Εργαστήριο Ηλεκτρονικής

Μόνιμο URI για αυτήν την κοινότηταhttps://dspace.library.tuc.gr/handle/123456789/43

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In vivo molecular imaging of epithelial pre-cancers based on dynamic optical scattering modeling
(Technical University of Crete, 2014) Papoutsoglou Georgios; Παπουτσογλου Γεωργιος; Balas Costas; Μπαλας Κωστας; Papageorgiou Markos; Παπαγεωργιου Μαρκος; Kalaitzakis Kostas; Καλαϊτζακης Κωστας; Stavrakakis Georgios; Σταυρακακης Γεωργιος; Zervakis Michalis; Ζερβακης Μιχαλης
We present a novel biophotonic method and imaging modality for estimating and mapping neoplasia-specific functional and structural parameters of the cervical precancerous epithelium. Estimations were based on experimental data obtained from dynamic contrast-enhanced optical imaging of cervix, in vivo. The dynamic characteristics of the measured optical signal are governed by the epithelial transport effects of the biomarker. A compartmental, pharmacokinetic, model of the cervical neoplastic epithelium has been developed, which predicts the dynamic optical effects in all possible parameter value combinations. Nine biological parameters, both structural and func-tional, have been identified to be potentially correlated with the neoplasia growth and to be mani-fested to the measured data in a convoluted manner. We have performed Global Sensitivity Analy-sis for the purpose of identifying the subset of the input parameters that are the key determinants of the model’s output. We have for the first time shown that it is possible to estimate, from in vivo measured dynamic optical data, the following neoplasia related parameters: number of neoplastic layers, extracellular space dimensions, functionality of tight junctions and extracellular pH. Global optimization techniques showed that the estimations of our method are of adequate accuracy and precision. Particularly, the Differential Evolution algorithm converged to the set of the four, most identifiable, parameters with an error of roughly 1%. We show that the estimated, in two millions of pixels, values of the four parameters are quite consistent with information provided in the literature. Our results are unique in the sense that for the first time functional and microstructural parameter maps can be estimated and displayed together, thus maximizing the diagnostic information. The quantity and the quality of this information are unattainable by other invasive and non invasive methods. The findings of this thesis suggest strongly that our method can improve our understand-ing of the neoplasia development mechanisms and of tumor growth and metastasis physiology. Corollary, it may become a valuable diagnostic tool that will also facilitate the development and evaluation of new cancer therapies.
Spectral deconvolution and concentration mapping in complex biochemical stains
(Technical University of Crete, 2014) Abatzi Fani; Αμπατζη Φανη; Balas Costas; Μπαλας Κωστας; Epitropou Georgios; Επιτροπου Γεωργιος; Zervakis Michalis; Ζερβακης Μιχαλης; Maravelaki Pagona; Μαραβελακη Παγωνα
Spectral Imaging (SI) combines spectroscopy and imaging, enabling the acquisition of a stack of images at narrow spectral bands comprising the so-called spectral cube. A complete spectrum can be calculated for every image pixel from the multidimensional spatio-spectral space of the cube. This study aims at identifying the concentration of solvents in mixtures of multiple biochemical stains with overlapping spectral signatures. More specifically, a series of experiments has been undertaken via experimental design arrangements (full factorial, face-centered & half factorial) employing both spectrum acquisition by spectrophotometer and spectral imaging acquisition. Furthermore, an extensive number of algorithmic methods, based on Beer Lambert's law generalization, including Classical Least Squares (CLS), Inverse Least Squares (ILS) with forward or backward selection, Principal Components Regression (PCR) and Partial Least Squares (PLS) has been implemented and applied to both simulated and experimental data. It was found that PLS can predict the concentrations in mixtures of two and three solvents with high accuracy on the datasets of spectral images. The combination of SI with concentration prediction algorithms can provide a valuable tool for quantitative assessment of the uptake of biological stains in histochemistry applications.

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