Validation of real-time methylation-specific PCR to determine O6-methylguanine-DNA methyltransferase gene promoter methylation in glioma.

Epigenetic silencing of the DNA repair protein O(6)-methylguanine-DNA methyltransferase (MGMT) by promoter methylation predicts successful alkylating agent therapy, such as with temozolomide, in glioblastoma patients. Stratified therapy assignment of patients in prospective clinical trials according to tumor MGMT status requires a standardized diagnostic test, suitable for high-throughput analysis of small amounts of formalin-fixed, paraffin-embedded tumor tissue. A direct, real-time methylation-specific PCR (MSP) assay was developed to determine methylation status of the MGMT gene promoter. Assay specificity was obtained by selective amplification of methylated DNA sequences of sodium bisulfite-modified DNA. The copy number of the methylated MGMT promoter, normalized to the beta-actin gene, provides a quantitative test result. We analyzed 134 clinical glioma samples, comparing the new test with the previously validated nested gel-based MSP assay, which yields a binary readout. A cut-off value for the MGMT methylation status was suggested by fitting a bimodal normal mixture model to the real-time results, supporting the hypothesis that there are two distinct populations within the test samples. Comparison of the tests showed high concordance of the results (82/91 [90%]; Cohen's kappa = 0.80; 95% confidence interval, 0.82-0.95). The direct, real-time MSP assay was highly reproducible (Pearson correlation 0.996) and showed valid test results for 93% (125/134) of samples compared with 75% (94/125) for the nested, gel-based MSP assay. This high-throughput test provides an important pharmacogenomic tool for individualized management of alkylating agent chemotherapy.

15-OR: Multi-phase postmortem CT angiography of pulmonary embolism: technique-related artefacts or real findings?

Multi-phase postmortem CT angiography (MPMCTA) is recognized as a valuable tool to explore the vascular system, with higher sensitivity than conventional autopsy. However, a limitation is the impossibility to diagnose pulmonary embolism (PE) due to post-mortem blood clots situated in pulmonary arteries. The purpose of this study was to explore an eventual possibility to distinguish between real PE and artefacts mimicking PE. Our study included 416 medico-legal cases. All of them underwent MPMCTA, conventional autopsy and histological examination. We selected cases presenting arterial luminal filling defects in the pulmonary arteries. Their radiological interpretation was confronted to the one of autopsy and histological examination. We also investigated an eventual correlation between artefacts in pulmonary arteries and those in other parts of the vascular system. In 123 cases, filling defects of pulmonary arteries were described during MPMCTA. In 57 cases, this was interpreted as artefact and in 4 cases as suspected PE. In 62 cases only a differential diagnosis was made. Autopsy and histology could clearly identify the artefacts as such. Only one case of real PE was radiologically misinterpreted as artefact. In 6 of the 62 cases with no interpretation a PE was diagnosed. In 3 out of 4 suspected cases, PE was confirmed. We found out that filling defects in pulmonary arteries are nearly always associated to other vascular artefacts. Therefore, we suggest following some rules for radiological interpretation in order to allow a reliable diagnosis of pulmonary embolism after MPMCTA.

Behavioural queueing : cellular automata and a laboratory experiment

Dans cette thèse, nous étudions les aspects comportementaux d'agents qui interagissent dans des systèmes de files d'attente à l'aide de modèles de simulation et de méthodologies expérimentales. Chaque période les clients doivent choisir un prestataire de servivce. L'objectif est d'analyser l'impact des décisions des clients et des prestataires sur la formation des files d'attente. Dans un premier cas nous considérons des clients ayant un certain degré d'aversion au risque. Sur la base de leur perception de l'attente moyenne et de la variabilité de cette attente, ils forment une estimation de la limite supérieure de l'attente chez chacun des prestataires. Chaque période, ils choisissent le prestataire pour lequel cette estimation est la plus basse. Nos résultats indiquent qu'il n'y a pas de relation monotone entre le degré d'aversion au risque et la performance globale. En effet, une population de clients ayant un degré d'aversion au risque intermédiaire encoure généralement une attente moyenne plus élevée qu'une population d'agents indifférents au risque ou très averses au risque. Ensuite, nous incorporons les décisions des prestataires en leur permettant d'ajuster leur capacité de service sur la base de leur perception de la fréquence moyenne d'arrivées. Les résultats montrent que le comportement des clients et les décisions des prestataires présentent une forte "dépendance au sentier". En outre, nous montrons que les décisions des prestataires font converger l'attente moyenne pondérée vers l'attente de référence du marché. Finalement, une expérience de laboratoire dans laquelle des sujets jouent le rôle de prestataire de service nous a permis de conclure que les délais d'installation et de démantèlement de capacité affectent de manière significative la performance et les décisions des sujets. En particulier, les décisions du prestataire, sont influencées par ses commandes en carnet, sa capacité de service actuellement disponible et les décisions d'ajustement de capacité qu'il a prises, mais pas encore implémentées. - Queuing is a fact of life that we witness daily. We all have had the experience of waiting in line for some reason and we also know that it is an annoying situation. As the adage says "time is money"; this is perhaps the best way of stating what queuing problems mean for customers. Human beings are not very tolerant, but they are even less so when having to wait in line for service. Banks, roads, post offices and restaurants are just some examples where people must wait for service. Studies of queuing phenomena have typically addressed the optimisation of performance measures (e.g. average waiting time, queue length and server utilisation rates) and the analysis of equilibrium solutions. The individual behaviour of the agents involved in queueing systems and their decision making process have received little attention. Although this work has been useful to improve the efficiency of many queueing systems, or to design new processes in social and physical systems, it has only provided us with a limited ability to explain the behaviour observed in many real queues. In this dissertation we differ from this traditional research by analysing how the agents involved in the system make decisions instead of focusing on optimising performance measures or analysing an equilibrium solution. This dissertation builds on and extends the framework proposed by van Ackere and Larsen (2004) and van Ackere et al. (2010). We focus on studying behavioural aspects in queueing systems and incorporate this still underdeveloped framework into the operations management field. In the first chapter of this thesis we provide a general introduction to the area, as well as an overview of the results. In Chapters 2 and 3, we use Cellular Automata (CA) to model service systems where captive interacting customers must decide each period which facility to join for service. They base this decision on their expectations of sojourn times. Each period, customers use new information (their most recent experience and that of their best performing neighbour) to form expectations of sojourn time at the different facilities. Customers update their expectations using an adaptive expectations process to combine their memory and their new information. We label "conservative" those customers who give more weight to their memory than to the xiv Summary new information. In contrast, when they give more weight to new information, we call them "reactive". In Chapter 2, we consider customers with different degree of risk-aversion who take into account uncertainty. They choose which facility to join based on an estimated upper-bound of the sojourn time which they compute using their perceptions of the average sojourn time and the level of uncertainty. We assume the same exogenous service capacity for all facilities, which remains constant throughout. We first analyse the collective behaviour generated by the customers' decisions. We show that the system achieves low weighted average sojourn times when the collective behaviour results in neighbourhoods of customers loyal to a facility and the customers are approximately equally split among all facilities. The lowest weighted average sojourn time is achieved when exactly the same number of customers patronises each facility, implying that they do not wish to switch facility. In this case, the system has achieved the Nash equilibrium. We show that there is a non-monotonic relationship between the degree of risk-aversion and system performance. Customers with an intermediate degree of riskaversion typically achieve higher sojourn times; in particular they rarely achieve the Nash equilibrium. Risk-neutral customers have the highest probability of achieving the Nash Equilibrium. Chapter 3 considers a service system similar to the previous one but with risk-neutral customers, and relaxes the assumption of exogenous service rates. In this sense, we model a queueing system with endogenous service rates by enabling managers to adjust the service capacity of the facilities. We assume that managers do so based on their perceptions of the arrival rates and use the same principle of adaptive expectations to model these perceptions. We consider service systems in which the managers' decisions take time to be implemented. Managers are characterised by a profile which is determined by the speed at which they update their perceptions, the speed at which they take decisions, and how coherent they are when accounting for their previous decisions still to be implemented when taking their next decision. We find that the managers' decisions exhibit a strong path-dependence: owing to the initial conditions of the model, the facilities of managers with identical profiles can evolve completely differently. In some cases the system becomes "locked-in" into a monopoly or duopoly situation. The competition between managers causes the weighted average sojourn time of the system to converge to the exogenous benchmark value which they use to estimate their desired capacity. Concerning the managers' profile, we found that the more conservative Summary xv a manager is regarding new information, the larger the market share his facility achieves. Additionally, the faster he takes decisions, the higher the probability that he achieves a monopoly position. In Chapter 4 we consider a one-server queueing system with non-captive customers. We carry out an experiment aimed at analysing the way human subjects, taking on the role of the manager, take decisions in a laboratory regarding the capacity of a service facility. We adapt the model proposed by van Ackere et al (2010). This model relaxes the assumption of a captive market and allows current customers to decide whether or not to use the facility. Additionally the facility also has potential customers who currently do not patronise it, but might consider doing so in the future. We identify three groups of subjects whose decisions cause similar behavioural patterns. These groups are labelled: gradual investors, lumpy investors, and random investor. Using an autocorrelation analysis of the subjects' decisions, we illustrate that these decisions are positively correlated to the decisions taken one period early. Subsequently we formulate a heuristic to model the decision rule considered by subjects in the laboratory. We found that this decision rule fits very well for those subjects who gradually adjust capacity, but it does not capture the behaviour of the subjects of the other two groups. In Chapter 5 we summarise the results and provide suggestions for further work. Our main contribution is the use of simulation and experimental methodologies to explain the collective behaviour generated by customers' and managers' decisions in queueing systems as well as the analysis of the individual behaviour of these agents. In this way, we differ from the typical literature related to queueing systems which focuses on optimising performance measures and the analysis of equilibrium solutions. Our work can be seen as a first step towards understanding the interaction between customer behaviour and the capacity adjustment process in queueing systems. This framework is still in its early stages and accordingly there is a large potential for further work that spans several research topics. Interesting extensions to this work include incorporating other characteristics of queueing systems which affect the customers' experience (e.g. balking, reneging and jockeying); providing customers and managers with additional information to take their decisions (e.g. service price, quality, customers' profile); analysing different decision rules and studying other characteristics which determine the profile of customers and managers.

First detection of Waddlia chondrophila in Africa using SYBR Green real-time PCR on veterinary samples.

Waddlia chondrophila is a strict intracellular microorganism belonging to the order Chlamydiales that has been isolated twice from aborted bovine fetuses, once in USA and once in Germany. This bacterium is now considered as an abortigenic agent in cattle. However, no information is available regarding the presence of this bacterium in Africa. Given the low sensitivity of cell culture to recover such an obligate intracellular bacterium, molecular-based diagnostic approaches are warranted. This report describes the development of a quantitative SYBR Green real-time PCR assay targeting the recA gene of W. chondrophila. Analytical sensitivity was 10 copies of control plasmid DNA per reaction. No cross-amplification was observed when testing pathogens that can cause abortion in cattle. The PCR exhibited a good intra-run and inter-run reproducibility. This real-time PCR was then applied to 150 vaginal swabs taken from Tunisian cows that have aborted. Twelve samples revealed to be Waddlia positive, suggesting a possible role of this bacterium in this setting. This new real-time PCR assay represents a diagnostic tool that may be used to further study the prevalence of Waddlia infection.

Postmortem imaging: real or virtual?

Since the earliest years after the discovery of X-rays, radiological images have been used successfully for answering medico-legal and forensic questions. The possibility to evaluate the inside of a body without actually opening it has been appreciated and used in forensic pathology since then. However, the introduction of modern cross-sectional imaging techniques into post-mortem investigations has created controversial discussions among the medico-legal community. Terms like "Virtual Autopsy" and "Necro-Radiology" have led to confusion and controversy concerning the role of radiological techniques in forensic case work. Regardless, the use of those techniques in post-mortem investigations is increasing, especially the one of Muti-Detector Computed Tomography (MDCT). But also other imaging techniques such as Postmortem Angiography and Magnetic Resonance Imaging are increasingly applied. This presentation shall give an overview over the different techniques of postmortem imaging. It will critically explain their advantages and limitations in forensic death investigations compared to conventional autopsy. The role of postmortem imaging shall be discussed in order to distinguish between real state of the art and virtual reality.

Quantification of thermotolerant campylobacter in Swiss water treatment plants, by real-time quantitative polymerase chain reaction

A total of 49 wastewater samples from 23 different wastewater treatment plants (WWTPs) were analyzed using real-time quantitative polymerase chain reaction for the presence and quantity of thermotolerant campylobacters. Thermotolerant campylobacters were detected in 87.5% (21/24) and 64% (16/25) of untreated and treated wastewater samples, respectively. Their concentration was sufficiently high to be quantified in 20.4% (10/49) of the samples. In these samples, the concentration ranged from 68 000 to 2292 000 cells/L in untreated wastewater and from 10 800 to 28 000 cells/L in treated water. We conclude that thermotolerant campylobacters present a health hazard for workers at WWTPs in Switzerland. [Authors]

Real-time, in vivo intracellular recordings of caterpillar-induced depolarization waves in sieve elements using aphid electrodes.

Plants propagate electrical signals in response to artificial wounding. However, little is known about the electrophysiological responses of the phloem to wounding, and whether natural damaging stimuli induce propagating electrical signals in this tissue. Here, we used living aphids and the direct current (DC) version of the electrical penetration graph (EPG) to detect changes in the membrane potential of Arabidopsis sieve elements (SEs) during caterpillar wounding. Feeding wounds in the lamina induced fast depolarization waves in the affected leaf, rising to maximum amplitude (c. 60 mV) within 2 s. Major damage to the midvein induced fast and slow depolarization waves in unwounded neighbor leaves, but only slow depolarization waves in non-neighbor leaves. The slow depolarization waves rose to maximum amplitude (c. 30 mV) within 14 s. Expression of a jasmonate-responsive gene was detected in leaves in which SEs displayed fast depolarization waves. No electrical signals were detected in SEs of unwounded neighbor leaves of plants with suppressed expression of GLR3.3 and GLR3.6. EPG applied as a novel approach to plant electrophysiology allows cell-specific, robust, real-time monitoring of early electrophysiological responses in plant cells to damage, and is potentially applicable to a broad range of plant-herbivore interactions.

Enhanced-reality video fluorescence: a real-time assessment of intestinal viability.

OBJECTIVE: Our aim was to evaluate a fluorescence-based enhanced-reality system to assess intestinal viability in a laparoscopic mesenteric ischemia model. MATERIALS AND METHODS: A small bowel loop was exposed, and 3 to 4 mesenteric vessels were clipped in 6 pigs. Indocyanine green (ICG) was administered intravenously 15 minutes later. The bowel was illuminated with an incoherent light source laparoscope (D-light-P, KarlStorz). The ICG fluorescence signal was analyzed with Ad Hoc imaging software (VR-RENDER), which provides a digital perfusion cartography that was superimposed to the intraoperative laparoscopic image [augmented reality (AR) synthesis]. Five regions of interest (ROIs) were marked under AR guidance (1, 2a-2b, 3a-3b corresponding to the ischemic, marginal, and vascularized zones, respectively). One hour later, capillary blood samples were obtained by puncturing the bowel serosa at the identified ROIs and lactates were measured using the EDGE analyzer. A surgical biopsy of each intestinal ROI was sent for mitochondrial respiratory rate assessment and for metabolites quantification. RESULTS: Mean capillary lactate levels were 3.98 (SD = 1.91) versus 1.05 (SD = 0.46) versus 0.74 (SD = 0.34) mmol/L at ROI 1 versus 2a-2b (P = 0.0001) versus 3a-3b (P = 0.0001), respectively. Mean maximal mitochondrial respiratory rate was 104.4 (±21.58) pmolO2/second/mg at the ROI 1 versus 191.1 ± 14.48 (2b, P = 0.03) versus 180.4 ± 16.71 (3a, P = 0.02) versus 199.2 ± 25.21 (3b, P = 0.02). Alanine, choline, ethanolamine, glucose, lactate, myoinositol, phosphocholine, sylloinositol, and valine showed statistically significant different concentrations between ischemic and nonischemic segments. CONCLUSIONS: Fluorescence-based AR may effectively detect the boundary between the ischemic and the vascularized zones in this experimental model.

Tabagisme passif et santé respiratoire de l'adulte: une menace bien réelle [ETS and respiratory health in adults: a real threat]

Hazards due to active smoking are known for a long time. On the other hand, the importance of the harmful effects of passive smoking are less recognized, having been shown initially mainly in the pediatric population. However, the importance of significant increased risks on various respiratory pathologies of the adult are well known today, in a Swiss society where the number of active smokers remains high, leading to a high prevalence of passive smoking exposure On the basis of recent publications and updates of various organizations, we propose a review of the history, noxious mechanisms and effects on various respiratory pathologies of the exposure to passive smoke in adults.

Navigator-gated and real-time motion corrected free-breathing MR Imaging of myocardial late enhancement.

PURPOSE: A new magnetic resonance imaging approach for detection of myocardial late enhancement during free-breathing was developed. METHODS AND RESULTS: For suppression of respiratory motion artifacts, a prospective navigator technology including real-time motion correction and a local navigator restore was implemented. Subject specific inversion times were defined from images with incrementally increased inversion times acquired during a single dynamic scout navigator-gated and real-time motion corrected free-breathing scan. Subsequently, MR-imaging of myocardial late enhancement was performed with navigator-gated and real-time motion corrected adjacent short axis and long axis (two, three and four chamber) views. This alternative approach was investigated in 7 patients with history of myocardial infarction 12 min after i. v. administration of 0.2 mmol/kg body weight gadolinium-DTPA. CONCLUSION: With the presented navigator-gated and real-time motion corrected sequence for MR-imaging of myocardial late enhancement data can be completely acquired during free-breathing. Time constraints of a breath-hold technique are abolished and optimized patient specific inversion time is ensured.

Machine learning for geospatial data : algorithms, software tools and case studies

Résumé Cette thèse est consacrée à l'analyse, la modélisation et la visualisation de données environnementales à référence spatiale à l'aide d'algorithmes d'apprentissage automatique (Machine Learning). L'apprentissage automatique peut être considéré au sens large comme une sous-catégorie de l'intelligence artificielle qui concerne particulièrement le développement de techniques et d'algorithmes permettant à une machine d'apprendre à partir de données. Dans cette thèse, les algorithmes d'apprentissage automatique sont adaptés pour être appliqués à des données environnementales et à la prédiction spatiale. Pourquoi l'apprentissage automatique ? Parce que la majorité des algorithmes d'apprentissage automatiques sont universels, adaptatifs, non-linéaires, robustes et efficaces pour la modélisation. Ils peuvent résoudre des problèmes de classification, de régression et de modélisation de densité de probabilités dans des espaces à haute dimension, composés de variables informatives spatialisées (« géo-features ») en plus des coordonnées géographiques. De plus, ils sont idéaux pour être implémentés en tant qu'outils d'aide à la décision pour des questions environnementales allant de la reconnaissance de pattern à la modélisation et la prédiction en passant par la cartographie automatique. Leur efficacité est comparable au modèles géostatistiques dans l'espace des coordonnées géographiques, mais ils sont indispensables pour des données à hautes dimensions incluant des géo-features. Les algorithmes d'apprentissage automatique les plus importants et les plus populaires sont présentés théoriquement et implémentés sous forme de logiciels pour les sciences environnementales. Les principaux algorithmes décrits sont le Perceptron multicouches (MultiLayer Perceptron, MLP) - l'algorithme le plus connu dans l'intelligence artificielle, le réseau de neurones de régression généralisée (General Regression Neural Networks, GRNN), le réseau de neurones probabiliste (Probabilistic Neural Networks, PNN), les cartes auto-organisées (SelfOrganized Maps, SOM), les modèles à mixture Gaussiennes (Gaussian Mixture Models, GMM), les réseaux à fonctions de base radiales (Radial Basis Functions Networks, RBF) et les réseaux à mixture de densité (Mixture Density Networks, MDN). Cette gamme d'algorithmes permet de couvrir des tâches variées telle que la classification, la régression ou l'estimation de densité de probabilité. L'analyse exploratoire des données (Exploratory Data Analysis, EDA) est le premier pas de toute analyse de données. Dans cette thèse les concepts d'analyse exploratoire de données spatiales (Exploratory Spatial Data Analysis, ESDA) sont traités selon l'approche traditionnelle de la géostatistique avec la variographie expérimentale et selon les principes de l'apprentissage automatique. La variographie expérimentale, qui étudie les relations entre pairs de points, est un outil de base pour l'analyse géostatistique de corrélations spatiales anisotropiques qui permet de détecter la présence de patterns spatiaux descriptible par une statistique. L'approche de l'apprentissage automatique pour l'ESDA est présentée à travers l'application de la méthode des k plus proches voisins qui est très simple et possède d'excellentes qualités d'interprétation et de visualisation. Une part importante de la thèse traite de sujets d'actualité comme la cartographie automatique de données spatiales. Le réseau de neurones de régression généralisée est proposé pour résoudre cette tâche efficacement. Les performances du GRNN sont démontrées par des données de Comparaison d'Interpolation Spatiale (SIC) de 2004 pour lesquelles le GRNN bat significativement toutes les autres méthodes, particulièrement lors de situations d'urgence. La thèse est composée de quatre chapitres : théorie, applications, outils logiciels et des exemples guidés. Une partie importante du travail consiste en une collection de logiciels : Machine Learning Office. Cette collection de logiciels a été développée durant les 15 dernières années et a été utilisée pour l'enseignement de nombreux cours, dont des workshops internationaux en Chine, France, Italie, Irlande et Suisse ainsi que dans des projets de recherche fondamentaux et appliqués. Les cas d'études considérés couvrent un vaste spectre de problèmes géoenvironnementaux réels à basse et haute dimensionnalité, tels que la pollution de l'air, du sol et de l'eau par des produits radioactifs et des métaux lourds, la classification de types de sols et d'unités hydrogéologiques, la cartographie des incertitudes pour l'aide à la décision et l'estimation de risques naturels (glissements de terrain, avalanches). Des outils complémentaires pour l'analyse exploratoire des données et la visualisation ont également été développés en prenant soin de créer une interface conviviale et facile à l'utilisation. Machine Learning for geospatial data: algorithms, software tools and case studies Abstract The thesis is devoted to the analysis, modeling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense machine learning can be considered as a subfield of artificial intelligence. It mainly concerns with the development of techniques and algorithms that allow computers to learn from data. In this thesis machine learning algorithms are adapted to learn from spatial environmental data and to make spatial predictions. Why machine learning? In few words most of machine learning algorithms are universal, adaptive, nonlinear, robust and efficient modeling tools. They can find solutions for the classification, regression, and probability density modeling problems in high-dimensional geo-feature spaces, composed of geographical space and additional relevant spatially referenced features. They are well-suited to be implemented as predictive engines in decision support systems, for the purposes of environmental data mining including pattern recognition, modeling and predictions as well as automatic data mapping. They have competitive efficiency to the geostatistical models in low dimensional geographical spaces but are indispensable in high-dimensional geo-feature spaces. The most important and popular machine learning algorithms and models interesting for geo- and environmental sciences are presented in details: from theoretical description of the concepts to the software implementation. The main algorithms and models considered are the following: multi-layer perceptron (a workhorse of machine learning), general regression neural networks, probabilistic neural networks, self-organising (Kohonen) maps, Gaussian mixture models, radial basis functions networks, mixture density networks. This set of models covers machine learning tasks such as classification, regression, and density estimation. Exploratory data analysis (EDA) is initial and very important part of data analysis. In this thesis the concepts of exploratory spatial data analysis (ESDA) is considered using both traditional geostatistical approach such as_experimental variography and machine learning. Experimental variography is a basic tool for geostatistical analysis of anisotropic spatial correlations which helps to understand the presence of spatial patterns, at least described by two-point statistics. A machine learning approach for ESDA is presented by applying the k-nearest neighbors (k-NN) method which is simple and has very good interpretation and visualization properties. Important part of the thesis deals with a hot topic of nowadays, namely, an automatic mapping of geospatial data. General regression neural networks (GRNN) is proposed as efficient model to solve this task. Performance of the GRNN model is demonstrated on Spatial Interpolation Comparison (SIC) 2004 data where GRNN model significantly outperformed all other approaches, especially in case of emergency conditions. The thesis consists of four chapters and has the following structure: theory, applications, software tools, and how-to-do-it examples. An important part of the work is a collection of software tools - Machine Learning Office. Machine Learning Office tools were developed during last 15 years and was used both for many teaching courses, including international workshops in China, France, Italy, Ireland, Switzerland and for realizing fundamental and applied research projects. Case studies considered cover wide spectrum of the real-life low and high-dimensional geo- and environmental problems, such as air, soil and water pollution by radionuclides and heavy metals, soil types and hydro-geological units classification, decision-oriented mapping with uncertainties, natural hazards (landslides, avalanches) assessments and susceptibility mapping. Complementary tools useful for the exploratory data analysis and visualisation were developed as well. The software is user friendly and easy to use.

Real-time full field laser Doppler imaging.

We present a full field laser Doppler imaging instrument, which enables real-time in vivo assessment of blood flow in dermal tissue and skin. This instrument monitors the blood perfusion in an area of about 50 cm(2) with 480 × 480 pixels per frame at a rate of 12-14 frames per second. Smaller frames can be monitored at much higher frame rates. We recorded the microcirculation in healthy skin before, during and after arterial occlusion. In initial clinical case studies, we imaged the microcirculation in burned skin and monitored the recovery of blood flow in a skin flap during reconstructive surgery indicating the high potential of LDI for clinical applications. Small animal imaging in mouse ears clearly revealed the network of blood vessels and the corresponding blood perfusion.