast acquisition schemes such as multi-shot acquisition and undersampled data acquisition have been introduced. However, these acquisition schemes may introduce serious artifacts in the reconstructed diffusion parameter maps if not complemented with smart image reconstruction. In this project, we introduce an advanced reconstruction framework that allows accelerated imaging by varying the diffusion contrast settings during the acquisition of a single image, e.g. for each shot in the multi-shot acquisition, introducing intra-scan modulation. This model-based reconstruction framework estimates diffusion parameter maps directly from the acquired intra-scan modulated data and simultaneously corrects for artifacts related to shot-to-shot phase inconsistencies. By now, the statistical performance of this framework has been assessed in Monte Carlo simulation studies. In the next phase of the project, the framework will be extended to include higher-order phase patterns to account for more complex subject motion. In addition, the framework will be combined with common acceleration methods such as parallel imaging to aim for higher acceleration rates. Finally, we aim to define the optimal imaging settings including sampling strategy and optimal diffusion contrast set-ups using a statistical experiment design based on a Cramér-Rao Lower Bound analysis. The feasibility of this approach will be investigated in real-data experiments considering at first, retrospective acceleration and, secondly, direct acquisition of multi-shot intra-scan modulated data.

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Translational research on quantitative super-resolution MR imaging. 01/05/2021 - 30/04/2023

Abstract

As defined by the Quantitative Imaging Biomarkers Alliance (QIBA), quantitative imaging aims at extracting "quantifiable features from medical images for the assessment of normal or the severity, degree of change, or status of a disease, injury, or chronic condition relative to normal". However, the lack of widespread consensus and integration in commercial software of quantitative MRI (qMRI) methods have hampered both the direct comparison between results of different research groups as well as the translation of cutting-edge qMRI technology to the clinic. The general aim of this research project is to bridge the gap between qMRI research and clinical applications using the syngo.via Frontier platform from Siemens Healthineers. This platform serves as an integrated research environment for advanced post-processing of medical images, allowing for both the development and the evaluation of algorithms in close collaboration with clinicians. An established group of MRI post-processing algorithms, commonly referred to as super-resolution reconstruction (SRR) techniques, are used to estimate a high-resolution image from an acquired set of low-resolution images, thereby improving the MRI trade-off between signal-to-noise ratio (SNR), spatial resolution and scan time. Specific SRR methods have been developed for high-resolution anatomical MRI, but also for qMRI by integrating quantitative models that enable the estimation of biophysical parameters for tissue characterisation. Although SRR holds applications in a variety of clinical fields, its clinical potential in the context of musculoskeletal (MSK) MRI remains to be thoroughly investigated. Consequently, the specific aim of this research project is twofold: 1. Following the demonstrated feasibility of SRR TSE MRI of the knee, we aim to evaluate the clinical application of the described anatomical SRR technique for accelerated high-resolution isotropic 3D knee MRI by comparison with the current clinical standard. Furthermore, the integration of the SRR post-processing algorithm for MSK MRI on the Siemens syngo.via Frontier platform will be finalized to facilitate clinical evaluation. 2. As previously reported, 3D UTE Spiral VIBE MRI shows great promise for fast T2* mapping of knee structures. To further improve accuracy and precision of the T2* estimation, we aim to develop a quantitative SRR framework for rapid isotropic T2* mapping of the knee, based on both ultra-short echo time (UTE) and multi-echo gradient echo (MEGE) imaging. In light of QIBA's mission, the developed quantitative SRR framework will be used to probe the suitability of the biophysical short and long T2* parameters as biomarkers of MSK tissue structural integrity. More specifically, the framework will be used to assess the severity of anterior cruciate ligaments (ACL) injuries and to evaluate the healing process of reconstructed/repaired ACLs.

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Towards robust disability prediction in multiple sclerosis from brain MRI. 01/05/2021 - 30/04/2023

Abstract

Multiple Sclerosis (MS) is an autoimmune inflammatory disease of the central nervous system. There is no cure for MS, but many treatments have been developed to slow down its progression. Disease progression monitoring and clinical decision making often rely on the expanded disability status scale (EDSS). Unfortunately, EDSS suffers from poor reliability, repeatability, and high inter-rater variability. A first goal of this project is to reduce the inter-rater variability and increase repeatability in quantifying the risk of patient disability by developing a machine learning technique based on anatomical magnetic resonance images (MRI) and diffusion MRI (dMRI). As a first step, we will focus on the prediction of the EDSS scoring, but other clinical scores will be included as well. To develop an automated EDSS scoring method, a large database is required. Such databases are typically composed of images from multiple centers, and hence depend on scanner hardware, reconstruction algorithms and acquisition protocols. These factors lead to high intra- and intersite variability in structural MRI data, and even more in parameters derived from dMRI data. A second goal is to develop, implement and validate harmonization methods for structural and dMRI data, to reduce unwanted variability while preserving biological variability. Working towards this goal, I co-authored a review paper on dMRI harmonization methods [Pinto, et al. 2020]. A next step is to validate a recently proposed diffusion harmonisation method "Method of Moments" [Huynh, et al. 2019] on in vivo dMRI data. Finally, as part of the Horizon 2020 initial training network B-Q MINDED, the project's ultimate goal is the integration of the harmonisation and EDSS scoring algorithms in a product that can be used in clinical trials and, in a later stage, in the daily clinic. Roadmap September 2021-October 2021: Finalizing of the EDSS scoring application based on anatomical MRI. Submission of a journal manuscript "Prediction of EDSS scores in MS patients from MRI" by the end of October 2021. November 2021-December 2021: Finalizing implementation and validation of deep-learning approaches for the harmonisation of anatomical and diffusion MR images. January 2022 - February 2022: Automated EDSS scoring based on harmonized structural and dMRI data. March 2022-July 2022: preparation of the PhD thesis. References Pinto, M.S., Paolella, R.,…..et al. "Harmonization of brain diffusion MRI: Concepts and methods." Frontiers in Neuroscience 14 (2020). Huynh, Khoi Minh, et al. "Multi-site harmonization of diffusion MRI data via method of moments." IEEE transactions on medical imaging 38.7 (2019): 1599-1609.

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Robust quantification of diffusion kurtosis parameters. 01/05/2021 - 30/04/2023

Abstract

Diffusion-weighted magnetic resonance imaging is a non-invasive technique to reveal the brain's microstructural properties by probing the local diffusion of water molecules. Fitting mathematical models to diffusion MRI data allows to extract quantitative information and, among these models, the diffusion tensor imaging (DTI) model is the most commonly applied. However, recent literature has shown that the diffusion kurtosis imaging (DKI) model can provide more accurate estimates of diffusion tensor properties as well as additional information in clinical applications. Unfortunately, the quality of the diffusion metrics extracted from these models is degraded by several acquisition artefacts, such as Gibbs ringing, eddy current distortions and susceptibility-induced artefacts. Besides these well-known artefacts, voxels in DW images may suffer from additional problems: ● signal intensity outliers resulting from motion, cardiac pulsation or system instabilities can compromise the parameter estimates to an extent that they are no longer useful; ● image voxels are relatively large (2 to 3 mm isotropic) and thus susceptible to partial volume effects, which is particularly a problem in brain images when cerebrospinal fluid contamination occurs making the interpretation of diffusion markers ambiguous and no longer tissue-specific. A first aim of this project is to improve and validate an outlier-robust framework for diffusion and kurtosis parameter estimation. During the initial phase of the PhD project, the performance of such a framework was assessed in simulation experiments, thereby ignoring spatial correlations of outliers. As a logical step for improving the method, prior information on how outliers correlate within a slice will be included. Subsequently, a validation study will be performed to assess the reproducibility of DKI metrics in real test-retest datasets. In the second and third year of the PhD project, an advanced bi-compartment model based on the combination of diffusion and relaxometry data has been proposed for correcting free water contamination in multi-shell multi-echo diffusion data. This work has resulted in a journal paper that will be submitted in the second quarter of 2021. This promising approach exploits the combination of diffusion and relaxometry data as a rich source of information, but is not applicable to datasets acquired with a single echo time, which are typically acquired in clinical practice. For this reason, our next research goal is to implement and validate approaches for partial volume correction in single/multi-shell single-echo acquisitions. For this purpose, the potential of artificial intelligence solutions will be explored to deal with the ill-conditioned parameter estimation problem. Finally, as part of the Horizon 2020 initial training network (ITN) B-Q MINDED, the ultimate aim of the project will be to integrate the developed techniques in a regulatory approved quantitative MR product that can be used in clinical trials and, in a later stage, in daily clinical practice for improved assessment of drug efficacy and patient follow-up.

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B budget True Atom (True atom probe tomography for semiconductor) 2021. 01/01/2021 - 31/12/2021

Abstract

Atom probe tomography is an analysis tool in materials science that allows to inspect the 3D chemical composition of needle shaped samples at the nano scale. The method works by field-induced evaporation. Ions are then consecutively emitted from the apex of the needle and are absorbed by a position sensitive detector. The result is a tomographic, atomically resolved image of the evaporated volume, represented as a point cloud in which each point is an atom. The current reconstruction approaches however were developed with homogeneous samples in mind and do not account for the complex shape of the sample surface, which evolves during the field evaporation process. The goal of this project is to develop new reconstruction methods that take the shape into account.

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A budget 2021 IMEC. 01/01/2021 - 31/12/2021

Abstract

The ASTRA toolbox is an open source platform for tomographic reconstruction. In this project, extensions for the ASTRA toolbox are developed. These include refractive imaging such as TeraHertz tomography.

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Prior-knowledge based iterative reconstruction for terahertz tomography. 01/11/2020 - 31/10/2022

Abstract

Terahertz (THz) tomography is an up and coming technology that uses electromagnetic radiation with terahertz frequency for tomographic imaging. Like X-rays, THz waves provide information about the interior of an object through interaction with the object. THz waves interact with many materials in different ways. They are absorbed in polar materials such as water, penetrate most packing materials (plastic, paper, ceramics, …) and are completely reflected by metal. In contrast to X-rays, there are no known negative effects of THz waves, making their application attractive for biomedical purposes as well as industrial inspection, non-destructive testing, material science and agro-food applications. The Gaussian THz beam however, diverges much faster than an X-ray beam and reflection and refraction effects play a dominant role, preventing the use of conventional X-ray reconstruction techniques. In this project, we focus on the development of prior-knowledge based iterative reconstruction techniques for THz tomographic data that model the physics of the THz image formation in the image reconstruction process, as opposed to performing pre- or post-processing steps. Such algorithms are nearly unexplored for THz imaging and can greatly increase the applicability of the technique through a substantial improvement in image quality.

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Q-INSPEX: Quantitative industrial inspection through non-invasive imaging. 15/10/2020 - 31/12/2026

Abstract

Q-INSPEX aims at the development of novel imaging and image processing protocols to non-invasively and quantitatively inspect objects and subjects. Core imaging technologies herein are X-ray, (near)-infrared, and TeraHertz imaging. These technologies are largely complementary to each other and can be used in different set-ups as (i) an R&D tool to measure specific characteristics of materials (e.g. food structures or polymers), (ii) as a quality control procedure implemented within an industrial setting (i.e. compatible with processing speeds) or (iii) in-field inspections of crops and infrastructure (e.g. corrosion). Furthermore, they can be applied in a wide variety of domains: additive manufacturing, composites, art objects, textiles, archaeology, crops, food, etc.

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Next generation X-ray phasecontrast imaging for food quality and process engineering. 01/10/2020 - 30/09/2024

Abstract

Many properties of food, plants or seeds that are relevant to process engineering or quality are related to microstructure. Insight in food microstructure is therefore essential to control the quality of food. In the food factory of the future, flexible and efficient processes require dedicated sensor technology and automated analysis methods. In this context, X-ray computed tomography (XCT) is gaining traction as a non-destructive method to produce extremely detailed images of both internal and external features. Current XCT based analysis of food has a number of limitations however: i) Many microstructural features of food remain invisible due to poor image contrast in soft matter. ii) Visibility and quantification of structure from absorption XCT images strongly depends on image resolution, while relevant sub-resolution size features often remain undetectable. iii) Quality control requires reliable detection and classification methods that should be compatible with process line speeds and dedicated instrumentation that is currently out of reach to the food industry. With phase contrast XCT, images can be acquired with unprecedented contrast far surpassing conventional XCT contrast. This technique was only available at large-scale synchrotron facilities, but recent developments now allow for low brilliance, polychromatic X‐ray sources in lab XCT systems. The applicability to food analysis is however to a large extent unexplored and the 3D inline application is hindered by the long acquisition time. The aim of this project is to overcome these limitations by developing novel (inline) XCT phase contrast acquisition, reconstruction and inspection algorithms specific for the food industry. This will enable us to address issues such as limited visibility of microstructural features, non-detection of sub-resolution size features and incompatibility of reliable detection and classification methods with process line speeds.

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Quantitative edge illumination computed tomography: multi-modal reconstructions from polychromatic sources. 01/10/2020 - 30/09/2022

Abstract

In X-ray computed tomography (XCT), X-ray images of a sample are taken from multiple angles and used to form a 3D reconstruction of the full sample, including many internal features. In a recently rising field in XCT, called phase contrast CT, a specialized set-up is used to obtain a signal that not only holds information on the absorption of the X-rays (as in traditional XCT), but also on the local scattering power in the sample and on the phase shift, a wave property. In the standard phase contrast reconstruction workflow, the acquired data is first separated in an attenuation, differential phase and dark field signal. These signals are then separately reconstructed, using an algorithm derived from traditional XCT, after which the data of the different signals is evaluated as a whole. We focus on two problems in this workflow. First, the signal separation and reconstruction use a linear model, which often does not align with reality. This model assumes a source that sends a single type of X-ray, whereas in a general setting there is a whole spectrum. Secondly, there is a relation between the different signals that are reconstructed, as they all come from the same sample. Currently this is not exploited during the reconstruction. The end goal of this project is to create a model for reconstruction exploiting all phase contrast modalities at the same time, while accounting for the different X-ray energies, such that phase contrast can be used in a quantitative setting.

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D Thermal imaging of people using statistical shape models. 01/10/2020 - 30/09/2022

Abstract

In this project, we will develop an easy to use method to monitor the thermal condition of a person as a function of time, with potential applications entailed in physical treatment or a sports activity. The method employs amongst others thermal imaging. To that end, we create a virtual 3D model of the person of interest. The proposed technique will enable the development of a flexible and mobile measurement system, which can be used in labs, hospitals, rehabilitation centers, sports training facilities, etc.

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FleXCT Service platform. 01/09/2020 - 15/07/2022

Abstract

Using X-ray Computed Tomography (XCT), internal and external characteristics of an object can be visualized in 3D in a non-destructive manner. Medical applications of XCT are well known, but also in other industrial sectors the possible applications of XCT are numerous, such as material characterisation, process and quality control and safety inspection. However, conventional XCT does not penetrate the industry very well, partly because each industrial application requires a specific XCT scan, processing speed and quality, and the XCT equipment available on the market is hardly adapted to these needs. After all, the majority of available CT equipment is very rigid in terms of scan geometry and not cost-efficient. In order to put new, innovative X-ray scanning methods into practice, imec-Visionlab purchased a custom-made X-ray device in 2019: the UniTomXL (Tescan-XRE). The alternative name provided for this device, FleXCT, emphasizes its unprecedented flexibility in terms of possible X-ray geometries for Industrial applications. Moreover, over the past 10 years, imec-Visionlab has developed the ASTRA toolbox with which the recorded X-ray scan can be reconstructed into detailed 3D images. However, in order to quickly respond to industrial XCT requests via an efficient service platform, a high-performance workflow is needed, consisting of 1) FleXCT initialization, 2) FleXCT scanning, 3) 3D image reconstruction, 4) Image visualization and analysis. In this project, the focus will therefore be on the development of such a workflow from customer demand to analysis. For this purpose, we will develop new scanning scripts, seamlessly link the ASTRA toolbox reconstruction algorithms to these scripts, and realize fast visualization and analysis of the reconstructed 3D models via the DragonFly software package (ORS, Canada, www.theobjects.com/dragonfly). Thanks to this new workflow, an efficient service platform will be offered to both academic and industrial partners.

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Upgrade of 9.4T Bruker BioSpec MRI imaging system to Avance NEO hardware architecture. 01/05/2020 - 30/04/2024

Abstract

Upgrade of the hardware of existing equipment (9.4T MRI system from Bruker) to perform state of the art MRI investigations in the brain of small animals such as mice, rats and birds. This hardware upgrade will enable implementation of all new Bruker software packages.

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High speed image processing for realtime control of 3D printers (VIL). 01/04/2020 - 30/03/2022

Abstract

The project aims to improve print quality and reduce waste and cost by in-line real-time monitoring of the melt pool and the product during printing and controlling printing in-the-loop. It aims to produce the first off-axis system based on video analysis.

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Multidimensional analysis of the nervous system in health and disease (µNeuro). 01/01/2020 - 31/12/2025

Abstract

Neuropathological research is an interdisciplinary field, in which imaging and image-guided interventions have become indispensable. However, the rapid proliferation of ever-more inquisitive technologies and the different scales at which they operate have created a bottleneck at the level of integration, a) of the diverse image data sets, and b) of multimodal image information with omics-based and clinical repositories. To meet a growing demand for holistic interpretation of multi-scale (molecule, cell, organ(oid), organism) and multi-layered (imaging, omics, chemo-physical) information on (dys)function of the central and peripheral nervous system, we have conceived μNEURO, a consortium comprising eight established teams with complementary expertise in neurology, biomedical and microscopic imaging, electrophysiology, functional genomics and advanced data analysis. The goal of μNEURO is to expedite neuropathological research and identify pathogenic mechanisms in neurodevelopmental and -degenerative disorders (e.g., Alzheimer's Disease, epilepsy, Charcot-Marie-Tooth disease) on a cell-to-organism wide scale. Processing large spatiotemporally resolved image data sets and cross-correlating multimodal images with targeted perturbations takes center stage. Furthermore, inclusion of (pre)clinical teams will accelerate translation to a clinical setting and allow scrutinizing clinical cases with animal and cellular models. As knowledge-hub for neuro-oriented image-omics, μNEURO will foster advances for the University and community including i) novel insights in molecular pathways of nervous system disorders; ii) novel tools and models that facilitate comprehensive experimentation and integrative analysis; iii) improved translational pipeline for discovery and validation of novel biomarkers and therapeutic compounds; iv) improved visibility, collaboration and international weight fueling competitive advantage for large multi-partner research projects.

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Fiber orientation distribution estimation of fiber reinforced polymers using phase contrast X-ray tomography. 01/01/2020 - 31/12/2023

Abstract

Fiber reinforced polymers (F s) are increasingly used in critical components in the aerospace and automotive industry because of their low weight, strength, and cost effectiveness. Construction of F s requires an in-depth understanding of their microstructure to evaluate the strength and integrity of the composites. High resolution X-ray computed tomography has become the method of choice to investigate the composition and internal structure of F s. Unfortunately, conventional attenuation based X-ray imaging suffers from poor spatial resolution and contrast between the fibers and the polymer matrix. Fortunately, imaging methods have recently become available for lab-X-ray systems that allow to measure the local X-ray scattering (dark field imaging), leading to images with unprecedented contrast complementary to the conventional attenuation contrast. Dark field X-ray imaging is especially useful to image F s as it allows to reconstruct the full scattering profile in each voxel. However, crossing or intertwined fibers within a voxel are hard to disentangle, which makes quantification of distributions of fiber directions challenging. In this project, we will develop new models for superresolution dark field X-ray imaging that allows to quantify F fiber distributions with a subvoxel spatial resolution. This may lead to a better understanding of F properties and ultimately a better design of such materials.

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Adaptive edge illumination-based phase contrast imaging. 01/01/2020 - 31/12/2023

Abstract

In X-ray computed tomography (XCT), X-ray abso tion images of a sample are taken from multiple angles and subsequently used to form a 3D reconstruction of the full sample, based on the attenuation of X-rays. In a recently rising field in XCT, called edge illumination phase contrast CT, a specialized set-up is used to measure, apart from the attenuation, also the local scattering power in the scanned sample and the phase shift of the X-rays. Compared to attenuation, the scatter and phase signals hold complementary information of the scanned sample. Since these signals cannot be measured directly, an absorbing mask (a grating) must be placed in front of the sample and another mask in front of the x-ray detector. In the standard phase contrast imaging workflow, these masks are custom made for a specific imaging geometry and perfectly aligned to each other to achieve the right measurement conditions. The main drawback of this rigid set-up is that geometry changes that are common practice in traditional CT (e.g. zooming in on a sample to optimize the resolution and field-of-view) are not possible. Our aim here is to overcome this limitation by designing novel masks that adapt to geometry changes of the XCT set-up. This fundamental change will open up phase contrast imaging to a much larger variety of sample sizes and at different scales of resolution.

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Flex-CT: a technology platform to evaluate new applications in industrial X-ray CT for inspection and quality control. 01/01/2020 - 31/12/2021

Abstract

A VLAIO COOCK project on novel applications within X-ray CT to inspect different types of materials and objects. MicroCT is a powerful, non-destructive technique for producing high quality 3D images of objects based on a set of X-ray projections. The main aim of the project is to define specific use cases that can be explored using our X-ray CT system (FLEX-CT) within an industrial setting.

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X-ray reconstruction of foam microstructure formation. 01/11/2019 - 30/10/2021

Abstract

Foams are found worldwide in a huge array of products, ranging from food to polyurethane foam (PU foam). However, the physics underpinning the foam formation process is not yet fully understood. A versatile and popular technique to investigate foam structure is micro X-ray computed tomography (microCT). MicroCT is a powerful, non-destructive technique for producing high quality 3D images of static objects based on a set of X-ray projections. In order to visualize the dynamics, a series of subsequent 3D images is traditionally acquired. This approach assumes the object to remain still during the acquisition of a single 3D image. However, in most dynamic imaging situations, this is only approximately valid. Therefore imaging of a fast dynamic processes such as foam formation is currently limited to synchrotron light sources as they are able to acquire a 3D image in the order of a few seconds. Unfortunately, synchrotron beamtime is very limited and experiments are typically queued for 3 to 12 months. This project will therefore focus on improving the image quality of lab-based microCT experiments of PU foam by developing, multimodal (absorption and phase data) 3D and 4D reconstruction algorithms. The key novelty lies in the use of specific prior knowledge about the foam cell shape and its material properties. On the application side my research will facilitate lab experiments and thereby greatly reduce the experiment cycle time in the industry.

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Flanders AI. 01/07/2019 - 31/12/2021

Abstract

The Flemish AI research program aims to stimulate strategic basic research focusing on AI at the different Flemish universities and knowledge institutes. This research must be applicable and relevant for the Flemish industry. Concretely, 4 grand challenges 1. Help to make complex decisions: focusses on the complex decision-making despite the potential presence of wrongful or missing information in the datasets. 2. Extract and process information at the edge: focusses on the use of AI systems at the edge instead of in the cloud through the integration of software and hardware and the development of algorithms that require less power and other resources. 3. Interact autonomously with other decision-making entities: focusses on the collaboration between different autonomous AI systems. 4. Communicate and collaborate seamlessly with humans: focusses on the natural interaction between humans and AI systems and the development of AI systems that can understand complex environments and can apply human-like reasoning.

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Meniscal functionalised scaffold to prevent knee Osteoarthritis onset after meniscectomy (MEFISTO). 01/04/2019 - 30/11/2023

Abstract

MEFISTO will develop two novel solutions to treat meniscus loss as a strategy for preventing the onset of an epidemic of post-meniscectomy knee osteoarthritis (OA) in Europe. Morphological profiling will identify the population of patients who, after meniscal resection, are at higher risk of early compartment degeneration, providing a personalized approach for the patient. The two different reconstructive strategies are: i) a controlled vascularized bioactive resorbable meniscal scaffold which will regenerate the native meniscus. This strategy will be addressed to younger patients with early osteoarthritic changes. ii) a bioactive non-resorbable meniscal prosthesis which will act as a mechanical unloading device and a drug delivery system, with the capacity to modulate the inflammatory environment. This strategy will be addressed to patients with advanced osteoarthritis. A socio-economic analysis of the efficacy of existing meniscal substitutes will complete the project. This analysis is of vital importance for the European healthcare system: it will provide a clear understanding of the costs and benefits of current clinical practice and allow the development of a best practice approach. The technological innovation lies in the development of biologically active functionalized nanobiomaterials that can interact with the surrounding articular tissues. In particular, an innovative meniscal scaffold will promote revascularization in the peripheral zone, while leaving the inner avascular, as happens in the native meniscal tissue. This concept is missing in current therapeutic approaches. The expected potential impact is huge as so many patients have undergone, and will undergo, meniscectomies. The interventions developed in MEFISTO will prevent these patients from receiving joint-sacrificing procedures such as metal prosthesis and reduce the social burden, associated costs and high levels of morbidity resulting from OA.

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Novel methods and 4D-XCT tools for in situ characterisation of materials and their microstructural changes during functional testing. 01/01/2019 - 31/12/2022

Abstract

Fibrous materials are found in biology (e.g. skin, muscle, tendon, ...), but also in industry in the form of composite materials in critical components of the aerospace, automotive and building applications. Not surprisingly, there is a great demand, both clinical and industrial, for an in-depth understanding of the microstructural response of these fibrous materials to external loading parameters defining their elasticity, strength and structural integrity. In this project, a novel experimental 4D characterization toolbox based on X-ray computed tomography (XCT) will be developed, including non-invasive contrast agents and dedicated in situ measurement devices, along with advanced 4D image reconstruction and analysis methods and computational models. Two representative case studies will demonstrate the general applicability of our approach: 3D printed fibre reinforced composites and biological tissues. The proposed 4D characterization approach will allow us to gain crucial insight into the microstructural changes that occur during dynamic functional testing of both types of fibrous materials. In turn, the improved knowledge of the dynamic material behaviour can pave the way towards optimized design and production of novel 3D printed composite materials and towards a more intelligent design of next-generation solutions for tissue restoration and regeneration. The project brings together a multidisciplinary team of experts from three Belgian universities, and will facilitate the translation of the developed 4D characterization toolbox, as well as the individual methodologies, towards industry, hospitals and research centers.

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Spherical deconvolution of high-dimensional diffusion MRI for improved microstructural imaging of the brain. 01/10/2018 - 30/09/2021

Abstract

Multi-tissue spherical deconvolution of diffusion MRI (dMRI) is a popular analysis method that provides the full white matter fiber orientation density function as well as the densities of cerebrospinal fluid and grey matter tissue in the living human brain, completely noninvasively. It can be used to track the long-range connections of the brain and provides a tract-specific biomarker for neuronal loss in the study of neurodegenerative diseases. Currently, the technique can be regarded as a macroscopic approach: it breaks up the dMRI voxels in terms of tissues rather than cellular components, the latter being potentially more relevant biomarkers. Unfortunately, recent studies have demonstrated that conventional low-dimensional dMRI scans lack the information to resolve these microstructural features. In this proposal, I will take multi-tissue spherical deconvolution to the next (microscopic) level by leveraging high-dimensional dMRI scans. These next-generation scans have shown great promise to disentangle different microstructural compartments. The new multi-compartment spherical deconvolution approach will allow simultaneous estimation of a high quality axonal orientation density function as well as the densities of cell bodies and extracellular space. This will enable high-quality fiber tracking and at the same time provide more relevant biomarkers, and will help spherical deconvolution to maintain its position as one of the go-to tools for dMRI analysis.

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Multiscale, Multimodal and Multidimensional imaging for Engineering (Mummering). 01/01/2018 - 31/12/2021

Abstract

The overarching goal of MUMMERING is to create a research tool that encompasses the wealth of new 3D imaging modalities that are surging forward for applications in materials engineering, and to create a doctoral programme that trains 15 early stage researchers (ESRs) in this tool. This is urgently needed to prevent that massive amounts of valuable tomography data ends on a virtual scrapheap. The challenge of handling and analysing terabytes of3D data is already limiting the level of scientific insight that is extracted from many data sets. With faster acquisition times and multidimensional modali-ties, these challenges will soon scale to the petabyte regime. To meet this challenge, we will create an open access, open source platform that transparently and efficiently handles the complete workflow from data acquisition, over reconstruction and segmentation to physical modelling, including temporal models, i.e. 3D "movies". We consider it essential to reach this final step without compromising scientific standards if 3D imaging is to become a pervasive research tool in the visions for Industry 4.0. The 15 ESRs will be enrolled in an intensive network-wide doctoral training programme that covers all aspects of 3D imaging and will benefit from a varied track of intersectoral secondments that will challenge and broaden their scope and approach to research.

Researcher(s)

Research team(s)

Breakthroughs in Quantitative Magnetic resonance ImagiNg for improved Detection of brain Diseases (B-Q MINDED). 01/01/2018 - 31/12/2021

Abstract

Magnetic resonance imaging (MRI) is one of the most useful and rapidly growing neuroimaging tools. Unfortunately, signal intensities in conventional MRI images are expressed in relative units that depend on scanner hardware and acquisition protocols. While this does not hinder visual inspection of anatomy, it hampers quantitative comparison of tissue properties within a scan, between successive scans, and between subjects. In contrast, advanced quantitative MRI (Q-MRI) methods like MR relaxometry or diffusion MRI do enable absolute quantification of biophysical tissue characteristics. Evidence is growing that Q-MRI techniques detect subtle microscopic damage, enabling more accurate and early diagnosis of neurodegenerative diseases. However, due to the long scan time required for Q-MRI, causing discomfort for patients and limiting the throughput, Q-MRI methods have not entered clinical practice yet. B-Q MINDED aims to overcome the current barriers by developing widely-applicable post-processing breakthroughs for accelerating Q-MRI. The originality of B-Q MINDED lies in its ambition to replace the conventional rigid multi-step processing pipeline with an integrated single-step parameter estimation framework. This approach will unlock a wealth of options for optimization of Q-MRI. To accomplish this goal, B-Q MINDED proposes a collaborative cross-disciplinary approach (from basic MR physics to clinical applications) with strong involvement of industry.

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Project website

3D deformable motion reconstruction from fluoroscopy images based on articulating statistical shape and intensity models. 01/01/2018 - 31/12/2021

Abstract

Motion patterns are crucial markers for the health of a horse. The lack of accurate motion analysis systems leads to subjective diagnoses with a negative effect on the treatment outcome. The current most accurate systems are based on X-ray radiographs of the subject during motion. The 3D reconstruction from the planar images requires a prior CT-scan of the subject. The techniques are not suitable for medical applications because they require placement of invasive markers into the subject and require a lot of manual processing. Moreover, they can not handle deformable motions. As a result, the cushions on horse hoofs which deform during landing, can not be imaged. We propose a motion reconstruction technique that replaces the CT-scan by a statistical shape and intensity model. Omitting a CT-scan reduces examination costs, time and radiation dose. A statistical model describes the variation in shape and densities present within the population. To describe moving subjects, such a model needs to be articulated over time. This will be extended with a model for the deformable dynamics of soft tissues. The motion reconstruction technique will autonomously find the right shape and pose of the model based on the X-ray images by comparing them with a simulated X-ray image of the model. The novel technique will serve as an objective diagnostic tool for diagnosis, follow-up and validation of innovative orthopedic products by means of motion analysis, both for animals and humans.

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Research team(s)

Next generation X-ray metrology for meeting industry standards (MetroFlex). 15/09/2017 - 14/09/2021

Abstract

Advanced manufacturing techniques, often based on computer aided design (CAD) models, are transforming the industrial landscape and offer exciting opportunities for producing tailor-made products with high added-value. At the same time, specifications and quality standards of end products are stringent and therefore sophisticated inspection tools are needed. In an industry 4.0 perspective, inspection occurs preferably inline to enable a rapid remediation of disturbances causing material defects and/or dimensional deviations. Hence, there is a growing demand for fast and flexible 3D metrology solutions in the factories of the future. In this context, X-ray computed tomography (CT) is gaining traction as a non-destructive method to produce extremely detailed images of both internal and external features of complex objects. However, conventional CT inspection approaches typically require many (several hundreds) X-ray projection images from a large number of viewing angles and subsequently a full 3D image reconstruction is performed. This results in a number of limitations: i) due to the lengthy acquisition and reconstruction process, CT is typically performed for offline inspections and R&D activities. Real-time inline CT scanning to achieve a 100% dimensional metrology inspection rate is not possible with the current CT systems. ii) conventional CT systems have a rigid well-defined setup, i.e. requiring either that the object can be put inside the scanner or that the source-detector system can physically rotate 360° around the object. As a result, larger objects such as a wing of an airplane or a partly assembled car cannot be scanned. iii) 3D reconstructed images may suffer from numerous artefacts (due to misalignment, beam hardening, etc.) while the traceability and uncertainty of CT measurements for metrology applications is insufficiently documented. In this project, we propose a radical paradigm shift by breaking with the traditional X-ray 3D metrology workflow through developing a new framework for 3D metrology that addresses the above mentioned problems. If successful, this SBO project will result in a flexible X-ray metrology toolkit to enable fast inline QC during production and to perform inspection tasks of larger parts. The identification of hidden defects and deviations from the nominal geometry during production will help to produce high quality products, as efficiently as possible and with a minimum of waste.

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Research team(s)

Spaceflight induced neuroplasticity studied with advanced magnetic resonance imaging methods (BRAIN-DTI). 01/01/2016 - 31/12/2021

Abstract

Advanced methods in Magnetic Resonance Imaging, such as resting state functional MRI (rfMRI) and Diffusion Tensor Imaging (DTI) will be used to study the effect of microgravity on the adaptive processes in the brain in astronauts. Preand post-flight data will be collected to elucidate changes in structural and functional brain wiring due to microgravity.

Researcher(s)

Research team(s)

Spaceflight induced neuroplasticity studied with advanced magnetic resonance imaging methods (BRAIN-DTI). 01/01/2012 - 31/12/2021

Abstract

Advanced methods in Magnetic Resonance Imaging, such as resting state functional MRI (rfMRI) and Diffusion Tensor Imaging (DTI) will be used to study the effect of microgravity on the adaptive processes in the brain in astronauts. Preand post-flight data will be collected to elucidate changes in structural and functional brain wiring due to microgravity.

Researcher(s)

Research team(s)

  • Lab for Equilibrium Investigations and Aerospace (LEIA)

B budget True Atom (True atom probe tomography for semiconductor) 2020. 01/01/2020 - 31/12/2020

Abstract

Atom probe tomography is an analysis tool in materials science that allows to inspect the 3D chemical composition of needle shaped samples at the nano scale. The method works by field-induced evaporation. Ions are then consecutively emitted from the apex of the needle and are absorbed by a position sensitive detector. The result is a tomographic, atomically resolved image of the evaporated volume, represented as a point cloud in which each point is an atom. The current reconstruction approaches however were developed with homogeneous samples in mind and do not account for the complex shape of the sample surface, which evolves during the field evaporation process. The goal of this project is to develop new reconstruction methods that take the shape into account.

Researcher(s)

Research team(s)

A budget 2020 IMEC. 01/01/2020 - 31/12/2020

Abstract

The ASTRA toolbox is an open source platform for tomographic reconstruction. In this project, extensions for the ASTRA toolbox are developed. These include refractive imaging such as TeraHertz tomography.

Researcher(s)

Research team(s)

Development of an inline inspection software platform to facilitate the ΔRAY spin-off creation. 01/10/2019 - 30/09/2020

Abstract

There is a widespread rising need from industry to move towards 100% non-destructive inline inspection and quality control. The main challenge in X-ray based inspection is to go beyond classical X-ray radiography image processing and make the step towards fast and robust 3D inspection. This challenge is rooted in the difficulty of disentangling the 3D spatial information that is encrypted in the X-ray radiographs. imec-Vision Lab has developed methodology that can enable the introduction of high throughput inline tomography for industrial quality control. In this project we aim to push this technology past TRL4 through the development of a computationally efficient and more robust software platform, which can greatly facilitate the creation of a spin-off.

Researcher(s)

Research team(s)

Quantitative X-ray tomography of advanced polymer composites. 15/07/2019 - 14/07/2020

Abstract

Advanced composite materials (ACMs) typically contain two or more constituents, such as matrix, fibers, and pores, with different physical and chemical characteristics. When combined, they produce a material with unique properties in terms of weight, strength, stiffness, or corrosion resistance. To inspect and study their 3D internal structure in a non-destructive way, the ACMs are imaged with X-rays, after which a 3D image is reconstructed from the X-ray radiographs, and further processed and analyzed in multiple sequential steps. This conventional workflow, however, suffers from inaccurate modeling and error propagation which severely limits the accuracy with which ACM parameters of interest can be estimated. In this project, we will develop a paradigm shifting approach in which the quantification of ACM parameters is substantially improved. This will be realized in a novel workflow by 1) accurately modelling all constituents of the ACM (matrix, pores, and fibers); 2) directly estimating the ACM model parameters from the X-ray radiographs, thereby preventing error propagation by providing a feedback mechanism; 3) analyzing the workflow's parameter space with respect to sensitivity and stability of parameters of interest. In this project, we develop methods that quantify ACM parameters, by targeting a new workflow for 1) accurately modeling all components of the ACM (matrix, pores and fibers); 2) estimating directly the parameters of the ACM model of the X-rays, thus preventing error propagation.

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Research team(s)

SHASIZE: a predictive tool based on statistical shape modeling for accurate clothing size prediction. 01/04/2019 - 30/09/2019

Abstract

Clothing webshops have to deal with a large number of returns because the customer orders the wrong size, so a lot of money (€600 billion worldwide) is lost. SHASIZE aims to create a true-to-life virtual mannequin, based on a few simple input parameters (length, weight, circumferences). This mannequin determines how well the garment fits.

Researcher(s)

Research team(s)

B-budget Tera Tomo (Terahertz imaging with imec technology) 2019. 01/01/2019 - 31/12/2019

Abstract

Terahertz radiation is non-ionizing and can be used for 3D inspection. In this project, new reconstruction methods are developed for Terahertz tomography. The THz beam is modelled and incorporated into iterative reconstruction methods.

Researcher(s)

Research team(s)

B budget True Atom (True atom probe tomography for semiconductor) 2019. 01/01/2019 - 31/12/2019

Abstract

Atom probe tomography is an analysis tool in materials science that allows to inspect the 3D chemical composition of needle shaped samples at the nano scale. The method works by field-induced evaporation. Ions are then consecutively emitted from the apex of the needle and are absorbed by a position sensitive detector. The result is a tomographic, atomically resolved image of the evaporated volume, represented as a point cloud in which each point is an atom. The current reconstruction approaches however were developed with homogeneous samples in mind and do not account for the complex shape of the sample surface, which evolves during the field evaporation process. The goal of this project is to develop new reconstruction methods that take the shape into account.

Researcher(s)

Research team(s)

A budget 2019 IMEC. 01/01/2019 - 31/12/2019

Abstract

The ASTRA toolbox is an open source platform for tomographic reconstruction. In this project, extensions for the ASTRA toolbox are developed. These include refractive imaging such as TeraHertz tomography.

Researcher(s)

Research team(s)

Quantitative edge illumination computed tomography: multi-modal reconstructions from polychromatic sources. 01/10/2018 - 30/09/2020

Abstract

In X-ray computed tomography (XCT), X-ray images of a sample are taken from multiple angles and used to form a 3D reconstruction of the full sample, including many internal features. In a recently rising field in XCT, called phase contrast CT, a specialized set-up is used to obtain a signal that not only holds information on the absorption of the X-rays (as in traditional XCT), but also on the local scattering power in the sample and on the phase shift, a wave property. In the standard phase contrast reconstruction workflow, the acquired data is first separated in an attenuation, differential phase and dark field signal. These signals are then separately reconstructed, using an algorithm derived from traditional XCT, after which the data of the different signals is evaluated as a whole. We focus on two problems in this workflow. First, the signal separation and reconstruction use a linear model, which often does not align with reality. This model assumes a source that sends a single type of X-ray, whereas in a general setting there is a whole spectrum. Secondly, there is a relation between the different signals that are reconstructed, as they all come from the same sample. Currently this is not exploited during the reconstruction. The end goal of this project is to create a model for reconstruction exploiting all phase contrast modalities at the same time, while accounting for the different X-ray energies, such that phase contrast can be used in a quantitative setting.

Researcher(s)

Research team(s)

Infrastructure for imaging nanoscale processes in gas/vapour or liquid environments. 01/05/2018 - 30/04/2021

Abstract

Processes in energy applications and catalysis as well as biological processes become increasingly important as society's focus shifts to sustainable resources and technology. A thorough understanding of these processes needs their detailed observation at a nano or atomic scale. Transmission electron microscopy (TEM) is the optimal tool for this, but in its conventional form it requires the study object to be placed in ultrahigh vacuum, which makes most processes impossible. Using environmental TEM holders, the objects can be placed in a gas/vapour or liquid environment within the microscope, enabling the real time imaging, spectroscopic and diffraction analysis of the ongoing processes. This infrastructure will enable different research groups within the University of Antwerp to perform a wide range of novel research experiments involving the knowledge on processes and interactions, including among others the growth and evolution of biological matter, interaction of solids with gasses/vapours or liquid for catalysis, processes occurring upon charging and discharging rechargeable batteries, the nucleation and growth of nanoparticles and the detailed elucidation of intracellular pathways in biological processes relevant for future drug delivery therapies and treatments.

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Research team(s)

FleXray: Flexible X-ray imaging for the next generation of tomographic applications. 01/05/2018 - 30/04/2021

Abstract

PC-CT reveals complementary information to traditional attenuation based X-ray imaging (i.e. higher contrast in soft tissue). The FleXray system will allow us to acquire data to fully explore a far wider range of applications and opportunities for PC-CT that are currently not possible: ● Exploration of advanced CT acquisition models to enable reconstruction from (1) fewer projection images and (2) projection images acquired during continuous sample rotation. This will result in faster PC-CT imaging (currently up to 8 times longer than regular CT). ● Dark field tomography is only in its infancy but recently showed huge potential in material characterisation. The FleXray system will open new research lines on dark field tomography, in particular in accurate and precise estimation of localized scattering profiles. ● Development of Krylov solvers with much faster convergence for simultaneous multimodal reconstruction of full 3D images of attenuation, phase and dark field signals.

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Research team(s)

Smart Light. 01/01/2018 - 31/12/2020

Abstract

The purpose of Smart*Light is to develop a compact electron accelerator that is able to function as a source of highly brilliant inverse Compton source of X-rays, with the possibility to tune the energy of the radiation.

Researcher(s)

Research team(s)

B budget 2018 IMEC. 01/01/2018 - 31/12/2018

Abstract

Atom probe tomography (APT) is a chemical analysis technique that provides a three-dimensional atom distribution of a measured specimen. A sharpened specimen is placed into a vacuum chamber and aligned to the center of an ion detector with a high voltage bias applied between the tip and the detector. A high electric field (about > 10 V/nm) is then formed at the apex of the tip, while the atoms at the surface of the apex are ionized and the intensity of the electric field is close to the threshold of breaking atomic bonds. For analyzing low conductive materials, a continuous pulsing laser is commonly introduced as a supplement of the thermal energy which helps ions at the apex to overcome the energy barrier of evaporation. Evaporated ions are detached from the tip surface and are accelerated toward the detector according to the electric field distribution between the tip and the detector. The impact position on the detector and the travelling time, as named time-of-flight (TOF), from emission to detection are measured. It is noteworthy that, with the limited size of a detector, only those ions in the field-of-view (FOV) will reach the detector. Moreover, because of the detector efficiency, only 50-70% of the ions that reach the detector will be recorded. These effects cause significant uncertainties on determining the volume for a reconstruction. In this project we will develop novel reconstruction methods for APT.

Researcher(s)

Research team(s)

A budget 2018 IMEC. 01/01/2018 - 31/12/2018

Abstract

The ASTRA toolbox is an open source platform for tomographic reconstruction. In this project, extensions for the ASTRA toolbox have been developed. These include refractive imaging such as TeraHertz tomography.

Researcher(s)

Research team(s)

Enabling Computer Aided Diagnosis of Foot Pathologies through the use of Metric Learning (CAD WALK). 01/10/2017 - 30/09/2019

Abstract

Dynamic plantar pressure imaging (PPI) refers to the measuring, across time, of pressure fields between the foot and the ground. PPI is used, in part, to diagnose foot problems such as metatarsalgia and plantar fasciitis. Despite the widespread clinical use of PPI, its diagnostic potential has not been fully exploited. PPI creates large and dynamic datasets that cannot be easily analysed and interpreted by the human brain. As a result, PPI images are subsampled before being clinically examined, which discards potentially valuable information. The objective for this action is to improve the diagnostic value of PPI through the introduction of a computer-aided diagnosis (CAD) system called CAD WALK.

Researcher(s)

Research team(s)

Dynamic imaging for segmentation and computational modelling of the heart (DIASTOLE). 01/06/2017 - 30/11/2019

Abstract

Cardiac imaging plays an important role in the detection of pathologies of the heart, including coronary and valvular heart disease. It is also increasingly used for planning of complex surgery, and for the patient-specific fitting of medical implants such as artificial valves. Up till recent, dynamic imaging of the heart motion was limited to fast ultrasound (US) imaging, or MRI and CT limited to 2D or a reduced axial field of view. The advent of wide-area detectors with high tube rotation speeds has now enabled acquiring CT volumes covering the entire heart, several times per second. Dynamic or 4D (3D+T) CT is of great promise to clinical cardiac imaging. The modality is particularly suited for applications requiring image processing such as physics-based modelling, in which models of the anatomy are extracted from the image as a starting point for computer simulations. In comparison to US, CT offers a larger field of view and superior signal to noise ratio, making it far better suited for whole-heart segmentation and geometric modelling. Conversely, 4D US offers superior temporal resolution, and provides greater detail on fine structures such as heart valves. Combining dynamic CT with US, would allow benefiting from the advantages of both modalities, and could lead to a robust and accurate workflow for extracting detailed, patient-specific information on heart anatomy and motion. Inclusion of 4D models in physics-based modelling could bring such simulations to a new level of realism, enabling their use for planning of complex interventions and in-silico trials of cardiovascular devices affected by motion. In term, this will reduce the uncertainties associated with such interventions through more accurate device sizing and positioning, and accelerate the development of novel cardiovascular implants. The DIASTOLE consortium aims to develop a novel 4D workflow for performing physics-based simulations for cardiovascular procedures in a dynamic environment, using patient-specific parametric models of the heart and main arteries, obtained from dynamic CT and US.

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Research team(s)

Breakthroughs towards high-resolution MR relaxometry within a clinically acceptable acquisition time for improved detection of brain diseases. 01/01/2017 - 31/03/2021

Abstract

Magnetic resonance imaging (MRI) is one of the most used neuroimaging techniques. Unfortunately, signal intensities in conventional MRI images are expressed in relative units that are dependent on hardware and software. This does not hinder visual inspection of anatomy, but severely complicates quantitative comparisons of the signal intensity within a scan, between successive scans, and between subjects. In contrast, MR relaxometry is an MRI technique that generates quantitative maps of absolute biophysical tissue characteristics (Deoni et al., 2010). Evidence is growing that MR relaxometry detects subtle microscopic tissue damage, which could lead to earlier diagnosis of various brain diseases including multiple sclerosis (Vrenken et al., 2006; Roosendaal et al., 2009 and Papadopoulos et al., 2010). Conventional MR relaxometry techniques, however, inherently require long scan times that impede the introduction in clinical practice. From a diagnostic perspective, long scan times increase the likelihood of motion artefacts, whereas from an economical perspective they reduce the throughput. In addition, long scan times cause discomfort for patients. For these reasons, MR relaxometry hasn't convinced the radiology community yet. The current project proposal aims to overcome these barriers by developing a radically new widely-applicable technological framework for accelerating MR relaxometry. At the end of this IOF SBO project, the feasibility and validity of our new approach for accelerated MR relaxometry will have been demonstrated. For final translation of the technology towards the market (and patients) we will team-up will industrial partners. Moreover, three companies (two MRI vendors and one specialized SME) already agreed to join the Industry Advisory Board and will support the project by providing early feedback. Finally, from a strategic perspective, this project bridges fundamental MR physics with applied bio-medical neuroimaging-MRI research. As such the project promotes cross-fertilization between the three Antwerp MRI-research groups (and faculties) involved. Hence, this research will enforce the mission and ambition of the University of Antwerp and its IOF consortium (Expert Group Antwerp Molecular imaging, EGAMI-image) to develop an IP portfolio and a strong translational and integrated MRI research program.

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Research team(s)

Validation of the piglet as animal model for deficient motor development : the paradigm of locomotion. 01/01/2017 - 31/12/2020

Abstract

Advances in antenatal medicine and neonatal intensive care have resulted in improved survival of human infants born with a low birth weight and at the limits of viability, but not in the reductions of motor deficits. Locomotor skills are essential for participation in all daily activities and therefore are paradigmatic for insights in motor development in general. Longitudinal experimental designs studying locomotion are needed to elucidate the contributions of intra-uterine growth restricted development of the musculoskeletal and the nervous system onto the motor deficits. Such fundamental longitudinal experiments are ethically controversial in human infants, necessitating appropriate animal models for research. In modern sows, piglets born with a low birth weight and low viability frequently occur. These piglets show characteristics of underdevelopment similar as those seen in human infants with a low birth weight and viability. This, together with their high physiological resemblance, makes the pig an ideal model to study the development of growth-impaired locomotion. This project characterizes and compares the longitudinal development of locomotion in the normal and low birth weight piglet. To this purpose we make use of 4D-morphology, dynamic mechanical modelling and functional morphological analyses (cfr. the concept of neuromechanics). This requires the technological development of rapid 3D dual energy tomography (including soft tissue reconstructions) integrated in the existing 3D²YMOX-platform (biplane X-ray). Differences in both coordination and control will be linked to changes at the level of the musculoskeletal, as well as the neurological components of the locomotor system.

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Research team(s)

InLocoMotion: Dynamic 3D human body shapes from static 3D scans and sparse motion tracking for the improvement of human-product systems: a case on cycling drag force estimation 01/01/2017 - 31/12/2020

Abstract

The human body is a complex biomechanical system with a large anatomical diversity. New methods for industrial design are emerging based on accurate 3D models and statistical analysis of their rich spatial geometry and complex variations. Most applications of this 3D anthropometry in the field of Product Development are confined to static 3D shapes, whereas many products such as garments, (space) suits, sports equipment, medical devices, vehicles, and household appliances might benefit from accurate dynamic deforming 3D models of the human body. Currently, even for products that dynamically interact with the human body (e.g. shoes), only static geometric information is considered, thereby ignoring the potential to consider full 3D surface in motion and dynamic deformation. In this Baekeland PhD project, we will construct and validate design methods to use dynamic 3D anthropometry in the process of product development and extend the use of static 3D anthropometry. We will combine the aforementioned state-of-the-art statistical shape models with state-of-the-art animation techniques and translate them to CAD tools and techniques to support the envisioned extension. Firstly, a method is provided to generate any individual 3D body shape in any position from a combination of geometrical shape information and temporal position parameters that is both easy to assess. Shape information will comprise an individual's shape in a static pose, e.g. standing position, or a set of 1D anthropometric parameters. Position parameters will be achieved by adapting reliable and accurate of-site motion capturing techniques. We will also investigate how product developers might use these parameterized person-specific dynamic 3D models in the process of product development i.e., what shape and position information they need during the design process and what the requirements are on that information such that they will use it most effectively. This will pinpoint how product developers will preferably interact with the envisioned human-product models. Next, these requirements will be used to develop CAD tools and techniques in which products can be designed on person-specific dynamic human body models, and resulting human-product models can be tuned and optimized by a anthropometric measurements and position parameters. For instance, a stack of person-specific human-product models can be generated with the same effort required to generate only one such model. Finally, we will validate our method by simulating drag force of cyclists, in comparison to ground truth values in a wind tunnel. The target is to come very close to real drag force values with a fraction of the cost and investment. Although this PhD will directly contribute to the subfield of aero-design and engineering in cycling, the lead up methods will also prove the accuracy of underlying models. We will thus establish a direct and accurate link for the product developer between human(-product) CAD models and the actual physical model to support simulation, verification and validation. Our method will improve the process of product development in several aspects. It will have the potential to reduce development costs by omitting the need for physical prototyping. An early stage verification of product functionality and composition of design specifications will require less iterations and entail a shorter time to market for new products. Our method will not only enhance comfort and functionality of final products but will also allow to develop new categories of consumer and medical wearable products, that owe their functionality to close and dynamic product-body interaction and extensive ergonomics.

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Research team(s)

High performance iterative reconstruction methods for Talbot‐Lau grating interferometry based phase contrast tomography. 01/01/2017 - 31/12/2020

Abstract

Phase Contrast X‐ray Computed Tomography (CT) measures besides the intensity also changes in the phase of a transmitted X‐rays. These changes give exquisite and complementary information about the object, in particular about soft tissues. More and more CT systems are able to measure these phases. However, the development of efficient mathematical reconstruction algorithms that reconstruct the 3D object from the measured data is only in its early stages. This project will make progress in the modelling of the data acquisition process and the reconstruction algorithms. It is a collaboration between the group A pplied Mathematics and the V ision Lab . Valorisation will be realized by the distribution of the new algorithms through the ASTRA toolbox and the initiation of research collaborations, licensing deals and contract research with industry.

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Research team(s)

Blended relaxometry/diffusion MRI: a one-stop-shop approach. 01/01/2017 - 31/12/2020

Abstract

Magnetic resonance imaging (MRI) is a rich and versatile imaging method able to generate images of the human body with different contrasts in a non-invasive and harmless way. Among the most popular MRI methods are diffusion MRI (dMRI) and MRI relaxometry: dMRI quantifies the mobility of water molecules, while relaxometry quantifies magnetization transfer in tissues. Acquiring both dMRI and relaxometry images of the brain is highly desirable for the following reasons: 1) both modalities provide complementary information about the brain's micro-structure and hence provide important biomarkers for brain pathologies; 2) while dMRI parameters are sensitive to brain pathologies, dMRI suffers from low specificity. Acquiring additional relaxometry data allows increasing this specificity. Unfortunately, acquiring diffusion and relaxometry parameters with sufficient resolution and precision in one imaging protocol is hardly feasible mainly due to time constraints. In this project, we will develop a paradigm shifting imaging protocol with accompanying parameter estimation framework to acquire diffusion and relaxometry parameters simultaneously. The main goal is to obtain a fingerprint image that reveals quantitative diffusion and relaxometry information in a one-stop-shop acquisition. This will not only allow to extract valuable biomarkers, but also to increase the precision and accuracy with which these parameters can be estimated in a clinically acceptable acquisition time.

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Research team(s)

Quantitative X-ray tomography of advanced polymer composites. 01/01/2017 - 31/12/2019

Abstract

Advanced composite materials (ACMs) typically contain two or more constituents, such as resin, fibers, and pores, with different physical and chemical characteristics. When combined, they produce a material with unique properties in terms of weight, strength, stiffness, or corrosion resistance. To inspect and study their 3D internal structure in a non-destructive way, the ACMs are imaged with X-rays, after which a 3D image is reconstructed from the X-ray radiographs, and further processed and analyzed in multiple sequential steps. This conventional workflow, however, suffers from inaccurate modeling and error propagation which severely limits the accuracy with which ACM parameters of interest can be estimated. In this project, we will develop a paradigm shifting approach in which the quantification of ACM parameters is substantially improved. This will be realized in a novel workflow by 1) accounting for possible deformation of the ACM during scanning, thereby reducing image reconstruction artefacts; 2) accurately modelling all constituents of the ACM (matrix, pores, and fibers); 3) directly estimating the ACM model parameters from the X-ray radiographs, thereby preventing error propagation by providing a feedback mechanism; 4) analyzing the workflow's parameter space with respect to sensitivity and stability of parameters of interest.

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Research team(s)

Reconstruction services. 07/12/2016 - 22/12/2016

Abstract

In this collaboration, specific reconstruction methods are being developed for extraction of quantitative information from X-ray CT images. The methods are validated on various experimental computed tomography datasets.

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Research team(s)

Knowledge and technology platform for customized design and 3D printing of ortheses (PLATO). 01/10/2016 - 30/09/2018

Abstract

We aim to create a knowledge and technology platform to promote the use of budget, low-resolution clinical scans for the production of customised lower-arm orthoses (splints) using 3D printing technology. This platform will combine statistical shape and pose models of the hand with parametric CAD design and a unique new material, resulting in an innovative splint workflow. Creating the envisioned knowledge and technology platform will steer the future of health services towards a remote, worldwide accessible patient care, offering splints at an affordable price.

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Research team(s)

Industrial X-ray CT for high throughput quality control (iXCON). 01/10/2016 - 30/09/2018

Abstract

Across the food and manufacturing sectors, internal product defects or features related to local density differences (breakdown of tissues in fruit, cracks, badly glued seals,...) are often next to impossible to detect by conventional inline ('at-conveyor-belt') sensor technologies. These technologies provide only a surface evaluation (e.g., camera systems), a partial volume analysis (e.g., near infrared), 2D images of the product interior (e.g., X-ray radiography) or the chance of detection depends strongly on the viewing angle. Volumetric (3D) imaging can resolve such features and locate them in the product in a non-destructive way by means of X-ray computed tomography (CT). However, while conventional CT systems allow full 3D analysis, they are (1) too slow, (2) too expensive or (3) not adapted to inline applications. Today, the lack of adequate volumetric quality control in the agricultural industry results in high rejection rates (between 5 and 10% in some sectors), mostly after destructive random sampling, resulting in entire batches being removed from the supply chain. Moreover, it is also important to stress that the lack of volumetric 3D data impedes the automation in this sector. Economic stakes are therefore high. With iXCon we plan to establish a break-through in high throughput industrial quality control of products in the agricultural processing and manufacturing industry. We aim to achieve this by designing a prototype X-ray imaging system suitable for high-throughput inline imaging with the ability to perform full 3D volumetric analysis. Integrated analysis methodology will combine X-ray and sensor data (i.e. optical, laser, thermal) with prior knowledge (i.e. statistical shape or CAD models) to allow for fast 3D quality control of a level that is until now unachievable by the state-of-the-art methods.

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Research team(s)

Advanced multimodal data analysis and visualization of composites based on grating interferometer micro-CT data (ADAM). 01/03/2016 - 28/02/2019

Abstract

In summary, the main goals pursued in the scope of ADAM project are: • To develop of advanced tomographic reconstruction methods for TLGI data, generating high quality reconstructions even from a limited number of projection angles and for directly estimating the material parameters of interest • To develop data fusion techniques, combinational and comparative visualization techniques enabling data overviews and detailed inspections, as well as visual analysis techniques for AC, DPC, and DFC-data of fiber-reinforced composites including bi- and multidirectional TLGI XCT data (a specimen is scanned two or more times with different orientations in order to acquire complete refraction information in all directions). A further requirement is that these techniques need to be capable of smart handling of the large TLGI XCT dataset sizes. • To evaluate the research results and to demonstrate the developed methods in a software prototype. • To disseminate the research results and acquired knowledge in order to foster the adoption of TLGI XCT inspection in industry; providing commercialization possibilities to the industrial partners and beyond.

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Turning images into value through statistical parameter estimation 01/01/2016 - 31/12/2020

Abstract

Purpose of this research community is to turn images into value by means of statistical parameter estimation. It targets to promote interdisciplinary quantitative imaging in the domain of physics, medical imaging and statistics.

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Decision support system for objective nasal airway obstruction assessment using computational fluid dynamics 01/01/2016 - 31/12/2020

Abstract

Surgery is often treatment of choice for nasal airway obstruction caused by anatomic abnormalities. Objective measures for nasal patency, such as rhinometry, correlate poorly with patient's symptoms and long-term satisfaction rates are low. In this project we develop a decision support system using patient-specific computational fluid dynamics models as an objective assessment tool in clinics. Model geometry is based on statistical shape models fitted to tomographic data.

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A superresolution framework for quantitative brain perfusion map estimation using Arterial Spin Labelling 01/01/2016 - 31/12/2019

Abstract

Perfusion magnetic resonance imaging (MRI) is an imaging tool to assess the spatial distribution of microvascular blood flow. Many neurological disorders are accompanied by cerebral blood flow (CBF) alterations, which makes perfusion MRI indispensable in routine clinical practice. Arterial spin labeling (ASL) perfusion MRI uses magnetically labeled arterial blood water as an endogenous diffusible tracer. Tissue perfusion is measured from the signal difference between images with labeled blood and control images. Lack of ionizing radiation, complete non-invasiveness, and absolute quantification of perfusion parameters make ASL a unique perfusion imaging modality. Current ASL methods, however, suffer from problems such as noisy images and patient movement, which are inherent to the acquisition process. My project aims to develop a framework that incorporates new ASL acquisition and reconstruction methods targeting these problems simultaneously. The core of this framework revolves around super resolution reconstruction (SRR) ASL imaging which allows direct estimation of high-resolution perfusion parameters from a set of differently sampled low-resolution images. Results will yield a patient-friendly, cost-efficient and quantitative protocol that allows accurate and precise perfusion measurement at increased resolution in a clinically acceptable acquisition time, by that removing the main obstacles for ASL to become the golden standard for perfusion measurements.

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Research team(s)

Generalised spherical deconvolution of diffusion MRI data for improved microstructural specificity and higher resolution imaging of white matter. 01/10/2015 - 30/09/2018

Abstract

Spherical deconvolution (SD) of diffusion-weighted MRI (DW-MRI) is a popular analysis method that allows extraction of white matter (WM) fibre orientation information in the living human brain, completely noninvasively. It can be used to track the long-range connections of the brain or serve as a tract-specific biomarker for neuronal loss in the study of neurodegenerative diseases. Recently, I proposed a new analysis method based on SD that models the presence of non-WM tissue in voxels, which was previously unaccounted for, enabling unprecedented tractography and quantification of WM. However, significant challenges remain, preventing SD from realizing its full potential: * The current approach models the signal arising from the three macroscopic tissue types. With a new approach, I want to take this to the microscopic level, taking into account the presence of axons, cell bodies and extracellular water. This will improve current neuronal fibre estimates and will introduce new quantitative measures that can be used as biomarkers in the study of neurodegenerative diseases. * Clinical scans are limited in spatial resolution due to constraints on scan time and signal-to-noise ratio (SNR). However, the very fine structures of the WM, and particularly the intricate folding patterns of the cortical surface, require high spatial resolution. I propose a new SD algorithm that can obtain high-resolution fibre information, with adequate SNR and within a practical acquisition time.

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Statistical foot modeling for a digital orthotics workflow (FOOTWORK). 01/10/2015 - 30/09/2017

Abstract

FOOTWORK's innovation is to make the digital orthotics workflow robust and automated by employing statistical foot models as prior knowledge in every phase of the workflow. Measurement phase: Statistical foot models will increase the robustness of 3D scanning wrt. noise, motion, and occlusions and allowing the use of low-cost scanners to obtain 3D accurate patient-specific foot models. Analysis phase: Statistical foot models will objectify and automate the identification of foot type and pathology based on the patient's 3D foot shape and dynamic plantar pressure. Orthotic modeling phase: Statistical foot models will enable automated and consistent design of the orthotic based on the measurements, resulting in a faster and more reliable process. In addition to these technical innovations, the consortium will also target the development of an innovative online digital workflow support platform that can be used in multiple settings, e.g. retail, orthopedics, or academic research. These innovations target a more effective and efficient digital orthotics workflow by eliminating much of the operator interaction and subjective human factors. As a result, the application of a digital orthotics workflow on a large scale becomes very attractive. Such a digital process results in faster, more reliable, and more pleasant service for the customer. It is also more economical and ecological as it involves less logistics and less material waste, keeping production in Flanders feasible. Furthermore, the opportunity to separate patient measurement from further processing allows high-tech orthopedic expert centers in Flanders to efficiently serve an international market. In that way, a larger market can be addressed more efficiently.

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Research team(s)

A super-resolution framework for quantitative brain perfusion mapping with Arterial Spin Labeling. 01/10/2015 - 31/12/2015

Abstract

Perfusion magnetic resonance imaging (MRI) is an imaging tool to assess the spatial distribution of microvascular blood flow. Many neurological disorders are accompanied by cerebral blood flow (CBF) alterations, which makes perfusion MRI indispensable in routine clinical practice. Arterial spin labeling (ASL) perfusion MRI uses magnetically labeled arterial blood water as an endogenous diffusible tracer. Tissue perfusion is measured from the signal difference between images with labeled blood and control images. Lack of ionizing radiation, complete non-invasiveness, and absolute quantification of perfusion parameters make ASL a unique perfusion imaging modality. Current ASL methods, however, suffer from problems such as noisy images and patient movement, which are inherent to the acquisition process. My project aims to develop a framework that incorporates new ASL acquisition and reconstruction methods targeting these problems simultaneously. The core of this framework revolves around super-resolution reconstruction (SRR) ASL imaging which allows direct estimation of high-resolution perfusion parameters from a set of differently sampled low-resolution images. Results will yield a patient-friendly, cost-efficient and quantitative protocol that allows accurate and precise perfusion measurement at increased resolution in a clinically acceptable acquisition time, by that removing the main obstacles for ASL to become the golden standard for perfusion measurements.

Researcher(s)

Research team(s)

Spin-off high tech innovation at school 15/09/2015 - 31/03/2016

Abstract

SpinOff wants to bridge the gap between modern science and high tech innovation. High school students will have the opportunity to get in contact with various high tech companies in collaboration with KHLimburg, UAntwerp, KULeuven, DSP Valley and IMEC.

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Project website

White matter characterization using diffusion MRI. 01/07/2015 - 30/09/2019

Abstract

I will study a white matter model that is not restricted to coherently-oriented structures, and parameterized by several white matter tract integrity metrics which are expected to be specific biomarkers of early pathologic changes. First, I will optimize the experimental design to enable accurate and precise parameter estimation. Second, a mouse model will be used to validate and understand what the model reflect at the microstructural tissue level. Third, I will evaluate whether the parameters are markers, capable in discriminating various pathological processes of Alzheimer disease.

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Biomedical Microscopic Imaging and in-vivo Bio-Imaging (EGAMI). 01/01/2015 - 31/12/2020

Abstract

EGAMI stands for Expert Group Antwerp Molecular Imaging. Moreover, EGAMI is the mirror word of 'image'. EGAMI clusters the internationally recognized expertise in the profession of fundamental and biomedical imaging at the University of Antwerp: the Bio-Imaging Lab, the Molecular Imaging Center Antwerp (MICA), Radiology, the Laboratory for Cell Biology and Histology, and the Vision Lab (for post-processing of medical images). EGAMI's mission is providing an integrated research platform that comprises all aspects of multimodality translational medical imaging. Multimodality refers to the integration of information from the various imaging techniques. Within EGAMI, there is pre-clinical and clinical expertise and infrastructure for magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), and single-photon emission computed tomography (SPECT). EGAMI executes projects ranging from applied biomedical (imaging) and fundamental research to imaging methodologies. Die applied biomedical research focusses on the research fields neuro(bio)logy (i.e. development and validation of biomarkers (as well as therapy evaluation) for diseases like Alzheimer's, schizophrenia, multiple sclerosis etc.) and oncology (i.e. biomarkers for improved patient stratification and therapy monitoring). Since the pre-clinical biomedical research within EGAMI makes use of miniaturized versions of imaging equipment for humans (scanners) is it inherently translational, in other words initial findings acquired in animal experiments can be translated into clinical applications for improved diagnosis and treatment of patients ('from bench to bedside'). Beside the application of imaging in the biomedical research, EGAMI also conducts projects that aim to achieve an improvement and optimization of the imaging methodology. The expertise of the MICA (e.g. the development of new radiotracers) and of the Vision Lab (e.g. the development of image reconstruction, segmentation, and analysis algorithms) offers here the strategic platform to assemble intellectual property rights.

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Building an articulating 3D shape model for an improved seating comfort. 01/01/2015 - 31/12/2018

Abstract

There is a wide variety of body shapes. The goal of this project is to develop a statistical shape model of the population, based on 3D scans of the exterior of the body. This virtual model is fully adjustable, both in pose as well as in body shape. The characteristics are also adjustable. The model can be used by product developers to deliver better, more comfortable, semi-custom designs.

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PhyT: Physical and thermal comfort of helmets. 01/01/2015 - 31/12/2017

Abstract

The general purpose of this research is to model a virtual head that allows developing individualized bicycle helmets for Physical and Thermal comfort (PhyT). To this end, a CFD based thermal model will be developed as well as a shape model of the human head.

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Optimization of spectacle frame design 01/11/2014 - 30/04/2016

Abstract

This project represents a formal research agreement between UA and on the other hand the client. UA provides the client research results mentioned in the title of the project under the conditions as stipulated in this contract.

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Research team(s)

First PET-MR: A Flemish Interuniversity Research Simultaneous Time-of-Flight PET-MR scanner. 14/08/2014 - 14/02/2019

Abstract

This project represents a formal research agreement between UA and on the other hand the Flemish Public Service. UA provides the Flemish Public Service research results mentioned in the title of the project under the conditions as stipulated in this contract.

Researcher(s)

Research team(s)

    Functional imaging and analysis of tumors (FIAT). 01/01/2014 - 31/12/2015

    Abstract

    The FIAT consortium will make concrete improvements to quantitative functional imaging of tumours, which will be incorporated in clinical and preclinical application packages, clinical software modules, image analyses and ultimately routine clinical procedures.

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    CT analysis, inspection and dimensional metrology (MetroCT). 01/01/2014 - 31/12/2015

    Abstract

    General purpose Our innovation goal is to realize a break-through in industrial CT image quality and to establish it as an enabling technology for the high-tech chemical, diamond and additive manufacturing industry. The main objective is the realisation of a practical, model-based iterative reconstruction platform for large industrial CT datasets that exploits prior knowledge of X-ray physics and material properties to enhance the spatial resolution.

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    Research team(s)

    New diagnostic technique for measuring eardrum deformations based on endoscopic profilometry with realtime distortion correction using graphics processing units. 01/10/2013 - 30/09/2015

    Abstract

    In this project, we will develop a novel procedure to generate distortion corrected endoscope images and combine this technique with graphics card programming to implement a new diagnostic medical tool for measuring eardrum deformations in real-time and in vivo. As my endoscopic profilometry technique is fully non-invasive, it will be very easy to introduce the new technique in the clinical setting once its possibilities have been demonstrated.

    Researcher(s)

    Research team(s)

    Resolution improvement of diffusion MRI images through model based and numeric-symbolic reconstruction. 01/01/2013 - 31/12/2016

    Abstract

    In this project, novel computational methods are developed for optimal sampling of dMRI data that allows to either restrict the acquisition time and/or improve the accuracy of the measured diffusion profiles. Also, a general and efficient reconstruction scheme is developed to obtain high resolution dMRI images, which accounts for motion and distortion artefacts.

    Researcher(s)

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    Grey value based reconstruction of magnetic resonance images. 01/01/2013 - 31/12/2016

    Abstract

    The main goal of this project is the development of novel reconstruction and segmentation techniques for MR images which lead to improvements in the inherent trade-off between image quality and acquisition time. Prior knowledge of the grey values in the reconstructed image will be modeled in an iterative reconstruction process. The first type of grey value based prior knowledge that we will investigate is the (partial) discreteness of the grey values, analogous to the field of (partial) discrete tomography in CT. Afterwards, histogram-based prior knowledge will be introduced.

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    Novel techniques for inspection and engineering of food (micro)structure based on X-ray computed tomography (TOMFOOD). 01/10/2012 - 30/09/2016

    Abstract

    Food microstructure is defined as the organisation of food constituents at the microscale and their interaction. Most solid foods, including bakery products, fruit, vegetables and dairy foods, are microstructured. Many properties of foods which are relevant to process engineering or quality are related to their microstructure. Microstructure affects food quality attributes such as texture, but also relates to the occurrence of internal defects, as well as affecting food stability and shelf life. Examples include sponginess of bread, texture of cakes and pastry, gas and water transport properties of fruit and consistency and texture of cheeses, cream and butter. Food processing operations affects the microstructure: existing porous structures are destroyed and new ones are created. Insight in food microstructure and how it changes during processing operations is essential to produce high quality food. In particular, consumer demands for enhanced nutritional quality (composition), sensory quality (texture, internal defects) and safety (absence of foreign materials) are driving manufacturers to optimize products and processes with respect to microstructure. X-ray computed tomography (CT) enables the non-destructive visualisation and quantification of the internal structure of objects. Technological advances led to micro-CT (or CT) and nano-CT systems with nowadays a pixel resolution at or below 1 micron, while fast X-ray CT scanners have emerged in the medical field. This project with the acronym TomFood aims at - Developing novel X-ray CT instruments for inspecting food structure and food microstructure of foods at the best possible image quality and resolution balanced to processing speed and equipment cost; - Developing novel tomographic reconstruction and analysis methods for improved quantification of food structure parameters; - Using X-ray CT to improve our understanding of process-structure-property relationships through advanced mathematical models; - Develop tools for design and engineering of novel food processes and food products; - Developing affordable online food inspection equipment in food processing plants to the benefit of the food industry in Flanders. The objectives are realised by means of a multidisciplinary consortium combining food technology experts in specific application fields (dairy, fruits and vegetables, cereal based foods) with experts in X-ray physics and image processing and analysis. The objectives are translated into a program of work packages for each specific objective. The aim is to force a breakthrough in each of the domains resulting in innovations for the Flemish industry.

    Researcher(s)

    Research team(s)

    Diffusion Kurtosis Magnetic Resonance Imaging in Neurodevelopment and Neurodegeneration. 01/10/2012 - 31/03/2016

    Abstract

    This project represents a formal research agreement between UA and on the other hand Janssen Pharmaceutica. UA provides Janssen Pharmaceutica research results mentioned in the title of the project under the conditions as stipulated in this contract.

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    Research team(s)

    Optimized workflow for in vivo small animal diffusion weighted MRI studies of white matter diseases: from acquisition to quantification. 01/07/2012 - 30/06/2016

    Abstract

    While the number of applications of diffusion MRI has exploded in recent years, obtaining reliable and quantitative diffusion information remains a challenging task. In this project, we aim to develop diffusion weighted MRI (DWI) sequences and processing routines to obtain reliable diffusion measures within an acceptable acquisition time and at high spatial resolution to reduce partial volume effects. This would be of particular interest for in vivo pre-clinical research in small animals as mice in which the needed signal to noise ratio for reliable diffusion measures sets constraints on the spatial resolution and measure time. We will develop a diffusion –acquisition & reconstruction -workflow that reconstructs a high resolution isotropic DWI data from a set of multi-slice 2D diffusion weighted images -acquired with a 7 or 9.4 T Bruker MR scanner -with a high in-plane resolution and a lower through-plane resolution and in which the stacks of slices are differently orientated. The new reconstruction method needs to model both the different orientations of the MR images as the different orientations of the applied diffusion weighted gradients. For this super resolution at these high magnetic field, sampling the DWI with conventional fast echo planar imaging sequences will be (1) too sensitive to orientation dependent eddy current image distortions – which prevents the multi angle acquisitions and (2) suffers from local loss of signal due to B0-inhomogeneities. Therefore, we aim to develop the method based on DW-Fast Spin echo acquisition in which the images don't show B0-inhomogeneities problems and moreover can be acquired at different angles. First, we will optimize the DWI with Fast Spin Echo sampling and reconstruction. Based on this sequence, further developments will be performed to set the optimal acquisition scheme to get to super-resolution DWI: being the best combination of the set of orientations of the multi-slice stacks combined with the different directions of the DW gradients. Hereto, we can define different development steps which each will deal with specific MR acquisition and/or processing challenges : motion artifacts, multi-shot acquisition, minimization of eddy current effects, phase-wrapping, T2-modulation over k-space, denoising. The MR-sequences will be developed and implemented – in ParaVision software- on the Bruker MR scanners from the Bio-Imaging lab. The reconstruction algorithms will be developed in Matlab at the Vision lab. This new development can only be realized based on the experiences and close collaboration of both research labs.

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    VECTor/CT: simultaneous PET/SPECT/CT scanner for small animals. 28/06/2012 - 31/12/2017

    Abstract

    This project represents a formal research agreement between UA and on the other hand the Flemish Public Service. UA provides the Flemish Public Service research results mentioned in the title of the project under the conditions as stipulated in this contract.

    Researcher(s)

    Research team(s)

      Integrated cerebral networks for perception, cognition and action in human and non-human primates (CEREBNET). 01/04/2012 - 31/12/2017

      Abstract

      To study functional brain networks supporting perception, action and cognition in normal and diseased human subjects and non-human primates (NHP). There will be a strong focus on anatomical, functional and effective connectivity and causality-oriented research to develop and to test biologically-relevant theoretical models for understanding brain function. The consortium will build heavily on joint expertise and collaborative experiments performed during previous phases of the IUAP program in which monkey imaging, developed by the pilot group, plays a crucial role to link human imaging studies with knowledge obtained through monkey electrophysiology.

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      Research team(s)

      Design and evaluation of a Brain Computer Interface headset for alternative communication and diagnostic support. 01/02/2012 - 31/12/2012

      Abstract

      The goal of this project is to find the requirements for the design of a comfortable, wirelessly operating EEG headset which will be a first step towards medically approved EEG-BCI devices that can be used to communicate by patients and support clinicians in the diagnosis of patients suffering from brain disorders. New ways to model the human head will be researched using statistical analysis, different types of materials and electrodes will be compared and simulations of pressure exerted by the headset will be made in order to achieve an optimal design, which will provide an added value for all involved stakeholders. This design will then be produced and evaluated by a test panel of healthy subjects, after which it will be validated by patients suffering from ALS.

      Researcher(s)

      Research team(s)

      Quantitative tomographic segmentation of magnetic resonance images 01/01/2012 - 31/12/2015

      Abstract

      This project proposal aims at the development of novel methods for segmentation of magnetic resonance images (MRI). Whereas conventional segmentation methods solely employ the reconstructed MRI image, we will target tomographic MRI image segmentation in which the original data from the MRI scanner is exploited. It is expected that this will lead to significantly improved segmentation accuracy.

      Researcher(s)

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      Project website

      Speeded up processing and reconstruction of magnetic resonance images (SUPERMRI). 01/01/2012 - 31/12/2013

      Abstract

      The SuperMRI project aims at - speeding up the acquisition process by developing new imaging sequences and sparse sampling strategies - reducing the computation time of iterative reconstruction algorithms by developing fast and generic forward and backward projectors through parallelization and distribution of the algorithms, in combination with suitable hardware architecture (GPU or FPGA). - Significantly improving the image quality by developing novel reconstruction algorithms for MRI, related to compressive sensing and discrete tomography. - developing fast tomographic image processing algorithms that exploit the available k-space data, with focus on segmentation and motion compensation.

      Researcher(s)

      Research team(s)

      Multi energy simulation and reconstruction (MESRECON). 01/01/2012 - 31/12/2013

      Abstract

      MESRECON aims to develope truly dual energy image reconstruction techniques. One key component of the solution is a detailed model of the acquisition and image processing. Developing such a model is a first aim of the project. The second aim is to develop dual energy reconstruction techniques based on this model and on advanced image restoration techniques.

      Researcher(s)

      Research team(s)

      New diagnostic technique for measuring eardrum deformations based on endoscopic profilometry with real-time distortion correction using graphics processing units. 01/10/2011 - 30/09/2013

      Abstract

      In this project, we will develop a novel procedure to generate distortion corrected endoscope images and combine this technique with graphics card programming to implement a new diagnostic medical tool for measuring eardrum deformations in real-time and in vivo. As my endoscopic profilometry technique is fully non-invasive, it will be very easy to introduce the new technique in the clinical setting once its possibilities have been demonstrated.

      Researcher(s)

      Research team(s)

      Simulation of image formation in X-ray phase contrast tomography 01/07/2011 - 31/12/2015

      Abstract

      Grating based differential phase contrast tomography is a new experimental technique to offers very exquisite images of soft tissues. However, the artifacts in the current images prohibit the accurate reconstruction of the inside of an object. The project aims to develop the algorithms that allow a quantitative reconstruction of this technique

      Researcher(s)

      Research team(s)

      Image reconstruction for in situ Computed Tomography 01/07/2011 - 31/12/2015

      Abstract

      Computed Tomography (CT) is a powerful, nondestructive technique for producing 2-D and 3-D cross-sectional images of an object from X-ray images. Conventional CT requires the object to be inserted into the CT scanner and that many X-ray images are taken, prior to reconstruction of the 3D image. However, there are many situations in which these requirements are not met: If the object is a fixed part of an object that is too large to fit in the scanner object; if moving the object is dangerous (e.g., explosives), if moving the object would disrupt or pollute the context (e.g., crime scene), if the object cannot easily be transported for X-ray scanning (e.g., horse with broken bone), or if the object is too valuable to be removed (e.g., cultural heritage). To cope with such situations, in situ X-ray scanning is required. Portable X-ray devices, such as a hand-held X-ray camera or a robot system, are available on the market, which are, however, intended to acquire only a single or a series of X-ray images. It is currently not possible to produce a 3D reconstruction of the object from these X-ray images. This is because: 1. in contrast to common CT-scanners, the exact position and orientation of the X-ray source/detector system with respect to the object is unknown. 2. if a hand-held camera or robot system is employed, the scanning process is time consuming, which limits the number of X-ray images to be acquired for tomography. Current CT reconstruction algorithms require a large number of X-ray images to obtain accurate results. 3. it may not be possible to acquire projection images from all angles. Both 2 and 3 result in a highly underdetermined inverse problem. This project aims at the development of robust, efficient reconstruction methods for in situ X-ray scanning & tomography. These methods will not require accurate prior knowledge of the scanning geometry, and will be tailored for achieving maximal reconstruction quality from a small number of projection images. To this end, the following computational strategies will be combined: 1. Automatic parameter estimation based on consistency maximization of the simulated X-ray images with respect to the measured X-ray images will allow the reconstruction algorithm to deal with unknown geometrical parameters. 2. New algorithms will be developed for efficient optimization of the high dimensional search space (including the unknown object volume and the position/orientation of the acquired X-ray images) by exploiting the linearity of certain reconstruction algorithms and exploring multi-resolution approaches for gradual refinement of parameter estimates. 3. Compressive sensing and discrete tomography will be incorporated within the parameter estimation framework to allow for accurate image reconstruction from few projections and deal effectively with a limited angular range. 4. State-of-the-art GPU computing techniques, based on recent advances in a current research project on accelerating tomography algorithms, will be employed to effectively deal with the high computational requirements. A successful project will open up numerous applications in various fields such as security, nondestructive testing, dental imaging, veterinary imaging, or cultural heritage.

      Researcher(s)

      Research team(s)

      Optimisation of X-ray laminography for non-destructive imaging of the internal structure of historical paintings 01/07/2011 - 30/06/2015

      Abstract

      Historical oil painting are built up of many, fairly thin layers of paint. Each layer consists of an organic binder mixed with (in)organic pigment grains. Recently, several new methods have been developed to visualize this multilayer structure in a non-destructive manner. X-ray laminography is a variant of X-ray tomography that is more appropriate for the study of large, flat objects such as paintings. The project involves the optimization of the experimental parameters for X-ray laminography of (parts of) oil paintings with the aim of being able to reconstruct their internal structure and time-dependent process of creation.

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      Research team(s)

      KWANTUM SPIN-OFF. Bridge between research in modern physics and high-tech entrepreneurship. 01/03/2011 - 31/10/2013

      Abstract

      This project represents a formal research agreement between UA and on the other hand KHL. UA provides KHL research results mentioned in the title of the project under the conditions as stipulated in this contract.

      Researcher(s)

      Research team(s)

      Quantitative three-dimensional structure determination using transmission electron microscopy : from images toward precise three-dimensional structures of nanomaterials at atomic scale. 01/01/2011 - 31/12/2014

      Abstract

      This project aims at the development of new measurement techniques using transmission electron microscopy in order to realize a breakthrough towards quantitative three-dimensional structure determination of nanomaterials at atomic scale.

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      Research team(s)

      Optimal estimation and processing of diffusion kurtosis parameters to evaluate their clinical relevance. 01/01/2011 - 31/12/2012

      Abstract

      This project represents a research agreement between the UA and on the onther hand IWT. UA provides IWT research results mentioned in the title of the project under the conditions as stipulated in this contract.

      Researcher(s)

      Research team(s)

      Analysis of images from the respiratory system (AIR). 01/01/2011 - 31/12/2012

      Abstract

      The project will in general investigate and develop a number of innovative imaging, image reconstruction and analysis techniques specifically for lung diseases, which can become market ready in a few years.

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      Research team(s)

      Quantitative extraction of normaal values from (diffusion weighted) MR images of the premature brain. 01/01/2010 - 31/12/2013

      Abstract

      Concretely, we will develop a DTI-atlas for the premature brain (0 to 4 year) by combining non-affine registration techniques and new denoising algorithms for DWI-data as well as accounting for different growing speeds of the premature brain. Nowadays, DWI is also seen as one of the fastest growing modalities for preterm brain analysis.

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      Research team(s)

      SUPERCT - Speeded Up Processing and Reconstruction of Tomograms. 01/01/2010 - 31/12/2011

      Abstract

      The SuperCT project aims at 1) reducing the computation time of several tomographic image reconstruction algorithms. In particular, we will focus on developing fast and flexible iterative reconstruction techniques for X-ray CT, SPECT, and Electron Tomography. Most of the speedups will be obtained by parallelization and distribution of the algorithms, in combination with suitable hardware architecture (GPU). 2) developing and implementing fast tomographic image processing algorithms that exploit the available projection data. In particular, we will focus on segmentation, beam hardening correction, visualization, and alignment.

      Researcher(s)

      Research team(s)

      Innovative Medical Imaging for Neurological Disorders (iMIND). 01/01/2010 - 31/12/2011

      Abstract

      Epilepsy is a frequently observed neurological disorder that is characterized by repeated epileptic seizures. These seizures are characterized by a sudden and unexpected change of the behavior and/or the consciousness of the patient as a result of excessive and uncontrolled electrical activity in a well-defined region in the cerebral cortex (the so-called epileptogenic zone). By placing electrodes using a standardized setup on the scalp surface, one can measure the electrical or magnetic fields generated by brain activity during an epileptic event. A recording of electrodes in function of time is called an electroencephalogram (EEG) or magnetencephalogram (MEG), respectively. EEG/MEG source analysis determines the origin of brain activity based on the EEG/MEG due to epileptic events and consists of two subproblems: First, by solving the forward problem one obtains the electrode potentials due to a given set of sources, which are characterized by the source parameters. Second, by solving the inverse problem the source parameters are estimated given a set of measured electrode potentials. These imaging techniques measure the generated brain activity with a high time resolution (milliseconds) but have a low spatial resolution. In the past decade, a vast number of medical imaging modalities have been used in the clinical practice. Magnetic Resonance Imaging (MRI), Computed Tomography (CT) and Diffusion Weighted MRI (DW-MRI) are techniques that reveal the anatomical structure of the brain. Functional imaging, such as Single Photon Emission Computed Tomography (SPECT) and functional MRI (fMRI), image changes in the blood flow. These techniques have a high spatial (approx. 1 mm for MRI, 7 mm for SPECT, 3 mm for fMRI) resolution, but a very low temporal resolution as the duration of the scan is in the order of minutes. Novel treatment options have emerged from the experimental field, which involve the stimulation of a specific brain region, deep brain stimulation (DBS), or vagal nerve, vagal nerve stimulation (VNS). However, the mechanism of action of these procedures is unknown and the efficacy of the treatment can be improved. Within the iMIND project we want to develop a software platform that can: 1. Gather the necessary information for the determination of the origin of epileptic seizures and the quantification of the mechanism of action of novel neuromodulatory treatment. 2. Coregistration of the different acquired images in order to visualize and analyze them in the same frame of reference. Furthermore we want to improve the accuracy of the EEG/MEG source analysis by incorporating anatomical and functional information obtained from medical imaging 3. Visualize the images in a comprehensive and user-friendly way. We also want to visualize the time information, provided by the improved EEG/MEG source analysis procedure. 4. Be used for the accurate determination of the epileptogenic zone. In this case we want to determine the added value of the software platform on a small population in the determination of the epileptic onset zone. 5. Be used in an experimental setting by quantitatively measuring the effect of different parameters of novel neuromodulatory treatment (DBS and/or VNS). In this case, the added value is determined by using the software platform for comparing functional images obtained during stimulation of small animals and correlating them with anatomical images.

      Researcher(s)

      Research team(s)

      CIMI - Color Imaging & Multidimensional Image processing in medical applications. 01/10/2009 - 31/12/2011

      Abstract

      Medical imaging is getting more and more complex. Unfortunately the medical imaging community today by far is not optimally making use of color and multi-dimensional information. This research project will significantly improve this situation. In the technology working packages we will develop platform technology to better handle multi-dimensional and color data. The basic technology that will be developed in this project covers the entire imaging chain starting with acquisition device over image processing to visualization and finally (clinical) validation and standards. This technology will be use as a basis in other working packages and applied to specific clinical applications.

      Researcher(s)

      Research team(s)

      Quantitative analysis of in vivo multimodal and multitemporal images: from animal models to novel medical applications (QUANTIVIAM). 01/01/2008 - 31/12/2011

      Abstract

      This project represents a research agreement between the UA and on the onther hand IWT. UA provides IWT research results mentioned in the title of the project under the conditions as stipulated in this contract.

      Researcher(s)

      Research team(s)

      Development of discrete tomography for transmission electron microscopy: 3D imaging of interfaces in ceramic and semiconducting multilayers. 01/01/2008 - 31/12/2011

      Abstract

      The main goal of this project is to develop discrete tomography for electron microscopy. As a starting point for the development of new reconstruction algorithms, the DART (Discrete Algebraic Reconstruction Technique) algorithm will be used. DART is an iterative algebraic reconstruction algorithm that is currently being developed at VISION LAB. It alternates between steps of the SIRT algorithm from continuous tomography and certian descretization steps. Within the SIRT iterations, subsets of the pixels are fixed at one of the constant grey levels, creating a new system of equations with fewer unknown than the original system.

      Researcher(s)

      Research team(s)

      Improvement of the image quality for fast Diffusion Tensor Imaging. 01/01/2008 - 31/12/2009

      Abstract

      Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) is a recently developed technique who permits to study the architecture of white brainmatter (WM) in vivo and in an non-invasive way. DT-MRI is based on the Brownian movement of H2O-molecules in biological tissue and makes it possible to determine the anisotropic diffusion of these molecules . This anisotropic diffusion can be related to aligned microstructures, like WM brain fibres, which has a great value in biomedical applications. Since a large amount of data is needed for this technique, it is desirable to use fast imaging sequences. However, these kind of sequences introduce specific artefacts in the images which degrade the quality of the DT-measurements. For this reason, several strategies will be used to upgrade this quality. The present acquisition standard for fast DTI, Echo Planar Imaging (EPI), is prone to severe susceptibility artefacts which introduce geometric distortions in the images. These artefacts are more explicit when working at higher field strengths (here: 7 Tesla and 9.4 Tesla). By using an adapted EPI-sequence, it is possible to measure the local susceptibility artefacts and to correct for distortions. Another strategy that will be used is to combine DTI with Fast Spin Echo (FSE). This technique should be less sensitive to susceptibility artefacts. A recent approach, in which multiple receivers are used (Parallel Imaging) will be used to reduce artefacts in DT-MRI.

      Researcher(s)

      Research team(s)

      Development of progressed estimating methods for the detection of brain activity with fMRI data. 01/01/2008 - 31/12/2009

      Abstract

      The focus of this project is signal processing in functional MRI. Functional brain imaging offers a way to image the specific brain areas that are active during a specific action. Brain activation is present in the MRI images due to the BOLD (Blood Oxygen Level Depended) response. Since the BOLD response signal is weak compared to the noise level accurate modeling of the response as well as the disturbances will improve brain activation detection. Within this project the time and space correlations of fMRI signal are modeled. The model will be used to derive estimators for task depended brain activation. We seek to improve the current standard processing of fMRI datasets in the following places. The time correlation is currently often modeled by an AR(1) process (autoregressive process with 1 degree of freedom). There are indications that this is not always an accurate description of the disturbances, therefore methods are developed that automatically select the best AR order and model from the data to optimally model the temporal noise structure. Usually for activation detection a linear regression is performed with the stimulus paradigm convolved by a HRF (hemodynamic response function) as regression vector. However it is known that the HRF is not constant among brain regions and subjects. Therefore we will investigate ways to estimate the HRF from the measured data. To decide which part of the brain are active statistical test have to be performed. In order to make these tests accurate the noise level of the images often is needed. This noise level can be robustly estimated from background area present in the images. Traditionally the maximum of the background mode of the histogram is used as an estimator for the noise level. In this project we develop a Maximum Likelihood method which can robustly determine the noise variance from the histogram of the background mode of the image.

      Researcher(s)

      Research team(s)

      Development of a generalised approach to discrete tomography: theory and algorithms. 01/10/2007 - 28/02/2010

      Abstract

      Researcher(s)

      Research team(s)

      Non-rigid coregistration of diffusion tensor images. 01/01/2007 - 31/12/2008

      Abstract

      DTI is a unique technique that provides in vivo and non-invasive information regarding the organisation and structural integrity of tissues. It is commonly used to study all kinds of brain diseases (MS, Parkinson, etc). An important issue is the early quantitative detection of brain abnormalities. However, that is only possible when non-affine registration techniques are avaliable that can deal with multi-valued data from DTI images. The goal of this project is to develop such registration techniques.

      Researcher(s)

      Research team(s)

      New reconstruction methods for ROI micro-CT. 01/01/2006 - 31/12/2007

      Abstract

      Region of interest (ROI) cone-beam tomography has become a hot topic in the continuous quest for reducing the amount of radiation and achieving a higher resolution of the object through geometric magnification. This magnification is achieved by moving the object closer towards the object so that the ROI fully covers the field of view. A disadvantage of this method is truncation of other parts of the object, while in theory all information about the object is needed for ideal reconstruction. In this project, two reconstruction algorithms are examined that, in combination with existing methods, may result in a significant improvement of the reconstruction quality. In the research group Visionlab, a new algorithm for ROI reconstruction is developed and is already implemented for a parallel geometry. The algorithm reduces the effect of the attenuation of the X-rays outside the ROI using a Gaussian window function. Preliminary results show that in case the parameters are adjusted optimally, good results are achieved. The goal of this project is now to further examine the algorithm and to improve it where its possible. The influence of noise will be examined and the method will be validated for real data and different acquisition geometries. The second algorithm, the universal reconstruction algorithm (URA) calculates a reconstruction of a general acquisition, for any geometry. The algorithm builds up the reconstruction image ray by ray in the frequency space. This is followed by an interpolation in order to fill up a regular lattice. After normalization and correction for unequal sampling, we get the image by performing an inverse Fourier transform. The URA will be analytically and numerically examined for both general acquisition problems (such as helical cone beam) and ROI problems.

      Researcher(s)

      Research team(s)

      Development of progressed estimating methods for the detection of brain activity with fMRI data. 01/01/2006 - 31/12/2007

      Abstract

      The focus of this project is signal processing in functional MRI. Functional brain imaging offers a way to image the specific brain areas that are active during a specific action. Brain activation is present in the MRI images due to the BOLD (Blood Oxygen Level Depended) response. Since the BOLD response signal is weak compared to the noise level accurate modeling of the response as well as the disturbances will improve brain activation detection. Within this project the time and space correlations of fMRI signal are modeled. The model will be used to derive estimators for task depended brain activation. We seek to improve the current standard processing of fMRI datasets in the following places. The time correlation is currently often modeled by an AR(1) process (autoregressive process with 1 degree of freedom). There are indications that this is not always an accurate description of the disturbances, therefore methods are developed that automatically select the best AR order and model from the data to optimally model the temporal noise structure. Usually for activation detection a linear regression is performed with the stimulus paradigm convolved by a HRF (hemodynamic response function) as regression vector. However it is known that the HRF is not constant among brain regions and subjects. Therefore we will investigate ways to estimate the HRF from the measured data. To decide which part of the brain are active statistical test have to be performed. In order to make these tests accurate the noise level of the images often is needed. This noise level can be robustly estimated from background area present in the images. Traditionally the maximum of the background mode of the histogram is used as an estimator for the noise level. In this project we develop a Maximum Likelihood method which can robustly determine the noise variance from the histogram of the background mode of the image.

      Researcher(s)

      Research team(s)

      Characterization of 3D-form. 01/01/2006 - 31/12/2007

      Abstract

      Characterization is a problem that arises in numerous engineering applications and computer vision. One can characterize the boundary of an object or the entire region the object surrounds. This characterization is possible using different metrics: area, perimeter, compactness, etc. In this project we investigate another technique: decomposition in eigen-functions. For this decomposition we use Fourier-analysis and wavelets, we also investigate their (dis)advantages. Before we can decompose an object into its eigen-functions, we have to represent the object as a function on a simple domain (like a plane or sphere). The process of constructing such a representation is called parameterization. In this project our goal is to develop robust and efficient algorithms for constructing the parameterization of arbitrary objects. Applications range from computer graphics (texturing, morphing) to the medical world (registration, analysis of deformation of certain brain parts).

      Researcher(s)

      Research team(s)

        Development of improved techniques for the analysis of functional magnetic resonance data. 01/10/2005 - 30/09/2008

        Abstract

        Researcher(s)

        Research team(s)

        Development of improved statistical tests for functional magnetic resonance data. 01/05/2005 - 30/04/2009

        Abstract

        In this project, improved statistical tests will be developed voor functional magnetic resonance imaging (FMRI) data analysis. Thereby, parametric tests, based on generalized likelihood ratio tests, as well as non-parametric tests (such as clustering) will be investigated. Finally, advanced methods for visualisation of fMRI detection results on high resolution MR images will be developed.

        Researcher(s)

        Research team(s)

        Shape reconstruction and characterization. 01/01/2004 - 31/12/2007

        Abstract

        The problems of reconstruction and characterization of the shape of an object or region are ubiquitous in computational science, engineering and computer vision. Important tools in solving these problems are moments and Fourier descriptors. Both have proved to be very useful in respectively shape reconstruction and characterization. From the literature on the shape-from-moments reconstruction problem, one can see that until recently the shape inverting problem was tackled using univariate techniques in one complex variable. This approach imposes restrictions on the shape of the object under reconstruction as well as on the dimensionality of the problem. Our aim is on one hand to further explore the three-dimensional problem, using true multidimensional techniques instead of reducing the problem to one-dimensional subproblems, and on the other hand to eliminate the current restrictions on the shape of the object under study. With respect to Fourier descriptors (FDs) used for shape characterization, interesting problems remain to be solved as well. Two-dimensional (2D) FDs have been exploited from the early 70's for the characterization of the contours of 2D objects. From the 90's on, methods for the computation of 3D Fourier descriptors were developed for the characterization of the surface of binary objects. Existing methods, however, still suffer from complexity problems, especially when the number of vertices is large (>5000). In general, 3D FDs are computed by mapping the object's polyhedron onto the surface of a unit sphere, after which it is expanded in spherical harmonics. This mapping from the object space (surface) to the parameter space (sphere) is non-trivial and current techniques suffer from computational problems. Our goal is to develop a robust method which implements this mapping in an efficient way, in other words, such that the computational time grows linearly with the number of vertices.

        Researcher(s)

        Research team(s)

          New reconstruction methods for ROI micro-CT. 01/01/2004 - 31/12/2005

          Abstract

          Region of interest (ROI) cone-beam tomography has become a hot topic in the continuous quest for reducing the amount of radiation and achieving a higher resolution of the object through geometric magnification. This magnification is achieved by moving the object closer towards the object so that the ROI fully covers the field of view. A disadvantage of this method is truncation of other parts of the object, while in theory all information about the object is needed for ideal reconstruction. In this project, two reconstruction algorithms are examined that, in combination with existing methods, may result in a significant improvement of the reconstruction quality. In the research group Visionlab, a new algorithm for ROI reconstruction is developed and is already implemented for a parallel geometry. The algorithm reduces the effect of the attenuation of the X-rays outside the ROI using a Gaussian window function. Preliminary results show that in case the parameters are adjusted optimally, good results are achieved. The goal of this project is now to further examine the algorithm and to improve it where its possible. The influence of noise will be examined and the method will be validated for real data and different acquisition geometries. The second algorithm, the universal reconstruction algorithm (URA) calculates a reconstruction of a general acquisition, for any geometry. The algorithm builds up the reconstruction image ray by ray in the frequency space. This is followed by an interpolation in order to fill up a regular lattice. After normalization and correction for unequal sampling, we get the image by performing an inverse Fourier transform. The URA will be analytically and numerically examined for both general acquisition problems (such as helical cone beam) and ROI problems.

          Researcher(s)

          Research team(s)

          Characterization of 3D-form. 01/01/2004 - 31/12/2005

          Abstract

          Characterization is a problem that arises in numerous engineering applications and computer vision. One can characterize the boundary of an object or the entire region the object surrounds. This characterization is possible using different metrics: area, perimeter, compactness, etc. In this project we investigate another technique: decomposition in eigen-functions. For this decomposition we use Fourier-analysis and wavelets, we also investigate their (dis)advantages. Before we can decompose an object into its eigen-functions, we have to represent the object as a function on a simple domain (like a plane or sphere). The process of constructing such a representation is called parameterization. In this project our goal is to develop robust and efficient algorithms for constructing the parameterization of arbitrary objects. Applications range from computer graphics (texturing, morphing) to the medical world (registration, analysis of deformation of certain brain parts).

          Researcher(s)

          Research team(s)

            Development of improved techniques for the analysis of functional magnetic resonance data. 01/10/2002 - 30/09/2005

            Abstract

            Researcher(s)

            Research team(s)

            01/05/2002 - 30/04/2004

            Abstract

            The goal of this project is the development of a robust method for characterization of 3D binary objects, applied to rough diamonds. In particular, we will focus on the development of an invariant multiscale representation of a object surfaces (manifolds). It is expected that, at least scientifically, a possible solution will be provided for the 'blood diamonds' problem.

            Researcher(s)

            Research team(s)

            3D source-localization through an integrated processing of simultaneously obtained EEG and fMRI data. 01/11/1998 - 31/10/2000

            Abstract

            This project deals with the processing of simulteneously acquired EEG and fMRI data as to obtain an accurate localization of epileptic brain activity. The research will focuss on the reduction of MR related artefacts in EEG data and vice versa, on image processing of magnitude MR data and on the modification of the MR imaging sequence after EEG data processing.

            Researcher(s)

            Research team(s)

              Image restoration of Magnetic Resonance Images 01/05/1997 - 30/04/1999

              Abstract

              The purpose of this project is the improvement of spatial resolution in the field of Magnetic Resonance Imaging. The system PSF will be determined theoretically and experimentally. This knowledge will be used in the optimalisation of a Fourier reconstruction scheme.

              Researcher(s)

              Research team(s)