Research Gunther Steenackers

Abstract

Breast reconstruction using autologous tissue, specifically with Deep Inferior Epigastric Artery Perforator (DIEP) flaps, has made significant advancements in recent years by aiming to minimize damage to the donor site. In this regard, selecting the appropriate perforator is crucial, given that the flap is perfused by a single perforator. The current standard method for selecting perforators is Computed Tomography Angiography (CTA), which has drawbacks including the use of contrast agents, exposure to radiation, high costs, and no information on flap perfusion. Recent studies have demonstrated that Dynamic Infrared Thermography (DIRT) is a non-invasive method capable of visualizing both the dominant perforators preoperatively and the perfused zones associated with these perforators intraoperatively. Identifying these perfused zones is essential for optimizing the breast's survival chances, but it requires an additional average surgical time of 60 minutes. The aim of this project is to predict perfused zones of specific perforators without intraoperative measurements, resulting in faster and more accurate treatments. This is achieved through the development of a Convolutional Neural Network trained with Finite Element Method (FEM) models of the abdomen with perforators. These models are constructed using both CTA and pre- and intraoperative DIRT data, with FEM updating to adapt the model's thermal behavior to the infrared measurements.

Researcher(s)

• Promoter: Steenackers Gunther
• Co-promoter: Thiessen Filip
• Fellow: Clarys Warre

Research team(s)

• Industrial Vision Lab (InViLab)

Project type(s)

• Research Project

Abstract

Over the years there has been a tremendous evolution in breast reconstructions with free flaps, focusing on reducing donor site morbidity. Breast reconstructions with Deep Inferior Epigastric artery Perforator (DIEP) flaps have become the gold standard. As this flap is only perfused by a single perforator, the selection of the perforator is of main importance. Computed Tomography Arteriography (CTA) is the gold standard for the selection of perforators. However, this technique has some major drawbacks: the use of intravenous contrast, radiation, high costs, not being usable perioperatively, and no information on flow characteristics. Not only the selection of the perforators is mandatory for successful breast reconstructions with free flaps. Flap failure in breast reconstructions is often due to technical failures during the dissection of the perforator, failure of the anastomosis, or kinking or compression of the pedicle during flap-inset and shaping. Clinical monitoring is mostly used to diagnose these problems. During this study, we will further evaluate the use of Dynamic Infrared Thermography (DIRT) during breast reconstructions as an alternative non-invasive examination that is applicable during all phases of breast reconstruction. This technique allows for identifying the most dominant perforators and the area they perfuse. Our clinical study confirms that DIRT is capable to confirm the location of perforators of DIEP-flaps preoperatively. Moreover, using DIRT, extra information on the quality of the perforators is obtained by objective monitoring of flap perfusion with the same standardized measurement set-up. Our preliminary studies show that DIRT is a promising technique for selecting perforators and monitoring flap perfusion, used during all phases of breast reconstruction. The project goal is to further evaluate the use of DIRT during breast reconstructions in order to reduce flap failure and ultimately reduce the cost for our society.

Researcher(s)

• Promoter: Steenackers Gunther
• Co-promoter: Verhoeven Veronique

Research team(s)

• Industrial Vision Lab (InViLab)

Project type(s)

• Research Project

Abstract

The need for protection and performance in various work and sport setups has driven the development of functional textiles and clothing with complex designs. Comfort is an important aspect for high performance sportswear and Personal Protective Equipment (PPE) as it affects the health, sports performance and work efficiency of athletes and workers. Unfortunately the comfort of these cloths, consisting of complex structures and special materials, is known to be poor. In many cases, the customers are satisfied with the functionality (i.e. protection against rain, cold, etc.) but disappointed in the poor comfort. Problem and innovation target: none of the existing state-of-the-art comfort test methods is ideal. They typically require numerous physical prototypes, specialized test equipment, lengthy and costly wearer tests, etc. Physiological parameters, such as skin temperature (Tsk) is linked to thermal sensations and comfort of the human subject and can be accurately assessed by Infrared (IR) thermography. Comfort is a complex matter influenced by the intensity of the activity, climatic conditions, textile material properties, clothing design and fit. Artificial Intelligence, in particular Deep Learning (DL), can be used to deal with these numerous parameters that influence the comfort. Goal and objectives: ComforTex-AI will help overcoming these issues related to comfort and aims to develop algorithms that will result in an affordable, quick and user-friendly methodology to assess and optimize the garment comfort. Fabric comfort characteristics (Ret/Rct), physiological parameters (i.e. Tsk), environmental factors (i.e. air temperature/humidity), work intensity and garment fit will be used as input for the AI Neural Networks (NN) that will predict comfort perceptions. The main expected result is a new algorithm-driven methodology developed in the form of a user-friendly tool for companies to aid in the design of comfortable functional clothing. Further results include among others: (1) Large library with quantitative data (i.e. Tsk, fabric properties Ret/Rct,) tested via state-of-the-art equipment and qualitative data (i.e. comfort sensations from human subjects) collected in various wear scenario's; (2) AI algorithm to assess and optimize clothing comfort (on 5-points scale); (3) Data-based product classification for specific usage conditions; (4) Validated cases for sportswear, workwear and PPE for specific purposes. Economic impact: implementation of the new methodology will assist the companies to make optimized choices of material and garment designs during the development phase. This will result in an increase of their turnover as a result of (1) shorter and cheaper development costs due to less physical prototypes and testing, (2) lower production costs due to more efficient use of materials and (3) merchandising of qualitative and comfortable clothing which comply with the wearer needs. This will furthermore limit the premature discharging, overproduction and overconsumption, which are currently huge concerns for the textile and clothing sector. Due to its strong multidisciplinary character, ComforTex-AI will enable the acquirement of new skills and knowledge and strengthen the market position of the companies in the sector. The specific target group consists of manufacturers of workwear, PPE and sportswear (20 to 25 companies in Belgium and 30 companies in Germany) and also producers of fabrics (11 weaving mills and 21 knitting mills in Belgium and 15 knitting mills in Germany). The project consortium consists of two sector associations (FKT as global coordinator and CREAMODA) and four RTOs with complementary expertise in materials and clothing comfort (HOGENT and Hohenstein Institute), IR thermography and AI techniques (UAntwerp and FITT / htw Saar).

Researcher(s)

• Promoter: Steenackers Gunther

Research team(s)

• Industrial Vision Lab (InViLab)

Project type(s)

• Research Project

Abstract

Osteoarthritis (OA) of the knee is defined as a mechanically induced degenerative joint disorder. Lower-limb deformity is one of the main causes of disease development as load is one-sidedly shifted and unequally redistributed over the articular surface. Knee joint replacement surgery is frequently performed, however postoperative patient dissatisfaction is present in one fifth of patients. Therefore, the aim of this proposal is to deepen the understanding of joint-preserving procedures to ameliorate the native joint by addressing two important clinical obstacles: the static two-dimensional pre-operative planning and adverse effects on the adjacent ankle joint. Development of a discrete element analysis (DEA) model of the knee joint will be based on three-dimensional geometric morphometric data obtained from our in-house available, largest present multi statistic shape model (SSM). For dynamic assessment, data will be extrapolated out of parameterized gait analysis. The generated knee joint DEA model will be integrated with our DEA models of the hip and ankle to provide an entire lower-limb model. K-fold cross validation will be used for evaluation in terms of in-model accuracy, specificity and generalizability. Translation of the developed and validated lower-limb model from engineering science to the clinical field will enable preoperative planning to allow an optimal correction of the contact mechanics of the knee as well as the adjacent joints.

Researcher(s)

• Promoter: Steenackers Gunther

Research team(s)

• Industrial Vision Lab (InViLab)

Project type(s)

• Research Project

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.

Researcher(s)

• Promoter: Sijbers Jan
• Co-promoter: De Beenhouwer Jan
• Co-promoter: Janssens Koen
• Co-promoter: Scheunders Paul
• Co-promoter: Steenackers Gunther
• Co-promoter: Van der Snickt Geert
• Fellow: De Samber Björn
• Fellow: Levrau Elisabeth

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

In collaboration with a composer a conversion of wind power data to music was established. The data of 40 wind turbines in Antwerp was captured during a period of 1 year and used in the musical composition. The results were presented during a concert in Antwerp (Havenhuis).

Researcher(s)

• Promoter: Steenackers Gunther

Research team(s)

• Industrial Vision Lab (InViLab)

Project type(s)

• Research Project

Abstract

In many fields of science and technology, the Midwave Infrared range (MWIR) (4000-400 cm-1) is a highly relevant part of the electromagnetic spectrum, because in this region many compounds and materials feature a unique pattern of absorption bands, directly related to their molecular structure. Current MWIR spectroscopic techniques (like FT-IR) are often used, but these are relatively slow and can only be used in laboratory conditions (on small samples). In this project we will use a Quantum Cascade Laser (QCL) to enable in-situ remote spectroscopy. Reflection (solids) or transmission (liquids and gasses) spectra can be measured with a detector by (fast) scanning of the QCL wavelength (up to 10.000 cm-1 per seconds). We will use the QCL laser in combination with three detectors available at the UAntwerp-InViLab and AXIS research groups: 1) a deuterated triglycine sulfate, or DTGS detector, 2) a scanning laser Doppler vibrometer to perform remote scanning photoacoustic spectroscopy, 3) a thermal camera for mid-wave hyperspectral imaging. The three proposed QCL-based systems are complementary to each other: the DTGS enables a very high wavelength resolution, the LDV can be used for photoacoustic spectroscopy to perform measurements at large stand-off distances (up to 100m), and the thermal camera-based setup delivers a very high spatial resolution, but with a lower wavelength resolution. The QCL based system will be used for the research at InViLab and AXIS in different applications: artwork, corrosion, biomedical and textile inspection. Furthermore, we have identified several other potential applications that we will look into in the future together with other UAntwerp research teams: plasma chemistry, histopathology, road materials, metal oxide powders, meso-porous materials, drug detection, recycling of polymer materials, wastewater.

Researcher(s)

• Promoter: Steenackers Gunther
• Co-promoter: Janssens Koen
• Co-promoter: Vanlanduit Steve

Research team(s)

• Industrial Vision Lab (InViLab)

Project type(s)

• Research Project

Abstract

General conclusions and achievements The goal of Inspect 4.0 is to combine and integrate machine learning and machine vision technology into a flexible and accurate quality inspection system that can be deployed across a variety of textile manufacturing setups. · Use of existing and new vision technology for woven fabrics. Cameras were chosen and a setup was built to allow inspection with the vision technology on an inspection table similar to those already used in the industry. (KPI2: Woven and coated textiles from partners will be analyzed on the developed measuring setup) · Several cases were handled during the project. Our ambition was not to develop and use black-box models, but to build the models case-by-case and further optimize them with the test data. Based on the limited test data available, this has largely been achieved. · Use of existing and new vision technology for coated fabrics. Based on the initial test results, it was decided that measuring coated fabrics was not feasible with the current availability of cameras. This, combined with the fact that the user group was mainly interested in detecting production errors in woven fabrics, it was decided to no longer focus on this for the time being. · Development of a demonstrator. A woven fabric demonstrator was produced on a light table, in roll configuration. (KPI3: The demonstrators from the project will be widely known to the textile industry through workshops, seminars, professional literature and online videos). This will be used in the near future for workshops, dissemination days and fairs. · Carrying out a feasibility analysis. The technology was applied and demonstrated on-site for use in an industrial setting, with a partner selected from the guidance group. This was finally realized in collaboration with VDS Weaving. All practical research and development results were presented and documented in slides. Both the recordings of the online dissemination moments and the slides are available via https://www.centexbelpresents.be/en/inspect4-0. The code can be tested without obligation on request. The information was provided, provided with the necessary support from our researchers to familiarize the participating companies with the possibilities without focusing on the bells and whistles. Impact analysis The project has contributed to the development of knowledge on the use of camera vision technology for textile defect detection. It served as a platform to connect textile sector partners with technology companies. Despite the implementation of this knowledge at target group companies, more knowledge is still needed. Various events have provided space for interaction and future collaborations. The project attracted a lot of interest, with 48 companies and 6 non-profit organizations participating. There were a total of 125 individual contacts. The results were distributed through various channels, including LinkedIn and the project website. After each event, links to documents and surveys were sent by email to the target companies.

Researcher(s)

• Promoter: Steenackers Gunther

Research team(s)

• Industrial Vision Lab (InViLab)

Project type(s)

• Research Project

Abstract

The determination of local components in human skin from in-vivo measurements is crucial for medical applications, especially for aiding the diagnostic of skin diseases. In the study of skin cancer and burn wounds and more specifically as a methodology for diagnosis of cancer type and identification of skin penetration depth, it is of great relevance to investigate which cell types are present and how these are distributed at or below the skin surface. Consequently, a number of medical inspection techniques have been developed that can be used for the identification of malignant skin properties and more specifically skin cancer types. However, most of the existing techniques are increasingly contested because they either require destructive sampling (biopsy) or only measure on or under the skin surface (hyperspectral imaging) without identification of the penetration depth or detailed physiology of the maligned skin tissue. As a promising non-contact and non-destructive imaging technology, dynamic infrared thermography (DIRT) inspection will be used in combination with hyperspectral imaging (HSI) and physical modeling for fast and accurate skin property identification but also for assisted medical screening as it is possible to differentiate physiological properties based on a combined thermal-hyperspectral response of the skin. In order to optimize the accuracy and speed of tissue screening, the combined HS+IR measurement methodology will be assisted by numerical modeling.

Researcher(s)

• Promoter: Steenackers Gunther

Research team(s)

• Industrial Vision Lab (InViLab)

Project type(s)

• Research Project

Abstract

In spite of its ability to successfully characterize the condition and materials of paintings and other works of art in a non-invasive way, Macro X-Ray Fluorescence imaging (MA-XRF) suffers from a drawback that significantly affects its most valued application: revealing hidden features and overpainted compositions. While the penetrative properties of the primary and secondary X-rays can be used beneficially to reveal subsurface information that is crucial for art historical scholars and conservators, the extent to which a particular layer can be visualized selectively depends on the exclusive presence of an element in that layer. By consequence, layers with a similar elemental signature emerge intermixed in the same distribution image while the exact layer sequence remains unclear. As a result, in many cases, (contested) sample extraction proves mandatory in order to assign the detected elements to a specific layer within the paint stratigraphy. In order to augment chemical imaging with an additional depth-dimension, a dual approach is presented: (1) separating surface signals from deeper signals by expanding the MA-XRF detector angle geometry and exploiting the resulting, potential depth information that lies within the absorption effects on emission line ratios, by adding a level of data-processing to the existing protocol; (2) reconstructing the layer buildup and allocation of the detected signals by including an Infrared thermographic camera (IRT). In order to characterize the number of layers present and their sequence, multi-sine heat excitation will be exploited for the spectral range of 1.5-5μm in combination with dedicated post-processing of the hypercube images in the frequency domain. The proposed multimodal MA-XRF+IRT measurement methodology is developed on paint mockups and validated on historical paintings and wood panels, in collaboration with the Royal Museum of Fine Arts Antwerp.

Researcher(s)

• Promoter: Steenackers Gunther
• Co-promoter: Van der Snickt Geert
• Fellow: Hillen Michaël

Research team(s)

• Industrial Vision Lab (InViLab)

Project type(s)

• Research Project

Abstract

Non-destructive testing using active thermography is still an expanding research area in order to achieve higher accuracy and faster measurements. More and more industrial manufacturers explore the opportunities of active thermography measurements resulting in more complex shapes and materials. Due to these evolutions it becomes nearly impossible to select the most applicable measurement setup in a fast manner. Especially inspections of large parts are a challenge since inspections of the complete part at once is not possible. Dynamic measurements are the solution to inspecting those samples, but consequently this implies new challenges regarding the measurement setup. In order to perform accurate inspections, trial and error is not a suitable solution because this working principle is time-consuming and should be redone every time the test sample changes, the measurement setup alters or when new innovations are discovered. The purpose of this research is to develop and implement an optimisation routine in order to give a suggestion of measurement setup parameters starting from finite element simulations and afterwards updating with knowledge of preliminary measurements. This optimisation routine will be performed using well-known response surface techniques and benchmarked with newly discovered methods. The optimisation routine will be tested on multiple samples in order to inspect the robustness and reliability.

Researcher(s)

• Promoter: Steenackers Gunther
• Fellow: Verspeek Simon

Research team(s)

• Industrial Vision Lab (InViLab)

Project type(s)

• Research Project

Abstract

In the study of historical paintings and as a preparation for restoration activities of such artefacts, it is of great relevance to investigate which materials and degradation products are present and how these are distributed at or below the painting surface. Consequently, a large number of analytical techniques have been developed that can be used for the identification of artists' materials. However, most of the existing techniques are increasingly contested because they require destructive sampling while in situ analysis with mobile equipment provides compositional data from only a limited number of individual points. In response, mobile scanning instruments for chemical imaging were developed, such as macro X-ray fluorescence (MA-XRF), that supply highly specific chemical information, but entail long scanning times for recording full spectra. As an alternative, thermography inspection is used for material parameter identification but also for art inspection as it is particularly fast. Therefore the goal of this research proposal is to eliminate the drawbacks of current inspection techniques by preceding the chemical speciation of different materials in a painting or surface layer (with XRF) with a swift chemical screening with thermography in the nearinfrared range. The resulting multi-sensor inspection methodology combines fast inspection with a slow inspection to achieve more accurate results and faster inspection times, including IR pigment identification algorithm.

Researcher(s)

• Promoter: Janssens Koen
• Co-promoter: Steenackers Gunther

Research team(s)

• Antwerp X-ray Imaging and Spectroscopy (AXIS)

Project type(s)

• Research Project

Abstract

In this project a new methodology for product testing and quality control is developed based on infrared lock-in thermography. Infrared thermography permits to visualize the thermal/ warmup response of objects. In particular, lock-in thermography employs a sinusoidal light source to warm up the object being studied. Although pulsed thermography (PT) is commonly used as thermographic inspection technique, this method is not well suited for inspection of fragile structures (art and biological tissue inspection, blood circulation, …) due to the large instant energy emission which involves insufficient controllability and non-uniformity. On the other hand, with traditional lock-in thermography only one defect depth can be inspected at a time. In addition, at least one steady state period of the sine wave excitation is necessary to obtain quantitative results.

Researcher(s)

• Promoter: Steenackers Gunther
• Co-promoter: Janssens Koen
• Co-promoter: Louarroudi Ebrahim

Research team(s)

• Industrial Vision Lab (InViLab)

Project type(s)

• Research Project

Abstract

Combining finite element models with non-destructive testing has enormous potential for valorization. The objective of this project is to develop a reliable damage detection and localization tool by combining NDT thermography data with FE modeling, making use of system identification. As the amount of experimental data is very high and depending on the resolution of the IR camera, the goal is to use virtual modeling in assistance of the NDT tests in order to gain accuracy and time-efficiency.

Researcher(s)

• Promoter: Steenackers Gunther
• Co-promoter: Lauriks Leen

Research team(s)

• Industrial Vision Lab (InViLab)

Project type(s)

• Research Project

Abstract

Bone cancer affects children and young adults and requires wide removal of bone, leaving large defects. In order to save the limb and to restore its function in a lasting way, dead bone from bone banks or sterilised removed bone (graft) is used to fill the defect and is fixed by plates and screws. Still, in some patients a gap between the dead graft and the remaining living bone is seen, causing a delayed healing which leads to prolonged non-weight bearing periods (>1 year) and reoperations. We aim to reduce the healing time by introducing a predefined compression force to a graft, comparable to methods used in fracture fixation and megaprosthesis ingrowth. However, no literature is available evaluating the compression force and its effect on graft healing. Moreover, as bone cancer is extremely rare, this small patient group is often ignored for research funding to improve the current knowledge. We need to reproduce this compression force in a reliable way in different patients and different bone parts. Therefore we need to develop a standardised surgical procedure and determine the relation between the compression force and the surgical variables, eg screw positioning. Data from in vitro cyclic loading experiments and the patient's characteristics will be used for virtual simulation of compression force during level walking. These data will be essential for the future introduction and development of innovative techniques such as patient-specific instruments and implants.

Researcher(s)

• Promoter: Steenackers Gunther

Research team(s)

• Industrial Vision Lab (InViLab)

Project type(s)

• Research Project

Abstract

The integrity of non-reinforced concrete structures at early stages of construction depends on many factors. One of these is the formation of cracks, which may be crucial in some applications. This problem is of great relevance for deep geological disposal concepts which consider concrete as one of the principle engineered barrier components, and for which the expected service life is > 1000s of years. This is particularly the case within the current Belgian disposal concept in which heat emitting radioactive wastes are post-conditioned in concrete/steel containers, to be placed in a deep underground geological formation using a system of galleries supported by non-reinforced concrete lining. In the early stages of repository construction and waste emplacement, the mechanical integrity of the concrete components is of utmost importance from the point of view of safety and performance. The potential retrievability/reversibility of wastes within a prolonged time period after waste emplacement places additional performance requirements on these concrete structures, which must retain their structural integrity over this period. The principal objective of this PhD is to make a first attempt at developing and implementing a multiscale-based coupled thermo-hydro-mechanical model to study the early age behaviour of nonreinforced concrete. In particular, the PhD student will develop a mathematical model that captures cracking potential due to thermo-hydraulic-mechanical transient conditions. To a limited extent, a secondary objective is also envisaged in which small laboratory-scale experiments may be carried out to derive parameters of importance for the multiscale models as well as for validation purposes.

Researcher(s)

• Promoter: Craeye Bart
• Co-promoter: Steenackers Gunther
• Fellow: Babaei Saeid

Research team(s)

Project type(s)

• Research Project

Abstract

The hip functions as a ball and socket joint, with cartilage layers that cover the joint surfaces on both sides protecting it from impacts and permitting smooth movements. When the cartilage is impaired by mechanical, infectious or inflammatory causes, the joint might eventually wear down - a disabling condition known as osteoarthritis. Recent literature indicates that up to 80% of all hip osteoarthritis cases might be related to subtle variations in the joint geometry.: These variations have been suggested to give rise to peak joint stresses and altered load distributions on the cartilage. Although the mechanism is getting increasingly recognized in the literature, profound understanding of its true impact is lacking. Further, the prevalence of these morphological variations is reported to be much higher than the actual number of patients presenting for treatment. The aim of this thesis is to explore the impact of variation in hip joint anatomy on load distribution during daily living activities. I intend to clarify the role of mechanical drivers in the onset and progression of cartilage lesions of the hip joint by means of advanced multidimensional statistics and personalized load and stress predictions. The final step of this thesis will be to gradually transfer these findings into clinical practice and at the operating theatre by providing virtual pre-surgical planning, accurately implemented during surgery, using state of the art navigation technology.

Researcher(s)

• Promoter: Steenackers Gunther
• Co-promoter: Audenaert Emmanuel

Research team(s)

• Industrial Vision Lab (InViLab)

Project type(s)

• Research Project

Abstract

A new generation of digital cameras makes use of emitted light pulses, more precisely the time between the emission and the reception of the reflected pulse, for computing the depth of the viewed object. This "Time-of-Flight" principle is replacing other 3D-scan strategies such as stereovision and structured light. Though the concept and possibilities of a ToF-camera essentially differs from these that are offered by "classical" optical cameras, the computer vision community still falls back on proven methods for calibration and structure-from-motion issues. We propose new techniques, fully exploiting the Time-of-Flight power, avoiding detection and recognition of features in the image. In a further step, we intend to design a new camera model, more general than the familiar pinhole model, providing a uniform framework for both lateral as depth calibration of ToF-cameras. The theory will be validated by simulations and real experiments (executed by a computer driven robot manipulator). Finally, real life applications will be considered, in cooperation with some of our industrial partners.

Researcher(s)

• Promoter: Penne Rudi
• Co-promoter: Steenackers Gunther
• Fellow: Roios Pedro

Research team(s)

• Industrial Vision Lab (InViLab)

Project type(s)

• Research Project

Abstract

In the study of historical paintings and more specifically as a preparation for restoration activities of such artefacts, it is of great relevance to investigate which materials and degradation products are present and how these are distributed at or below the painting surface. Commonly used non-destructive in situ methods such as X-ray fluorescence (XRF) and X-ray diffraction (XRD), are only used for spot analyses and require several minutes to record a spectrum from a single sample position, resulting in long scanning times required to record the data hypercubes. As an alternative, thermography inspection, as a non-contact and non-destructive technique is used for material parameter identification but also for art inspection as it is possible to differentiate chemical compounds. Therefore the goal of this research proposal is to improve non-invasive macroscopic material characterization of flat objects, both from an industrial and cultural heritage context, by augmenting existing elemental imaging technology with more species specific imaging of organic and inorganic compounds and this by combining the established X-ray based approaches with IR thermography and hyperspectral (HS) images. A combined X-ray, IR thermography and HS technique eliminates the disadvantages of these techniques and results in a faster measurement and material identification technique with respect to measurement time but also accuracy of the material parameter identification.

Researcher(s)

• Promoter: Steenackers Gunther
• Co-promoter: Janssens Koen
• Co-promoter: Ribbens Bart

Research team(s)

• Industrial Vision Lab (InViLab)

Project type(s)

• Research Project

Abstract

This project represents a research contract awarded by the University of Antwerp. The supervisor provides the Antwerp University research mentioned in the title of the project under the conditions stipulated by the university.

Researcher(s)

• Promoter: Demeyer Serge
• Co-promoter: Blondia Chris
• Co-promoter: De Meulenaere Paul
• Co-promoter: Hellinckx Peter
• Co-promoter: Latré Steven
• Co-promoter: Peremans Herbert
• Co-promoter: Steckel Jan
• Co-promoter: Steenackers Gunther
• Co-promoter: Vangheluwe Hans
• Co-promoter: Vanlanduit Steve
• Co-promoter: Weyn Maarten
• Fellow: De Mey Fons
• Fellow: Hristoskova Anna

Research team(s)

• Antwerp Systems and software Modelling (AnSyMo)

Project type(s)

• Research Project

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)

• Promoter: Mertens Luc
• Co-promoter: Penne Rudi
• Co-promoter: Steenackers Gunther

Research team(s)

• Industrial Vision Lab (InViLab)

Project type(s)

• Research Project

Abstract

The hip functions as a ball and socket joint, with cartilage layers that cover the joint surfaces on both sides protecting it from impacts and permitting smooth movements. When the cartilage is impaired by mechanical, infectious or inflammatory causes, the joint might eventually wear down - a disabling condition known as osteoarthritis. Recent literature indicates that up to 80% of all hip osteoarthritis cases might be related to subtle variations in the joint geometry. These variations have been suggested to give rise to peak joint stresses and altered load distributions on the cartilage. Although the mechanism is getting increasingly recognized in the literature, profound understanding of its true impact is lacking. Further, the prevalence of these morphological variations is reported to be much higher than the actual number of patients presenting for treatment. The aim of this thesis is to explore the impact of variation in hip joint anatomy on load distribution during daily living activities. I intend to clarify the role of mechanical drivers in the onset and progression of cartilage lesions of the hip joint by means of advanced multidimensional statistics and personalized load and stress predictions. The final step of this thesis will be to gradually transfer these findings into clinical practice and at the operating theatre by providing virtual pre-surgical planning, accurately implemented during surgery, using state of the art navigation technology.

Researcher(s)

• Promoter: Steenackers Gunther
• Co-promoter: Audenaert Emmanuel

Research team(s)

• Industrial Vision Lab (InViLab)

Project type(s)

• Research Project

Abstract

We focus on 2D point sets with an elliptic shape and 3D point sets with an ellipsoidal shape, e.g. in camera images or a data fusion setting. Noise on these data points forces us to look for robust procedures that derive the quantities we need. Motivating case study: Suppose that the image is taken by a calibrated camera from a ball with known radius, what is the position of this ball relative to the camera?

Researcher(s)

• Promoter: Penne Rudi
• Co-promoter: Mertens Luc
• Co-promoter: Steenackers Gunther

Research team(s)

• Industrial Vision Lab (InViLab)

Project website

Project type(s)

• Research Project