Proceedings of the International Workshop on Medical Ultrasound Tomography: 1.- 3. Nov. 2017, Speyer, Germany

Ultrasound Tomography is an emerging technology for medical imaging that is quickly approaching its clinical utility. Research groups around the globe are engaged in research spanning from theory to practical applications. The International Workshop on Medical Ultrasound Tomography (1.-3. November 2017, Speyer, Germany) brought together scientists to exchange their knowledge and discuss new ideas and results in order to boost the research in Ultrasound Tomography

Identification et caractérisation des conditions aux limites pour des simulations biomécaniques patient-spécifiques

The purpose of the work is to find a way to estimate the boundary conditions of the liver. They play an essential role in forming the predictive capacity of the biomechanical model, but are presented mainly by ligaments, vessels, and surrounding organs, the properties of which are "patient specific" and cannot be measured reliably. We propose to present the boundary conditions as nonlinear springs and estimate their parameters. Firstly, we create a generalized initial approximation using the constitutive law available in the literature and a statistical atlas, obtained from a set of models with segmented ligaments. Then, we correct the approximation based on the nonlinear Kalman filtering approach, which assimilates data obtained from a modality during surgical intervention. To assess the approach, we performed experiments for both synthetic and real data. The results show a certain improvement in simulation accuracy for the cases with estimated boundaries.L'objectif de ce travail est trouvé un moyen d'estimer les conditions aux limites du foie. Elles jouent un rôle essentiel dans la capacité de prédiction du modèle biomécanique, mais sont principalement présentées par les ligaments, les vaisseaux et les organes environnants, dont les propriétés sont "spécifiques au patient" et ne peuvent être mesurées fidèlement. Nous proposons de présenter ces conditions comme des ressorts non linéaires et d'estimer ses paramètres. D’abord, nous créons une approximation initiale en utilisant la loi constitutive disponible dans la littérature et un atlas statistique obtenu à partir des modèles avec des ligaments segmentés. Après, nous la corrigeons basée sur le filtrage de Kalman non linéaire, qui assimile les données acquises d'une modalité pendant la chirurgie. Pour évaluation, nous avons réalisé des expériences avec des données synthétiques et réelles. Les résultats montrent une amélioration de la précision pour les cas avec des limites estimées

Measuring at high sensitivity by nuclear imaging the permeation of ultrasmall nanoparticles across polymeric and biological membranes

L'objectif de ce projet est de développer une nouvelle technique basée sur l'imagerie nucléaire pour mesurer à haute sensibilité et en temps réel la perméation de substances (molécules ou nanoparticules) à travers des polymères et des membranes biologiques. Les gants de polymère sont utilisés comme équipements de protection individuelle dans de nombreux domaines d'activité professionnelle où les risques chimiques sont présents. L'efficacité des gants à bloquer le passage de certaines substances est mesurée par des dispositifs dédiés appelés cellules de diffusion (DFC). Ces dispositifs sont constitués d'un compartiment donneur (DC) et d'un compartiment accepteur (AC), séparés par une membrane. Habituellement, la perméation des substances à travers les membranes est mesurée en prélevant des échantillons dans l'AC à différents moments, afin de révéler les profils de perméation à partir desquels les paramètres clés de perméation peuvent être extraits. Cependant, lorsqu'il faut mesurer le passage de faibles concentrations de composés potentiellement toxiques (par exemple, des pesticides ou des agents de chimiothérapie), ou de nanoparticules (NPs), les limites de détection des techniques analytiques actuelles sont généralement insuffisantes pour révéler leur profil de perméation avec précision. Il est donc difficile d'extraire les paramètres de perméation tels que le temps de latence, l'influx et les coefficients de diffusion. Afin de mesurer de manière précise et quantitative les paramètres cinétiques décrivant le passage de substances (molécules ou NPs) à travers des membranes polymériques et des membranes biologiques, il est nécessaire de développer une DFC utilisant un mode de détection avec un très haut degré de sensibilité et permettant des mesures en continu. Dans ce projet de recherche, une nouvelle technologie a été développée sous la forme d'une DFC adaptée à l'imagerie nucléaire. La tomographie par émission de positrons (TEP) permet la détection de molécules et de NPs avec un degré de sensibilité bien supérieur aux méthodes spectroscopiques et spectrométriques habituellement employées pour la détection des processus de perméation. Des études de diffusion des petites molécules radiomarquées à travers des membranes de dialyse ont d'abord été réalisées, afin de prouver le concept de cette nouvelle technologie. Ensuite, la perméation de NPs radiomarquées à travers des gants et des membranes biologiques a été évaluée. Les nanoparticules d'or (AuNPS) ont été utilisées comme type de contaminant car ce type de produit, de plus en plus utilisé en médecine, est particulièrement difficile à détecter par les techniques de mesure habituelles dans les tests de perméation. Les données acquises au cours de ces études ont permis de mettre en évidence des profils de perméation de NPs avec une très haute résolution temporelle, et une sensibilité de détection permettant de calculer tous les principaux paramètres décrivant la perméation des contaminants à travers les membranes (coefficient de diffusion, temps de latence, taux de perméation, etc). Cette technologie pourrait être utilisée pour évaluer la diffusion de matières dangereuses à travers les gants utilisés comme équipement de protection. La diffusion d'ingrédients actifs à partir de formulations topiques, buccales et ophtalmiques, ainsi que la diffusion de produits cosmétiques appliqués sur la peau, pourront aussi être étudiées.The objective of this project is to develop a new measurement technique based on nuclear imaging, to measure at high sensitivity and in real-time, the permeation of substances (molecules or nanoparticles) through polymers and biological membranes. Polymer gloves are used as personal protective equipment in many areas of professional activity where chemical risks are present. The effectiveness of polymers in blocking the passage of certain substances is measured by dedicated devices known as diffusion cells (DFC). These devices are made of a donor (DC) and an acceptor (AC) compartment, separated by a membrane. Usually, the permeation of substances across membranes is measured by sampling from the AC at different time points, to reveal permeation profiles from which key permeation parameters can be extracted. However, for measuring the passage of low concentration of potentially toxic compounds (e.g. pesticides or chemotherapy agents), or nanoparticles (NPs), the detection limits of current analytical techniques are generally insufficient to reveal their permeation profile accurately. Thus, itis hard to extract permeation parameters as the lag time, the influx, and diffusion coefficients. In order to accurately and quantitatively measure the kinetic parameters describing the passage of substances (molecules or NPs) through polymeric and biological membranes, it is necessary to develop DFCs using a high sensitivity detection modality that allows continuous measurements. In this research project, a new technology was developed in the form of a DFC adapted to nuclear imaging. Nuclear imaging such as positron emission tomography (PET) allows the detection of molecules and NPs with a degree of sensitivity far superior to the spectroscopic and spectrometric methods usually employed for the detection of permeation processes. Diffusion studies of small molecular weight radiolabeled molecules across dialysis membranes was first performed, to prove the concept of this novel technology. Then, the permeation of radiolabeled NPs through gloves and biological membranes was evaluated. Gold nanoparticles (AuNPs) were used as a type of contaminant because they are increasingly used in medicine and because it is particularly difficult to detect them by the usual measurement techniques in permeation tests. The data acquired during these studies allowed to reveal NP permeation profiles with a very high temporal resolution, at a detection sensitivity allowing to calculate all the main parameters describing the permeation of contaminants through membranes (lag time, permeation rate, diffusion coefficient, etc). The technology could be used to assess the diffusion of hazardous materials through gloves used as protective gear, as well as the diffusion of active ingredients and NPs from topical, buccal and ophthalmic drug formulations, as well as the diffusion of cosmetic products applied to the skin

Atlas-based segmentation and classification of magnetic resonance brain images

A wide range of different image modalities can be found today in medical imaging. These modalities allow the physician to obtain a non-invasive view of the internal organs of the human body, such as the brain. All these three dimensional images are of extreme importance in several domains of medicine, for example, to detect pathologies, follow the evolution of these pathologies, prepare and realize surgical planning with, or without, the help of robot systems or for statistical studies. Among all the medical image modalities, Magnetic Resonance (MR) imaging has become of great interest in many research areas due to its great spatial and contrast image resolution. It is therefore perfectly suited for anatomic visualization of the human body such as deep structures and tissues of the brain. Medical image analysis is a complex task because medical images usually involve a large amount of data and they sometimes present some undesirable artifacts, as for instance the noise. However, the use of a priori knowledge in the analysis of these images can greatly simplify this task. This prior information is usually represented by the reference images or atlases. Modern brain atlases are derived from high resolution cryosections or in vivo images, single subject-based or population-based, and they provide detailed images that may be interactively and easily examined in their digital format in computer assisted diagnosis or intervention. Then, in order to efficiently combine all this information, a battery of registration techniques is emerging based on transformations that bring two medical images into voxel-to-voxel correspondence. One of the main aims of this thesis is to outline the importance of including prior knowledge in the medical image analysis framework and the indispensable role of registration techniques in this task. In order to do that, several applications using atlas information are presented. First, the atlas-based segmentation in normal anatomy is shown as it is a key application of medical image analysis using prior knowledge. It consists of registering the brain images derived from different subjects and modalities within the atlas coordinate system to improve the localization and delineation of the structures of interest. However, the use of an atlas can be problematic in some particular cases where some structures, for instance a tumor or a sulcus, exists in the subject and not in the atlas. In order to solve this limitation of the atlases, a new atlas-based segmentation method for pathological brains is proposed in this thesis as well as a validation method to assess this new approach. Results show that deep structures of the brain can still be efficiently segmented using an anatomic atlas even if they are largely deformed because of a lesion. The importance of including a priori knowledge is also presented in the application of brain tissue classification. The prior information represented by the tissue templates can be included in a brain tissue segmentation approach thanks to the registration techniques. This is another important issue presented in this thesis and it is analyzed through a comparative study of several non-supervised classification techniques. These methods are selected to represent the whole range of prior information that can be used in the classification process: the image intensity, the local spatial model, and the anatomical priors. Results show that the registration between the subject and the tissue templates allows the use of prior information but the accuracy of both the prior information and the registration highly influence the performance of the classification techniques. Another aim of this thesis is to present the concept of dynamic medical image analysis, in which the prior knowledge and the registration techniques are also of main importance. Actually, many medical image applications have the objective of statically analyzing one single image, as for instance in the case of atlas-based segmentation or brain tissue classification. But in other cases the implicit idea of changes detection is present. Intuitively, since the human body is changing continuously, we would like to do the image analysis from a dynamic point of view by detecting these changes, and by comparing them afterwards with templates to know if they are normal. The need of such approaches is even more evident in the case of many brain pathologies such as tumors, multiple sclerosis or degenerative diseases. In these cases, the key point is not only to detect but also to quantify and even characterize the evolving pathology. The evaluation of lesion variations over time can be very useful, for instance in the pharmaceutical research and clinical follow up. Of course, a sequence of images is needed in order to do such an analysis. Two approaches dealing with the idea of change detection are proposed as the last (but not least) issue presented in this work. The first one consists of performing a static analysis of each image forming the data set and, then, of comparing them. The second one consists of analyzing the non-rigid transformation between the sequence images instead of the images itself. Finally, both static and dynamic approaches are illustrated with a potential application: the cortical degeneration study is done using brain tissue segmentation, and the study of multiple sclerosis lesion evolution is performed by non-rigid deformation analysis. In conclusion, the importance of including a priori information encoded in the brain atlases in medical image analysis has been put in evidence with a wide range of possible applications. In the same way, the key role of registration techniques is shown not only as an efficient way to combine all the medical image modalities but also as a main element in the dynamic medical image analysis

Proceedings of the 2018 Canadian Society for Mechanical Engineering (CSME) International Congress

Published proceedings of the 2018 Canadian Society for Mechanical Engineering (CSME) International Congress, hosted by York University, 27-30 May 2018