Specific absorption rate in neonates undergoing magnetic resonance procedures at 1.5 T and 3 T.

MRI is finding increased clinical use in neonatal populations; the extent to which electromagnetic models used for quantification of specific absorption rate (SAR) by commercial MRI scanners accurately reflect this alternative scenario is unclear. This study investigates how SAR predictions relating to adults can be related to neonates under differing conditions when imaged using 1.5 T and 3 T MRI scanners. Electromagnetic simulations were produced in neonatal subjects of different sizes and positions within a generic MRI body transmit device operating at both 64 MHz and 128 MHz, corresponding to 1.5 T and 3 T MRI scanners, respectively. An adult model was also simulated, as was a spherical salt‐water phantom, which was also used in a calorimetry experiment. The SAR in neonatal subjects was found to be less than that experienced in an adult in all scenarios; however, the overestimation factor was variable. For example a 3 T body scan resulting in local 10 g SAR of 10.1 W kg(−1) in an adult would deposit 2.6 W kg(−1) in a neonate: an approximately fourfold difference. The SAR experienced by neonatal subjects undergoing MRI is lower than that in adults in equivalent situations. If the safety of such procedures is assessed using adult‐appropriate models then the result is a conservative estimate. © 2015 The Authors. NMR in Biomedicine published by John Wiley & Sons, Ltd

Brain development in fetal growth restriction: A volumetric approach using fetal MRI

Fetal growth restriction is the failure of a fetus to achieve its full growth potential, resulting in a neonate that is small for its gestational age. The aetiology of fetal growth restriction is varied and fetal growth restriction secondary to placental insufficiency is attributed to a failure of trophoblast invasion leading to under perfusion of the uteroplacental bed. In response to the adverse conditions in-utero, fetuses tend to compensate by increasing blood flow to the essential organs such as the brain, heart, and adrenals, at the expense of other organs (cerebral redistribution). As a consequence, growth tends to be asymmetric, with maintenance of the head growth velocity while the other growth parameters tail off; an effect which is also known as the ‘brain sparing effect’. Despite this apparent brain sparing effect, children who were growth restricted in utero are at increased risk of developmental delay and behavioural problems. 30 growth restricted and 48 normally grown fetuses were recruited into this study and were imaged using both conventional ultrasound with Doppler assessment, as well as fetal MRI with ssFSE sequences through the feto-placental unit and fetal brain. A dynamic approach was taken when imaging the fetal brain to compensate for the presence of fetal motion. MR imaging of the feto-placental unit detected significant differences in placental appearance, significantly smaller volumes of intra-abdominal and intra-thoracic organs, and significantly smaller regional brain growth among growth restricted fetuses. MR studies of the placenta in fetal growth restriction demonstrated a placental phenotype in growth restricted pregnancies that is characterised by smaller placental volumes, a significant increase in the placental volume affected by apparent pathology on MRI and a thickened, globular placenta. Although placental volume increased with gestation in both groups, the placental volume remained significantly smaller in the growth restricted fetuses (p = 0.003). There was also a significant correlation between the percentage of placental volume affected by abnormal heterogeneity and the severity of fetal growth restriction (r = 0.82, p < 0.001), and an increase in the maximal placental thickness to placental volume ratio above the 95th centile for gestational age was associated with fetal and early neonatal mortality (relative risk = 7, 95%CI = 2.96 – 16.55, p < 0.001) (figure 3.6) MR studies of fetal intra-thoracic and intra-abdominal volumes showed that although the volume of the intra-thoracic and intra-abdonimal organs (heart, lungs, thymus, liver and kidney) increased as gestation increased in both groups, the volumes of all three structures remained smaller in growth restricted fetuses (p < 0.01) (Figures 4.7 - 4.9) compared with normally grown fetuses. MR studies of the fetal brain demonstrated smaller intracranial volume, total brain volume and cerebellar volume in growth restricted fetuses. In addition, growth restricted fetuses with early onset fetal growth restriction demonstrated smaller vermis height and a corresponding increase in the tegmento-vermian angle. Growth restricted fetuses also demonstrated a disproportionate decrease in extra- and intra-cerebral fluid. This thesis showed evidence of changes in regional and global organ growth in growth restricted fetuses using high resolution fetal MRI. It is hoped that future imaging studies could offer useful insights into the origins and clinical significance of these findings and its consequences for later neurodevelopment

Towards Individualized Transcranial Electric Stimulation Therapy through Computer Simulation

Transkranielle Elektrostimulation (tES) beschreibt eine Gruppe von Hirnstimulationstechniken, die einen schwachen elektrischen Strom über zwei nicht-invasiv am Kopf angebrachten Elektroden applizieren. Handelt es sich dabei um einen Gleichstrom, spricht man von transkranieller Gleichstromstimulation, auch tDCS abgekürzt. Die allgemeine Zielstellung aller Hirnstimulationstechniken ist Hirnfunktion durch ein Verstärken oder Dämpfen von Hirnaktivität zu beeinflussen. Unter den Stimulationstechniken wird die transkranielle Gleichstromstimulation als ein adjuvantes Werkzeug zur Unterstützung der mikroskopischen Reorganisation des Gehirnes in Folge von Lernprozessen und besonders der Rehabilitationstherapie nach einem Schlaganfall untersucht. Aktuelle Herausforderungen dieser Forschung sind eine hohe Variabilität im erreichten Stimulationseffekt zwischen den Probanden sowie ein unvollständiges Verständnis des Zusammenspiels der der Stimulation zugrundeliegenden Mechanismen. Als Schlüsselkomponente für das Verständnis der Stimulationsmechanismen wird das zwischen den Elektroden im Kopf des Probanden aufgebaute elektrische Feld erachtet. Einem grundlegenden Konzept folgend wird angenommen, dass Hirnareale, die einer größeren elektrischen Feldstärke ausgesetzt sind, ebenso einen höheren Stimulationseffekt erfahren. Damit kommt der Positionierung der Elektroden eine entscheidende Rolle für die Stimulation zu. Allerdings verteilt sich das elektrische Feld wegen des heterogenen elektrischen Leitfähigkeitsprofil des menschlichen Kopfes nicht uniform im Gehirn der Probanden. Außerdem ist das Verteilungsmuster auf Grund anatomischer Unterschiede zwischen den Probanden verschieden. Die triviale Abschätzung der Ausbreitung des elektrischen Feldes anhand der bloßen Position der Stimulationselektroden ist daher nicht ausreichend genau für eine zielgerichtete Stimulation. Computerbasierte, biophysikalische Simulationen der transkraniellen Elektrostimulation ermöglichen die individuelle Approximation des Verteilungsmusters des elektrischen Feldes in Probanden basierend auf deren medizinischen Bildgebungsdaten. Sie werden daher zunehmend verwendet, um tDCS-Anwendungen zu planen und verifizieren, und stellen ein wesentliches Hilfswerkzeug auf dem Weg zu individualisierter Schlaganfall-Rehabilitationstherapie dar. Softwaresysteme, die den dahinterstehenden individualisierten Verarbeitungsprozess erleichtern und für ein breites Feld an Forschern zugänglich machen, wurden in den vergangenen Jahren für den Anwendungsfall in gesunden Erwachsenen entwickelt. Jedoch bleibt die Simulation von Patienten mit krankhaftem Hirngewebe und strukturzerstörenden Läsionen eine nicht-triviale Aufgabe. Daher befasst sich das hier vorgestellte Projekt mit dem Aufbau und der praktischen Anwendung eines Arbeitsablaufes zur Simulation transkranieller Elektrostimulation. Dabei stand die Anforderung im Vordergrund medizinische Bildgebungsdaten insbesondere neurologischer Patienten mit krankhaft verändertem Hirngewebe verarbeiten zu können. Der grundlegende Arbeitsablauf zur Simulation wurde zunächst für gesunde Erwachsene entworfen und validiert. Dies umfasste die Zusammenstellung medizinischer Bildverarbeitungsalgorithmen zu einer umfangreichen Verarbeitungskette, um elektrisch relevante Strukturen in den Magnetresonanztomographiebildern des Kopfes und des Oberkörpers der Probanden zu identifizieren und zu extrahieren. Die identifizierten Strukturen mussten in Computermodelle überführt werden und das zugrundeliegende, physikalische Problem der elektrischen Volumenleitung in biologischen Geweben mit Hilfe numerischer Simulation gelöst werden. Im Verlauf des normalen Alterns ist das Gehirn strukturellen Veränderungen unterworfen, unter denen ein Verlust des Hirnvolumens sowie die Ausbildung mikroskopischer Veränderungen seiner Nervenfaserstruktur die Bedeutendsten sind. In einem zweiten Schritt wurde der Arbeitsablauf daher erweitert, um diese Phänomene des normalen Alterns zu berücksichtigen. Die vordergründige Herausforderung in diesem Teilprojekt war die biophysikalische Modellierung der veränderten Hirnmikrostruktur, da die resultierenden Veränderungen im Leitfähigkeitsprofil des Gehirns bisher noch nicht in der Literatur quantifiziert wurden. Die Erweiterung des Simulationsablauf zeichnete sich vorrangig dadurch aus, dass mit unsicheren elektrischen Leitfähigkeitswerten gearbeitet werden konnte. Damit war es möglich den Einfluss der ungenau bestimmbaren elektrischen Leitfähigkeit der verschiedenen biologischen Strukturen des menschlichen Kopfes auf das elektrische Feld zu ermitteln. In einer Simulationsstudie, in der Bilddaten von 88 Probanden einflossen, wurde die Auswirkung der veränderten Hirnfaserstruktur auf das elektrische Feld dann systematisch untersucht. Es wurde festgestellt, dass sich diese Gewebsveränderungen hochgradig lokal und im Allgemeinen gering auswirken. Schließlich wurden in einem dritten Schritt Simulationen für Schlaganfallpatienten durchgeführt. Ihre großen, strukturzerstörenden Läsionen wurden dabei mit einem höheren Detailgrad als in bisherigen Arbeiten modelliert und physikalisch abermals mit unsicheren Leitfähigkeiten gearbeitet, was zu unsicheren elektrischen Feldabschätzungen führte. Es wurden individuell berechnete elektrische Felddaten mit der Hirnaktivierung von 18 Patienten in Verbindung gesetzt, unter Berücksichtigung der inhärenten Unsicherheit in der Bestimmung der elektrischen Felder. Das Ziel war zu ergründen, ob die Hirnstimulation einen positiven Einfluss auf die Hirnaktivität der Patienten im Kontext von Rehabilitationstherapie ausüben und so die Neuorganisierung des Gehirns nach einem Schlaganfall unterstützen kann. Während ein schwacher Zusammenhang hergestellt werden konnte, sind weitere Untersuchungen nötig, um diese Frage abschließend zu klären.:Kurzfassung Abstract Contents 1 Overview 2 Anatomical structures in magnetic resonance images 2 Anatomical structures in magnetic resonance images 2.1 Neuroanatomy 2.2 Magnetic resonance imaging 2.3 Segmentation of MR images 2.4 Image morphology 2.5 Summary 3 Magnetic resonance image processing pipeline 3.1 Introduction to human body modeling 3.2 Description of the processing pipeline 3.3 Intermediate and final outcomes in two subjects 3.4 Discussion, limitations & future work 3.5 Conclusion 4 Numerical simulation of transcranial electric stimulation 4.1 Electrostatic foundations 4.2 Discretization of electrostatic quantities 4.3 The numeric solution process 4.4 Spatial discretization by volume meshing 4.5 Summary 5 Simulation workflow 5.1 Overview of tES simulation pipelines 5.2 My implementation of a tES simulation workflow 5.3 Verification & application examples 5.4 Discussion & Conclusion 6 Transcranial direct current stimulation in the aging brain 6.1 Handling age-related brain changes in tES simulations 6.2 Procedure of the simulation study 6.3 Results of the uncertainty analysis 6.4 Findings, limitations and discussion 7 Transcranial direct current stimulation in stroke patients 7.1 Bridging the gap between simulated electric fields and brain activation in stroke patients 7.2 Methodology for relating simulated electric fields to functional MRI data 7.3 Evaluation of the simulation study and correlation analysis 7.4 Discussion & Conclusion 8 Outlooks for simulations of transcranial electric stimulation List of Figures List of Tables Glossary of Neuroscience Terms Glossary of Technical Terms BibliographyTranscranial electric current stimulation (tES) denotes a group of brain stimulation techniques that apply a weak electric current over two or more non-invasively, head-mounted electrodes. When employing a direct-current, this method is denoted transcranial direct current stimulation (tDCS). The general aim of all tES techniques is the modulation of brain function by an up- or downregulation of brain activity. Among these, transcranial direct current stimulation is investigated as an adjuvant tool to promote processes of the microscopic reorganization of the brain as a consequence of learning and, more specifically, rehabilitation therapy after a stroke. Current challenges of this research are a high variability in the achieved stimulation effects across subjects and an incomplete understanding of the interplay between its underlying mechanisms. A key component to understanding the stimulation mechanism is considered the electric field, which is exerted by the electrodes and distributes in the subjects' heads. A principle concept assumes that brain areas exposed to a higher electric field strength likewise experience a higher stimulation. This attributes the positioning of the electrodes a decisive role for the stimulation. However, the electric field distributes non-uniformly across subjects' brains due to the heterogeneous electrical conductivity profile of the human head. Moreover, the distribution pattern is variable between subjects due to their individual anatomy. A trivial estimation of the distribution of the electric field solely based on the position of the stimulating electrodes is, therefore, not precise enough for a well-targeted stimulation. Computer-based biophysical simulations of transcranial electric stimulation enable the individual approximation of the distribution pattern of the electric field in subjects based on their medical imaging data. They are, thus, increasingly employed for the planning and verification of tDCS applications and constitute an essential tool on the way to individualized stroke rehabilitation therapy. Software pipelines facilitating the underlying individualized processing for a wide range of researchers have been developed for use in healthy adults over the past years, but, to date, the simulation of patients with abnormal brain tissue and structure disrupting lesions remains a non-trivial task. Therefore, the presented project was dedicated to establishing and practically applying a tES simulation workflow. The processing of medical imaging data of neurological patients with abnormal brain tissue was a central requirement in this process. The basic simulation workflow was first designed and validated for the simulation of healthy adults. This comprised compiling medical image processing algorithms into a comprehensive workflow to identify and extract electrically relevant physiological structures of the human head and upper torso from magnetic resonance images. The identified structures had to be converted to computational models. The underlying physical problem of electric volume conduction in biological tissue was solved by means of numeric simulation. Over the course of normal aging, the brain is subjected to structural alterations, among which a loss of brain volume and the development of microscopic alterations of its fiber structure are the most relevant. In a second step, the workflow was, thus, extended to incorporate these phenomena of normal aging. The main challenge in this subproject was the biophysical modeling of the altered brain microstructure as the resulting alterations to the conductivity profile of the brain were so far not quantified in the literature. Therefore, the augmentation of the workflow most notably included the modeling of uncertain electrical properties. With this, the influence of the uncertain electrical conductivity of the biological structures of the human head on the electric field could be assessed. In a simulation study, including imaging data of 88 subjects, the influence of the altered brain fiber structure on the electric field was then systematically investigated. These tissue alterations were found to exhibit a highly localized and generally low impact. Finally, in a third step, tDCS simulations of stroke patients were conducted. Their large, structure-disrupting lesions were modeled in a more detailed manner than in previous stroke simulation studies, and they were physically, again, modeled by uncertain electrical conductivity resulting in uncertain electric field estimates. Individually simulated electric fields were related to the brain activation of 18 patients, considering the inherently uncertain electric field estimations. The goal was to clarify whether the stimulation exerts a positive influence on brain function in the context of rehabilitation therapy supporting brain reorganization following a stroke. While a weak correlation could be established, further investigation will be necessary to answer that research question.:Kurzfassung Abstract Contents 1 Overview 2 Anatomical structures in magnetic resonance images 2 Anatomical structures in magnetic resonance images 2.1 Neuroanatomy 2.2 Magnetic resonance imaging 2.3 Segmentation of MR images 2.4 Image morphology 2.5 Summary 3 Magnetic resonance image processing pipeline 3.1 Introduction to human body modeling 3.2 Description of the processing pipeline 3.3 Intermediate and final outcomes in two subjects 3.4 Discussion, limitations & future work 3.5 Conclusion 4 Numerical simulation of transcranial electric stimulation 4.1 Electrostatic foundations 4.2 Discretization of electrostatic quantities 4.3 The numeric solution process 4.4 Spatial discretization by volume meshing 4.5 Summary 5 Simulation workflow 5.1 Overview of tES simulation pipelines 5.2 My implementation of a tES simulation workflow 5.3 Verification & application examples 5.4 Discussion & Conclusion 6 Transcranial direct current stimulation in the aging brain 6.1 Handling age-related brain changes in tES simulations 6.2 Procedure of the simulation study 6.3 Results of the uncertainty analysis 6.4 Findings, limitations and discussion 7 Transcranial direct current stimulation in stroke patients 7.1 Bridging the gap between simulated electric fields and brain activation in stroke patients 7.2 Methodology for relating simulated electric fields to functional MRI data 7.3 Evaluation of the simulation study and correlation analysis 7.4 Discussion & Conclusion 8 Outlooks for simulations of transcranial electric stimulation List of Figures List of Tables Glossary of Neuroscience Terms Glossary of Technical Terms Bibliograph

Understanding quantitative DCE-MRI of the breast : towards meaningful clinical application

In most industrialized countries breast cancer will affect one out of eight women during her lifetime. In the USA, after continuously increasing for more than two decades, incidence rates are slowly decreasing since 2001. Since 1990, death rates from breast cancer have steadily decreased in women, which is attributed to both earlier detection and improved treatment. Still, it is second only to lung cancer as a cause of cancer death in women. In this work we set out to improve early detection of breast cancer via quantitative analysis of magnetic resonance images (MRI). Screening and diagnosis of breast cancer are generally performed using X-ray mammography, possibly in conjunction with ultrasonography. However, MRI is becoming an important modality for screening of women at high-risk due to for instance hereditary gene mutations, as a problem-solving tool in case of indecisive mammographic and / or ultrasonic imaging, and for anti-cancer therapy assessment. In this work, we focused on MR imaging of the breast. More specifically, the dynamic contrast-enhanced (DCE) part of the protocol was highlighted, as well as radiological assessment of DCE-MRI data. The T_1-weighted (T_1: longitudinal relaxation time, a tissue property) signal-versus-time curve that can be extracted from the DCE-MRI series that is acquired at the time of and after injection of a T_1-shortening (shorter T_1 results in higher signal) contrast agent, is usually visually assessed by the radiologist. For example, a fast initial rise to the peak (1-2 minutes post injection) followed by loss of signal within a time frame of about 5-6 minutes is a sign for malignancy, whereas a curve showing persistent (slow) uptake within the same time frame is a sign for benignity. This difference in contrast agent uptake pattern is related to physiological changes in tumorous tissue that for instance result in a stronger uptake of the contrast agent. However, this descriptive way of curve type classification is based on clinical statistics, not on knowledge about tumor physiology. We investigated pharmacokinetic modeling as a quantitative image analysis tool. Pharmacokinetics describes what happens to a substance (e.g. drug or contrast agent) after it has been administered to a living organism. This includes the mechanisms of absorption and distribution. The terms in which these mechanisms are described are physiological and can therefore provide parameters describing the functioning of the tissue. This physiological aspect makes it an attractive approach to investigate (aberrant) tissue functioning. In addition, this type of analysis excludes confounding factors due to inter- and intra-patient differences in the systemic blood circulation, as well as differences in the injection protocol. In this work, we discussed the physiological basis and details of different types of pharmacokinetic models, with the focus on compartmental models. Practical implications such as obtaining an arterial input function and model parameter estimation were taken into account as well. A simulation study of the data-imposed limitations – in terms of temporal resolution and noise properties – on the complexity of pharmacokinetic models led to the insight that only one of the tested models, the basic Tofts model, is applicable to DCE-MRI data of the breast. For the basic Tofts model we further investigated the aspect of temporal resolution, because a typical diagnostic DCE-MRI scan of the breast is acquired at a rate of about 1 image volume every minute; whereas pharmacokinetic modeling usually requires a sampling time of less than 10 s. For this experiment we developed a new downsampling method using high-temporal-resolution raw k-space data to simulate what uptake curves would have looked like if they were acquired at lower temporal resolutions. We made use of preclinical animal data. With this data we demonstrated that the limit of 10 s can be stretched to about 1 min if the arterial input function (AIF, the input to the pharmacokinetic model) is inversely derived from a healthy reference tissue, instead of measured in an artery or taken from the literature. An important precondition for the application of pharmacokinetic modeling is knowledge of the relationship between the acquired DCE-MRI signal and the actual concentration of the contrast agent in the tissue. This relationship is not trivial because with MRI we measure the indirect effect of the contrast agent on water protons. To establish this relationship via calculation of T_1 (t), we investigated both a theoretical and an empirical approach, making use of an in-house (University of Chicago) developed reference object that is scanned concurrently with the patient. The use of the calibration object can shorten the scan duration (an empirical approach requires less additional scans than an approach using a model of the acquisition technique), and can demonstrate if theoretical approaches are valid. Moreover we produced concentration images and estimated tissue proton density, also making use of the calibration object. Finally, via pharmacokinetic modeling and other MRI-derived measures we partly revealed the actions of a novel therapeutic in a preclinical study. In particular, the anti-tumor activity of a single dose of liposomal prednisolone phosphate was investigated, which is an anti-inflammatory drug that has demonstrated tumor growth inhibition. The work presented in this thesis contributes to a meaningful clinical application and interpretation of quantitative DCE-MRI of the breast

Imaging Breast Cancer Progression and Lymph Node Metastases in Murine Models Using MRI and Magnetic Nanoparticles

Most breast cancer related deaths are caused by the spread or metastasis of the primary tumor to distant sites in the body. The lymph nodes are one of the first places where metastases can be detected and are frequently examined for macroscopic metastases to help determine course of treatment for patients. However, little is known about the significance of microscopic metastases and disseminated individual cancer cells within the nodes. The goal of this work was to use MRI to monitor the development of primary tumors and lymphatic metastases in models of breast cancer. In this thesis, we examined the MRI appearance of lymph nodes in several different strains of immune compromised mice (nude, CB -17 SCID, NOD/SCID IL2Rnull) and compared the appearance to immune competent C57/Bl6 strain. We found that immune deficiencies influenced the MRI appearance of nodes and that the nude strain had highly variable lymph node appearance and volume. We also compared orthotopic transplantation models of breast cancer that used both the nude and CB-17 SCID strains using MRI. We found that MRI was most reliable for detecting metastases in the lymph nodes of SCID mice and that the variability of the appearance of nodes in nude mice can lead to their misclassification. We then used the SCID orthotopic breast cancer model to monitor the appearance and retention of iron oxide nanoparticle labeled cancer cells in both the primary tumor and lymph nodes. We found that iron-labeled cells are still detected within the primary tumor after 28 days post-implantation and that these labeled cells almost exclusively migrated to the lymph nodes. The development of improved methods for monitoring the development of the primary tumor and metastases and the roles that different cells populations have in these processes will allow for more accurate knowledge of how cancer cell heterogeneity impacts disease progression. These tools will allow for more effective monitoring of the treatment effect of new drugs on primary tumors and metastatic dissemination

Quantification of perfusion abnormalities using dynamic contrast-enhanced magnetic resonance imaging in muco-obstructive lung diseases

Pulmonary perfusion is regionally impaired in muco-obstructive lung diseases such as cystic fibrosis (CF) and chronic obstructive pulmonary disease (COPD) due to the destruction of the alveolar-capillary bed and hypoxic pulmonary vasoconstriction in response to alveolar hypoxia. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is an established technique for assessing regional perfusion abnormalities by exploiting contrast enhancement in the lung parenchyma during the first pass of an intravenously injected contrast agent bolus. Typically, perfusion abnormalities are assessed in clinical studies by visual scoring or by quantifying pulmonary blood flow (PBF) and pulmonary blood volume (PBV). Automated quantification can help to address inter-reader variability issues with human reader, facilitate detailed perfusion analyses and is time efficient. However, currently used absolute quantification of PBF and PBV is highly variable. For this reason, an algorithm was developed to quantify the extent of pulmonary perfusion in percent (QDP) using unsupervised clustering algorithms, which leads to an intrinsic normalisation and can reduce variability compared to absolute perfusion quantification. The aims of this work were to develop a robust algorithm for quantifying QDP, to investigate the midterm reproducibility of QDP, and to validate QDP using MRI perfusion scoring, quantitative computed tomography (CT) parameters, and pulmonary function testing (PFT) parameters. Furthermore, the performance of QDP was compared to the performance of PBF and PBV. The development of QDP and its technical and clinical validation were performed using data from two studies, which utilise DCE-MRI. First, the algorithm was developed using data of 83 COPD subjects from the ‘COSYCONET’ COPD cohort by comparing different unsupervised clustering approaches including Otsu´s method, k-means clustering, and 80th percentile threshold. Second, the reproducibility of QDP was investigated using data from a study of 15 CF and 20 COPD patients who underwent DCE-MRI at baseline and one month later (reproducibility study). According to the indicator dilution theory, impulse response function maps were calculated from DCE-MRI data, which formed the basis for the quantification of QDP, PBF and PBV. Overall, QDP based on Otsu´s method showed the highest agreement with the MRI perfusion score, quantitative CT parameters and PFT parameters in the COSYCONET study and was therefore selected for further evaluations. QDP correlated moderately with the MRI perfusion score in CF (r=0.46, p<0.05) and moderately to strongly in COPD (r=0.66 and r=0.72, p<0.001) in both studies. PBF and PBV correlated poorly with the MRI perfusion score in CF (r=-0.29, p=0.132 and r=-0.35, p=0.067, respectively) and moderately in COPD (r=-0.49 to -0.57, p<0.001). QDP correlated strongly with the CT parameter for emphysema (r=0.74, p<0.001) and weakly with the CT parameter for functional small airway disease (r=0.35, p<0.001) in COPD. The extent of perfusion defects from DCE-MRI corresponded to extent of abnormal lung (emphysema+functional small airway disease) from CT, with a mean difference of 6.03±16.94. QDP correlated moderately with PFT parameters in both studies and patient groups, with one exception in the reproducibility study where no correlation was observed in the COPD group. The use of unsupervised clustering approaches increased the reproducibility (±1.96SD related to the median) of QDP (CF: ±38%, COPD: ±37%) compared to PBF(CF: ±89%, COPD: ±55%) and PBV(CF: ±55%, COPD: ±51%) and reduced outliers. These results demonstrate that the quantification of pulmonary perfusion using unsupervised clustering approaches in combination with the mathematical models of the indicator dilution theory improves the reproducibility and the correlations with visual MRI perfusion scoring, quantitative CT parameters and PFT parameters. QDP based on Otsu´s method showed high agreement with the MRI perfusion score, suggesting that in future clinical studies pulmonary perfusion can be assessed objectively by computer algorithms replacing the time-consuming visual scoring. Concordance between the extent of QDP from MRI and the extent of abnormal lung from CT indicates that pulmonary perfusion abnormalities themselves may contribute to, or at least precede, the development of irreversible emphysema. The findings of both studies show that QDP is clinically meaningful in muco-obstructive lung diseases as it is significantly associated with the MRI perfusion score, quantitative CT parameters, and PFT parameters

Influence of blue light on skin models consisting of human fibroblasts and keratinocytes

In this project a therapy and a device to treat chronic wounds in diabetics were developed. The therapy is based on photobiomodulation using blue light. The effects of different doses of blue light were first tested in vitro on skin models of keratinocytes and fibroblasts in different models. Here, an enhanced metabolic activity and proliferation could be identified with 10.35 J/cm². With higher fluences (41.40 and 82.80 J/cm²) the opposite effect could be observed: decreased metabolic activity and proliferation were revealed. Further, the experiments were transferred to in vivo testing on rats. Also here, single blue light irradiations with different doses revealed different effects in dependence of the dose, as is typical for photobiomodulation, described as the Arndt-Schulz-curve. Effects could be seen regarding the CRP, showing a reduction of inflammation with lower fluences. Gene expression revealed changes in the inflammatory response, where certain anti-inflammatory and pro-inflammatory pathways were deregulated. The inflammatory response seemed to be enhanced, driving forward the overall inflammation response. This could be confirmed by long-term irradiation of diabetic rats, where wounds treated with light therapy healed faster than non-treated wounds. Inflammatory pathways were deregulated, proliferation and differentiation was up-regulated, formation of fibers and neurons and remodeling of granulation tissue was enhanced. To be able to apply the findings on patients, a device was developed, which emits blue light. This device was tested in vitro first, where single irradiations showed the same effects as with the lamp used for preliminary experiments. To prevent heat development the device was programmed to use pulses instead of continuous irradiation. Thereby the heat development could be reduced from a maximum temperature of 52°C to 37°C with pulses of 30 s. This scheme was tested on porcine skin, where no negative effects could be seen. The light schedule was tested again in vitro, showing a shift to higher fluences to achieve the desired effects. In vivo testing unfortunately did not lead to positive results, probably due to involvement of a wound dressing, changing the homogeneity of the irradiation area. Nevertheless, the project led to interesting outcomes, which are ready to be applied in a clinical trial and could improve healing of chronic wounds

Investigation of pathophysiological mechanisms in clinically isolated syndrome using advanced imaging techniques

This thesis concerns an observational study of patients recruited after their first episode of neurological symptoms suggestive of demyelination in the central nervous system and diagnosed either with clinically isolated syndrome or relapsing-remitting multiple sclerosis. In multiple sclerosis, brain tissues can exhibit extensive neuroaxonal microstructural and metabolic abnormalities, but little is known about their presence and significance at the time of the first demyelinating episode. I used a novel multi-parametric quantitative MRI approach, combining neurite orientation dispersion and density imaging (NODDI), which gives information about tissue microstructure, and 23Na MRI, which estimates total sodium concentration, a marker of metabolic dysfunction, in the brains of clinically isolated syndrome patients. I found microstructural and sodium homeostasis alterations in cortical areas of patients that showed clinical relevance. Within the diffuse axonal dispersion found in the normal-appearing white matter, the corpus callosum shared with lesions, signs of axonal damage and metabolic dysfunction, thus emerging as a possible target for early neuroprotective interventions. Structural cortical networks (SCNs) represent patterns of coordinated morphological modifications in cortical areas and they have shown pathophysiological changes in many brain disorders, including multiple sclerosis. I investigated alterations of SCNs at the individual level in this early cohort. Patients showed altered small-world topology, an efficient network organization combining dense local clustering with relatively few long-distance connections. These disruptions were worse for patients with higher lesion load and worse cognitive processing speed indicating that pathophysiological changes in the cortical morphology can influence clinical outcomes. Finally, I hypothesised that the patients in the cohort presenting with optic neuritis may have disturbances in neuropsychological functions related to visual processes. I found that cognitive visuospatial processing is affected after unilateral optic neuritis and improves over time with visual recovery, independently of the structural damage in the visual and central nervous system