114 research outputs found

    Fluorescence molecular tomography: Principles and potential for pharmaceutical research

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    Fluorescence microscopic imaging is widely used in biomedical research to study molecular and cellular processes in cell culture or tissue samples. This is motivated by the high inherent sensitivity of fluorescence techniques, the spatial resolution that compares favorably with cellular dimensions, the stability of the fluorescent labels used and the sophisticated labeling strategies that have been developed for selectively labeling target molecules. More recently, two and three-dimensional optical imaging methods have also been applied to monitor biological processes in intact biological organisms such as animals or even humans. These whole body optical imaging approaches have to cope with the fact that biological tissue is a highly scattering and absorbing medium. As a consequence, light propagation in tissue is well described by a diffusion approximation and accurate reconstruction of spatial information is demanding. While in vivo optical imaging is a highly sensitive method, the signal is strongly surface weighted, i.e., the signal detected from the same light source will become weaker the deeper it is embedded in tissue, and strongly depends on the optical properties of the surrounding tissue. Derivation of quantitative information, therefore, requires tomographic techniques such as fluorescence molecular tomography (FMT), which maps the three-dimensional distribution of a fluorescent probe or protein concentration. The combination of FMT with a structural imaging method such as X-ray computed tomography (CT) or Magnetic Resonance Imaging (MRI) will allow mapping molecular information on a high definition anatomical reference and enable the use of prior information on tissue’s optical properties to enhance both resolution and sensitivity. Today many of the fluorescent assays originally developed for studies in cellular systems have been successfully translated for experimental studies in animals. The opportunity of monitoring molecular processes non-invasively in the intact organism is highly attractive from a diagnostic point of view but even more so for the drug developer, who can use the techniques for proof-of-mechanism and proof-of-efficacy studies. This review shall elucidate the current status and potential of fluorescence tomography including recent advances in multimodality imaging approaches for preclinical and clinical drug development

    Multi-Modality Diffuse Fluorescence Imaging Applied to Preclinical Imaging in Mice

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    RÉSUMÉ Cette thĂšse vise Ă  explorer l'information anatomique et fonctionnelle en dĂ©veloppant de nouveaux systĂšmes d'imagerie de fluorescence macroscopiques Ă  base de multi-modalitĂ©. L‘ajout de l‘imagerie anatomique Ă  des modalitĂ©s fonctionnelles telles que la fluorescence permet une meilleure visualisation et la rĂ©cupĂ©ration quantitative des images de fluorescence, ce qui en retour permet d'amĂ©liorer le suivi et l'Ă©valuation des paramĂštres biologiques dans les tissus. Sur la base de cette motivation, la fluorescence a Ă©tĂ© combinĂ©e avec l‘imagerie ultrasonore (US) d'abord et ensuite l'imagerie par rĂ©sonance magnĂ©tique (IRM). Dans les deux cas, les performances du systĂšme ont Ă©tĂ© caractĂ©risĂ©es et la reconstruction a Ă©tĂ© Ă©valuĂ©e par des simulations et des expĂ©rimentations sur des fantĂŽmes. Finalement, ils ont Ă©tĂ© utilisĂ©s pour des expĂ©riences d'imagerie molĂ©culaire in vivo dans des modĂšles de cancer et d‘athĂ©rosclĂ©rose chez la souris. Les rĂ©sultats ont Ă©tĂ© prĂ©sentĂ©s dans trois articles, qui sont inclus dans cette thĂšse et dĂ©crits briĂšvement ci-dessous. Un premier article prĂ©sente un systĂšme d'imagerie bimodalitĂ© combinant fluorescence Ă  onde continue avec l‘imagerie Ă  trois dimensions (3D) US. A l‘aide de stages X-Y motorisĂ©s, le systĂšme d'imagerie a Ă©tĂ© en mesure de recueillir lâ€˜Ă©mission fluorescente et les Ă©chos acoustiques dĂ©limitant la surface 3D et la position des inclusions fluorescentes dans l'Ă©chantillon. Une validation sur fantĂŽmes, a montrĂ© que l'utilisation des priors anatomiques provenant des US amĂ©liorait la qualitĂ© de la reconstruction fluorescente. En outre, un Ă©tude pilote in-vivo en utilisant une souris Apo-E a Ă©valuĂ© la faisabilitĂ© de cette approche d'imagerie double modalitĂ© pour de futures Ă©tudes prĂ©-cliniques. Dans un deuxiĂšme effort, et sur la base du premier travail, nous avons amĂ©liorĂ© le systĂšme d'imagerie par fluorescence-US au niveau des algorithmes, de la prĂ©cision----------ABSTRACT This thesis aims to explore the anatomical and functional information by developing new macroscopic multi-modality fluorescence imaging schemes. Adding anatomical imaging to functional modalities such as fluorescence enables better visualization and recovery of fluorescence images, in turn, improving the monitoring and assessment of biological parameters in tissue. Based on this motivation, fluorescence was combined with ultrasound (US) imaging first and then magnetic resonance imaging (MRI). In both cases, the systems characterization and reconstruction performance were evaluated by simulations and phantom experiments. Eventually, they were applied to in vivo molecular imaging in models of cancer and atherosclerosis in mice. Results were presented in three peer-reviewed journals, which are included in this thesis and shortly described below. A first article presented a dual-modality imaging system combining continuous-wave transmission fluorescence imaging with three dimensional (3D) US imaging. Using motorized X-Y stages, the fluorescence-US imaging system was able to collect boundary fluorescent emission, and acoustic pulse-echoes delineating the 3D surface and position of fluorescent inclusions within the sample. A validation in phantoms showed that using the US anatomical priors, the fluorescent reconstruction quality was significantly improved. Furthermore, a pilot in-vivo study using an Apo-E mouse evaluated the feasibility of this dual-modality imaging approach for future animal studies. In a second endeavor, and based on the first work, we improved the fluorescence-US imaging system in terms of sampling precision and reconstruction algorithms. Specifically, now combining US imaging and profilometry, both the fluorescent target and 3D surface of sample could be obtained in order to achieve improved fluorescence reconstruction. Furthermore,

    Advanced maximum entropy approaches for medical and microscopy imaging

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    The maximum entropy framework is a cornerstone of statistical inference, which is employed at a growing rate for constructing models capable of describing and predicting biological systems, particularly complex ones, from empirical datasets.‎ In these high-yield applications, determining exact probability distribution functions with only minimal information about data characteristics and without utilizing human subjectivity is of particular interest. In this thesis, an automated procedure of this kind for univariate and bivariate data is employed to reach this objective through combining the maximum entropy method with an appropriate optimization method. The only necessary characteristics of random variables are their continuousness and ability to be approximated as independent and identically distributed. In this work, we try to concisely present two numerical probabilistic algorithms and apply them to estimate the univariate and bivariate models of the available data. In the first case, a combination of the maximum entropy method, Newton's method, and the Bayesian maximum a posteriori approach leads to the estimation of the kinetic parameters with arterial input functions (AIFs) in cases without any measurement of the AIF. ‎The results shows that the AIF can reliably be determined from the data of dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) by maximum entropy method. Then, kinetic parameters can be obtained. By using the developed method, a good data fitting and thus a more accurate prediction of the kinetic parameters are achieved, which, in turn, leads to a more reliable application of DCE-MRI. ‎ In the bivariate case, we consider colocalization as a quantitative analysis in fluorescence microscopy imaging. The method proposed in this case is obtained by combining the Maximum Entropy Method (MEM) and a Gaussian Copula, which we call the Maximum Entropy Copula (MEC). This novel method is capable of measuring the spatial and nonlinear correlation of signals to obtain the colocalization of markers in fluorescence microscopy images. Based on the results, MEC is able to specify co- and anti-colocalization even in high-background situations.‎ ‎The main point here is that determining the joint distribution via its marginals is an important inverse problem which has one possible unique solution in case of choosing an proper copula according to Sklar's theorem. This developed combination of Gaussian copula and the univariate maximum entropy marginal distribution enables the determination of a unique bivariate distribution. Therefore, a colocalization parameter can be obtained via Kendall’s t, which is commonly employed in the copula literature. In general, the importance of applying these algorithms to biological data is attributed to the higher accuracy, faster computing rate, and lower cost of solutions in comparison to those of others. The extensive application and success of these algorithms in various contexts depend on their conceptual plainness and mathematical validity. ‎ Afterward, a probability density is estimated via enhancing trial cumulative distribution functions iteratively, in which more appropriate estimations are quantified using a scoring function that recognizes irregular fluctuations. This criterion resists under and over fitting data as an alternative to employing the Bayesian criterion. Uncertainty induced by statistical fluctuations in random samples is reflected by multiple estimates for the probability density. In addition, as a useful diagnostic for visualizing the quality of the estimated probability densities, scaled quantile residual plots are introduced. Kullback--Leibler divergence is an appropriate measure to indicate the convergence of estimations for the probability density function (PDF) to the actual PDF as sample. The findings indicate the general applicability of this method to high-yield statistical inference.Die Methode der maximalen Entropie ist ein wichtiger Bestandteil der statistischen Inferenz, die in immer stĂ€rkerem Maße fĂŒr die Konstruktion von Modellen verwendet wird, die biologische Systeme, insbesondere komplexe Systeme, aus empirischen DatensĂ€tzen beschreiben und vorhersagen können. In diesen ertragreichen Anwendungen ist es von besonderem Interesse, exakte Verteilungsfunktionen mit minimaler Information ĂŒber die Eigenschaften der Daten und ohne Ausnutzung menschlicher SubjektivitĂ€t zu bestimmen. In dieser Arbeit wird durch eine Kombination der Maximum-Entropie-Methode mit geeigneten Optimierungsverfahren ein automatisiertes Verfahren verwendet, um dieses Ziel fĂŒr univariate und bivariate Daten zu erreichen. Notwendige Eigenschaften von Zufallsvariablen sind lediglich ihre Stetigkeit und ihre Approximierbarkeit als unabhĂ€ngige und identisch verteilte Variablen. In dieser Arbeit versuchen wir, zwei numerische probabilistische Algorithmen prĂ€zise zu prĂ€sentieren und sie zur SchĂ€tzung der univariaten und bivariaten Modelle der zur VerfĂŒgung stehenden Daten anzuwenden. ZunĂ€chst wird mit einer Kombination aus der Maximum-Entropie Methode, der Newton-Methode und dem Bayes'schen Maximum-A-Posteriori-Ansatz die SchĂ€tzung der kinetischen Parameter mit arteriellen Eingangsfunktionen (AIFs) in FĂ€llen ohne Messung der AIF ermöglicht. Die Ergebnisse zeigen, dass die AIF aus den Daten der dynamischen kontrastverstĂ€rkten Magnetresonanztomographie (DCE-MRT) mit der Maximum-Entropie-Methode zuverlĂ€ssig bestimmt werden kann. Anschließend können die kinetischen Parameter gewonnen werden. Durch die Anwendung der entwickelten Methode wird eine gute Datenanpassung und damit eine genauere Vorhersage der kinetischen Parameter erreicht, was wiederum zu einer zuverlĂ€ssigeren Anwendung der DCE-MRT fĂŒhrt. Im bivariaten Fall betrachten wir die Kolokalisierung zur quantitativen Analyse in der Fluoreszenzmikroskopie-Bildgebung. Die in diesem Fall vorgeschlagene Methode ergibt sich aus der Kombination der Maximum-Entropie-Methode (MEM) und einer Gaußschen Copula, die wir Maximum-Entropie-Copula (MEC) nennen. Mit dieser neuartigen Methode kann die rĂ€umliche und nichtlineare Korrelation von Signalen gemessen werden, um die Kolokalisierung von Markern in Bildern der Fluoreszenzmikroskopie zu erhalten. Das Ergebnis zeigt, dass MEC in der Lage ist, die Ko- und Antikolokalisation auch in Situationen mit hohem Grundrauschen zu bestimmen. Der wesentliche Punkt hierbei ist, dass die Bestimmung der gemeinsamen Verteilung ĂŒber ihre Marginale ein entscheidendes inverses Problem ist, das eine mögliche eindeutige Lösung im Falle der Wahl einer geeigneten Copula gemĂ€ĂŸ dem Satz von Sklar hat. Diese neu entwickelte Kombination aus Gaußscher Kopula und der univariaten Maximum Entropie Randverteilung ermöglicht die Bestimmung einer eindeutigen bivariaten Verteilung. Daher kann ein Kolokalisationsparameter ĂŒber Kendall's t ermittelt werden, der ĂŒblicherweise in der Copula-Literatur verwendet wird. Die Bedeutung der Anwendung dieser Algorithmen auf biologische Daten lĂ€sst sich im Allgemeinen mit hoher Genauigkeit, schnellerer Rechengesch windigkeit und geringeren Kosten im Vergleich zu anderen Lösungen begrĂŒnden. Die umfassende Anwendung und der Erfolg dieser Algorithmen in verschiedenen Kontexten hĂ€ngen von ihrer konzeptionellen Eindeutigkeit und mathematischen GĂŒltigkeit ab. Anschließend wird eine Wahrscheinlichkeitsdichte durch iterative Erweiterung von kumulativen Verteilungsfunktionen geschĂ€tzt, wobei die geeignetsten SchĂ€tzungen mit einer Scoring-Funktion quantifiziert werden, um unregelmĂ€ĂŸige Schwankungen zu erkennen. Dieses Kriterium verhindert eine Unter- oder Überanpassung der Daten als Alternative zur Verwendung des Bayes-Kriteriums. Die durch statistische Schwankungen in Stichproben induzierte Unsicherheit wird durch mehrfache SchĂ€tzungen fĂŒr die Wahrscheinlichkeitsdichte berĂŒcksichtigt. ZusĂ€tzlich werden als nĂŒtzliche Diagnostik zur Visualisierung der QualitĂ€t der geschĂ€tzten Wahrscheinlichkeitsdichten skalierte Quantil-Residuen-Diagramme eingefĂŒhrt. Die Kullback-Leibler-Divergenz ist ein geeignetes Maß, um die Konvergenz der SchĂ€tzungen fĂŒr die Wahrscheinlichkeitsdichtefunktion (PDF) zu der tatsĂ€chlichen PDF als Stichprobe anzuzeigen. Die Ergebnisse zeigen die generelle Anwendbarkeit dieser Methode fĂŒr statistische Inferenz mit hohem Ertrag.

    Radiolabeled PET/MRI Nanoparticles for Tumor Imaging

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    The development of integrated positron emission tomography (PET)/ magnetic resonance imaging (MRI) scanners opened a new scenario for cancer diagnosis, treatment, and follow-up. Multimodal imaging combines functional and morphological information from different modalities, which, singularly, cannot provide a comprehensive pathophysiological overview. Molecular imaging exploits multimodal imaging in order to obtain information at a biological and cellular level; in this way, it is possible to track biological pathways and discover many typical tumoral features. In this context, nanoparticle-based contrast agents (CAs) can improve probe biocompatibility and biodistribution, prolonging blood half-life to achieve specific target accumulation and non-toxicity. In addition, CAs can be simultaneously delivered with drugs or, in general, therapeutic agents gathering a dual diagnostic and therapeutic effect in order to perform cancer diagnosis and treatment simultaneous. The way for personalized medicine is not so far. Herein, we report principles, characteristics, applications, and concerns of nanoparticle (NP)-based PET/MRI CAs

    Molecular Imaging

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    The present book gives an exceptional overview of molecular imaging. Practical approach represents the red thread through the whole book, covering at the same time detailed background information that goes very deep into molecular as well as cellular level. Ideas how molecular imaging will develop in the near future present a special delicacy. This should be of special interest as the contributors are members of leading research groups from all over the world

    Regularized estimation and model selection in compartment models

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    Dynamic imaging series acquired in medical and biological research are often analyzed with the help of compartment models. Compartment models provide a parametric, nonlinear function of interpretable, kinetic parameters describing how some concentration of interest evolves over time. Aiming to estimate the kinetic parameters, this leads to a nonlinear regression problem. In many applications, the number of compartments needed in the model is not known from biological considerations but should be inferred from the data along with the kinetic parameters. As data from medical and biological experiments are often available in the form of images, the spatial data structure of the images has to be taken into account. This thesis addresses the problem of parameter estimation and model selection in compartment models. Besides a penalized maximum likelihood based approach, several Bayesian approaches-including a hierarchical model with Gaussian Markov random field priors and a model state approach with flexible model dimension-are proposed and evaluated to accomplish this task. Existing methods are extended for parameter estimation and model selection in more complex compartment models. However, in nonlinear regression and, in particular, for more complex compartment models, redundancy issues may arise. This thesis analyzes difficulties arising due to redundancy issues and proposes several approaches to alleviate those redundancy issues by regularizing the parameter space. The potential of the proposed estimation and model selection approaches is evaluated in simulation studies as well as for two in vivo imaging applications: a dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) study on breast cancer and a study on the binding behavior of molecules in living cell nuclei observed in a fluorescence recovery after photobleaching (FRAP) experiment

    Image Restoration for Fluorescence Planar Imaging with Diffusion Model

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