Spatial Mass Spectral Data Analysis Using Factor and Correlation Models

ToF-SIMS is a powerful and information rich tool with high resolution and sensitivity compared to conventional mass spectrometers. Recently, its application has been extended to metabolic profiling analysis. However, there are only a few algorithms currently available to handle such output data from metabolite samples. Therefore some novel and innovative algorithms are undoubtedly in need to provide new insights into the application of ToF-SIMS for metabolic profiling analysis. In this thesis, we develop novel multivariate analysis techniques that can be used in processing ToF-SIMS data extracted from metabolite samples. Firstly, several traditional multivariate analysis methodologies that have previously been suggested for ToF-SIMS data analysis are discussed, including Clustering, Principal Components Analysis (PCA), Maximum Autocorrelation Factor (MAF), and Multivariate Curve Resolution (MCR). In particular, PCA is selected as an example to show the performance of traditional multivariate analysis techniques in dealing with large ToF-SIMS data extracted from metabolite samples. In order to provide more realistic and meaningful interpretation of the results, Non-negative Matrix Factorisation (NMF) is presented. This algorithm is combined with the Bayesian Framework to improve the reliability of the results and the convergence of the algorithm. However, the iterative process involved leads to considerable computational complexity in the estimation procedure. Another novel algorithm is also proposed which is an optimised MCR algorithm within alternating non-negativity constrained least squares (ANLS) framework. It provides a more simple approximation procedure by implementing a dimensionality reduction based on a basis function decomposition approach. The novel and main feature of the proposed algorithm is that it incorporates a spatially continuous representation of ToF-SIMS data which decouples the computational complexity of the estimation procedure from the image resolution. The proposed algorithm can be used as an efficient tool in processing ToF-SIMS data obtained from metabolite samples

Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches

Imaging spectrometers measure electromagnetic energy scattered in their instantaneous field view in hundreds or thousands of spectral channels with higher spectral resolution than multispectral cameras. Imaging spectrometers are therefore often referred to as hyperspectral cameras (HSCs). Higher spectral resolution enables material identification via spectroscopic analysis, which facilitates countless applications that require identifying materials in scenarios unsuitable for classical spectroscopic analysis. Due to low spatial resolution of HSCs, microscopic material mixing, and multiple scattering, spectra measured by HSCs are mixtures of spectra of materials in a scene. Thus, accurate estimation requires unmixing. Pixels are assumed to be mixtures of a few materials, called endmembers. Unmixing involves estimating all or some of: the number of endmembers, their spectral signatures, and their abundances at each pixel. Unmixing is a challenging, ill-posed inverse problem because of model inaccuracies, observation noise, environmental conditions, endmember variability, and data set size. Researchers have devised and investigated many models searching for robust, stable, tractable, and accurate unmixing algorithms. This paper presents an overview of unmixing methods from the time of Keshava and Mustard's unmixing tutorial [1] to the present. Mixing models are first discussed. Signal-subspace, geometrical, statistical, sparsity-based, and spatial-contextual unmixing algorithms are described. Mathematical problems and potential solutions are described. Algorithm characteristics are illustrated experimentally.Comment: This work has been accepted for publication in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensin

Cell behavior on surfaces with different micropatterns and topographies

This thesis describes cell behaviour on two kinds of substrates with different chemical and topographical surface features: micropatterned and randomly modified substrates. Micropatterned surfaces were obtained by photoimmobilization of the polysaccharide hyaluronic acid (Hyal) on aminosilanized glass in the presence of photomasks with different geometries. The patterns obtained had different dimensions and chemistry: positive/negative spiral and squared micro-patterned surfaces of decreasing dimensions. The microstructured surfaces were characterized by AFM, SEM, ToF-SIMS, ATR/Ft-IR. SEM analysis allowed measurements of the micropattern’s dimensions: the spiral ranged from 100μm at the periphery to 1μm in the central part, the square and rectangle pattern consisted of a central square of 100x100μm and rectangles of different dimensions decreasing from the centre to the edges of the micropatterned area (2x1μm). The behaviour of four cell types was tested on these micropatterned surfaces: human coronary artery endothelial cells (HCAEC), human dermal fibroblasts, NIH 3T3 fibroblasts and human normal osteoblasts (NHOst). Each cell type was seeded separately. The cell parameters analyzed were cell morphology, adhesion, cytoskeleton changes and distribution by using SEM, AFM and inverted optical fluorescence microscopy. Cell adhesion analysis demonstrated that HCAEC, human dermal fibroblasts and NHOst did not adhere to the immobilised Hyal but adapted their shape to the different sizes of the square and spiral patterns of silanized glass. In particular, the number of adherent HCAEC and human dermal fibroblasts depended on the dimensions of both the glass domains and the nuclei of the cells. Also, in both geometric patterns, the reduction of the adhesive glass width induced human dermal fibroblasts to create bonds amongst themselves. NIH3T3 cells adhered inside the squares and the spiral, but reducing the adhesive glass domains width induced NIH3T3 to also adhere to immobilised Hyal probably due to the binding of cell’s specific receptor for Hyal, CD44 to photoimmobilized Hyal. Then co-cultures of different cell types were performed on micro-structured surfaces. Cell behaviour was evaluated and monitored by inverted optical and time-lapse video microscope. A heterotypic cell-cell interaction among two or three different cell types occurred in the same chemical and topographic micro-domains. By co-culturing fibroblasts with different dimensions (human dermal fibroblasts greater than NIH3T3) with already adhered HCAEC on patterned samples with different dimensions, it was demonstrated that the success of the co-culture did not depend on cell dimensions but rather on the dimension of adhesive microdomains. The simultaneous presence of HCAEC and NIH3T3 fibroblasts did not prevent the adhesion of human normal osteoblasts on the spiral pattern. In particular, areas containing three different types of cells were visible along the glass spiral pattern mostly on the external of the spiral, where the available space to spread was wider. Cell behaviour on randomly modified surfaces was also investigated. Randomly modified surfaces were sandblasted titanium disks, bare or coated with CMCAPh, a new phosphonate derivative of carboxymethylcellulose (CMC). The cells tested were human osteoblasts. Coating was used with the aim to increase the osteogenic activity of implant surfaces. The phosphonate polysaccharide was obtained by using a carbodiimide-like activating agent for carboxylic groups and 2-aminoethyl-phosphonic acid to create an amidic bond between the amine of the phosphonate agent and the carboxylic acids of CMC. The polymer was characterized by 31P-NMR, FT-IR and potentiometric titration. CMCAPh showed different properties from CMC and its amidated derivative polymer CMCA. Furthermore the polymer film on the titanium surface was characterized by AFM and TOF-SIMS analysis. An ATR FT-IR study was carried out to evaluate the polymer bonding mode onto the titanium surface. The effect of CMCAPh polymer in solution on normal human osteoblasts (NHOst) was studied in vitro, monitoring cell proliferation, cell differentiation and osteogenic activity and was then compared with that of the amidic derivative of carboxymethylcellulose (CMCA). Osteoblast morphology was evaluated by SEM. Adhesion analysis of normal human osteoblasts (NHOst) demonstrated a better adhesion on the titanium surface coated with CMCAPh than on bare titanium

Tensor electrical impedance myography identifies clinically relevant features in amyotrophic lateral sclerosis

Objective. Electrical impedance myography (EIM) shows promise as an effective biomarker in amyotrophic lateral sclerosis (ALS). EIM applies multiple input frequencies to characterise muscle properties, often via multiple electrode configurations. Herein, we assess if non-negative tensor factorisation (NTF) can provide a framework for identifying clinically relevant features within a high dimensional EIM dataset. Approach. EIM data were recorded from the tongue of healthy and ALS diseased individuals. Resistivity and reactivity measurements were made for 14 frequencies, in three electrode configurations. This gives 84 (2 × 14 × 3) distinct data points per participant. NTF was applied to the dataset for dimensionality reduction, termed tensor EIM. Significance tests, symptom correlation and classification approaches were explored to compare NTF to using all raw data and feature selection. Main Results. Tensor EIM provides highly significant differentiation between healthy and ALS patients (p < 0.001, AUROC = 0.78). Similarly tensor EIM differentiates between mild and severe disease states (p < 0.001, AUROC = 0.75) and significantly correlates with symptoms (ρ = 0.7, p < 0.001). A trend of centre frequency shifting to the right was identified in diseased spectra, which is in line with the electrical changes expected following muscle atrophy. Significance. Tensor EIM provides clinically relevant metrics for identifying ALS-related muscle disease. This procedure has the advantage of using the whole spectral dataset, with reduced risk of overfitting. The process identifies spectral shapes specific to disease allowing for a deeper clinical interpretation

Disseny i aplicació d’eines quimiomètriques per a l’anàlisi d’imatges hiperespectrals

[cat] Les imatges hiperespectrals són una mesura instrumental singular i de gran interès, ja que proporcionen informació química (espectral) i de distribució espacial (imatge) dels constituents de les mostres. Aquest fet les fa especialment interessants en aplicacions de la indústria farmacèutica, dels camps mediambiental i biomèdic i en la recerca i identificació de materials. L’objectiu d’aquesta tesi ha estat el coneixement de la naturalesa de la mesura de les imatges hiperespectrals amb la finalitat de dissenyar o adaptar eines d’anàlisi de dades més específiques i de proporcionar protocols d’actuació per a la interpretació d’aquest tipus de mesura en funció del tipus de tècnica espectroscòpica utilitzada i del problema químic d’interès. De manera específica, aquest treball s’ha centrat en l’estudi del potencial del mètode de resolució multivariant de corbes per mínims quadrats alternats, MCR-ALS, per a l’anàlisi d’imatges hiperespectrals, que proporciona mapes de distribució i espectres purs dels constituents de les imatges a partir únicament del coneixement de la mesura original. S’ha treballat amb l’anàlisi d’imatges individuals i l’anàlisi conjunta d’imatges obtingudes amb la mateixa tècnica o amb diferents plataformes espectroscòpiques. A partir de l’estudi d’imatges Raman i IR associades a problemes químics de diferents tipologies, s’han proposat protocols d’anàlisi que inclouen el preprocessat de les dades originals, l’obtenció dels mapes de distribució i espectres purs dels constituents de la imatge i el postprocessat dels mapes i espectres resolts per a l’obtenció d’informació addicional. L’ús dels mapes i espectres resolts proporciona informació molt diversa, com és ara la identificació, la quantificació i la caracterització de l’heterogeneïtat dels constituents de la imatge o la interpretació global i local d’un procés. Els mapes resolts han estat també una informació de partida excel·lent en altres tipus d’anàlisi, com la segmentació de la imatge o en procediments de superresolució, orientats a millorar la resolució espacial de les imatges instrumentals. La combinació de l’anàlisi multiconjunt de resolució i segmentació s’ha revelat de gran utilitat per a distingir poblacions de mostres de teixits biològics amb diferents estats patològics. Per últim, s’ha proposat un procediment per a la fusió i anàlisi d’imatges adquirides amb diferents tècniques espectroscòpiques i de diferent resolució espacial mitjançant una nova variant del mètode MCR-ALS per a estructures multiconjunt incompletes, que permet aprofitar la informació complementària de les tècniques acoblades i preservar la màxima resolució espacial.[eng] Hyperspectral images are unique instrumental measurements that contain chemical (spectral) information and detailed knowledge of the distribution of the sample constituents on the sample surface scanned. This thesis is mainly oriented to know in depth the nature of this instrumental measurement in order to design and adapt specific chemometric tools that help in the proposal of general protocols for the interpretation of hyperspectral images according to the spectroscopic technique used and the chemical problem of interest. Particularly, much work has been focused on the study of the potential of multivariate curve resolution-alternating least squares, MCR-ALS, for the analysis of hyperspectral images. This algorithm provides distribution maps and pure spectra for the image constituents from the sole information contained in the raw measurement. Within this framework, individual analysis of images and image multiset analysis on data structures formed by images collected with the same technique or by images coming from different spectroscopic platforms have been explored. From the study of Raman and IR hyperspectral images linked to different chemical problem typologies, data analysis protocols have been proposed that include preprocessing of original data, recovery of distribution maps and pure spectra of image constituents and postprocessing of resolved maps and pure spectra to obtain further information. Resolved distribution maps and pure spectra provide diverse information, such as identification, quantification and heterogeneity characterization of the image constituents or the global and local description of a process. The use of resolved distribution maps has proven to be an excellent starting point for other kinds of analysis, such as image segmentation or super-resolution algorithms, oriented to improve the spatial resolution of experimental hyperspectral images. Combined multiset resolution and segmentation analysis has been shown to be very useful for the differentiation of populations of biological tissues with different pathological conditions. Finally, a strategy for data fusion of hyperspectral images from different spectroscopic platforms and different spatial resolution has been proposed. This approach uses a new variant of MCR-ALS for incomplete multiset structures that takes advantage of the complementary information provided by the different spectroscopic techniques without losing spatial resolution

Mechanisms for the Incorporation and Distribution of Radionuclides in Near-Surface Fallout

Fallout is the radioactive glass formed from a mixture of vaporized anthropogenic materials with proximate environmental material. These glassy byproducts constitute a compositional record of environmental materials such as soil and vapor precursors that are responsible for chemical heterogeneity in the glasses. The work of this dissertation is to untangle these different sources of compositional heterogeneity, distinguishing low-abundance vaporized anthropogenic-rich material from natural compositions, in order examine soil behavior and chemical evolution during fallout formation. Unfortunately, mixing convolutes the multivariate elemental relationships in these melts due to overlapping element abundances that might distinguish different source materials and chemical behaviors, making bivariate analysis difficult or impossible to interpret. For these reasons historical and modern studies do not thoroughly investigated how soil affects deposition and incorporation of vaporized material in fallout. This work employs a two-step strategy to overcome these challenges and more effectively understand preserved heterogeneity. First (1), three prospective multivariate approaches are compared: classical least squares (CLS) and principal component analysis (PCA), which are commonly employed in the literature, with Multivariate Curve Resolution Alternating Least Squares (MCR-ALS) that is novel in this application. This analysis shows that a closure and equality constrained MCR-ALS approach succeeds in identifying unrecognized precursor components and is a suitable alternative to PCA, or CLS-style approaches in situations distinguished by limited a priori information of precursor compositions. Second (2), the MCR-ALS method is applied to unbiased spatial analyses from 13 Trinity fallout glasses to resolve the contribution and composition of the environmental or vaporized sources. This numerical approach reveals the importance of environmental precursors such as alkali feldspar, calcite, and quartz, to chemical heterogeneity in most glasses. In contrast to previous studies, however, the work resolves a high Al-Si-rich precursor constituting evidence for volatile loss in the melts. Likewise, an Ca- Fe-Mg-rich composition is also resolved, constituting possible evidence for a vapor source term. Correlation of spatial activity distributions, measured by autoradiography, with the compositional precursors from MCR-ALS in clear evidence supporting surface-driven condensation, wherein molten soil components serve as heterogeneous nucleation sites for the vapor source, agglomeration of small melts onto the surface of larger melts, and physical mixing of the vapor source into the interior of molten objects. This is the first report to provide quantitative model-supported evidence for key mechanisms affecting the composition of the vapor phase in a nuclear event and highlighting the impact of local materials on resultant fallout compositions

Context dependent spectral unmixing.

A hyperspectral unmixing algorithm that finds multiple sets of endmembers is proposed. The algorithm, called Context Dependent Spectral Unmixing (CDSU), is a local approach that adapts the unmixing to different regions of the spectral space. It is based on a novel function that combines context identification and unmixing. This joint objective function models contexts as compact clusters and uses the linear mixing model as the basis for unmixing. Several variations of the CDSU, that provide additional desirable features, are also proposed. First, the Context Dependent Spectral unmixing using the Mahalanobis Distance (CDSUM) offers the advantage of identifying non-spherical clusters in the high dimensional spectral space. Second, the Cluster and Proportion Constrained Multi-Model Unmixing (CC-MMU and PC-MMU) algorithms use partial supervision information, in the form of cluster or proportion constraints, to guide the search process and narrow the space of possible solutions. The supervision information could be provided by an expert, generated by analyzing the consensus of multiple unmixing algorithms, or extracted from co-located data from a different sensor. Third, the Robust Context Dependent Spectral Unmixing (RCDSU) introduces possibilistic memberships into the objective function to reduce the effect of noise and outliers in the data. Finally, the Unsupervised Robust Context Dependent Spectral Unmixing (U-RCDSU) algorithm learns the optimal number of contexts in an unsupervised way. The performance of each algorithm is evaluated using synthetic and real data. We show that the proposed methods can identify meaningful and coherent contexts, and appropriate endmembers within each context. The second main contribution of this thesis is consensus unmixing. This approach exploits the diversity and similarity of the large number of existing unmixing algorithms to identify an accurate and consistent set of endmembers in the data. We run multiple unmixing algorithms using different parameters, and combine the resulting unmixing ensemble using consensus analysis. The extracted endmembers will be the ones that have a consensus among the multiple runs. The third main contribution consists of developing subpixel target detectors that rely on the proposed CDSU algorithms to adapt target detection algorithms to different contexts. A local detection statistic is computed for each context and then all scores are combined to yield a final detection score. The context dependent unmixing provides a better background description and limits target leakage, which are two essential properties for target detection algorithms

Application of multivariate image analysis to prostate cancer for improving the comprehension of the related physiological phenomena and the development and validation of new imaging biomarkers

[ES] El aumento de la esperanza de vida en la población con edad por encima de 50 años está generando un mayor número de casos detectados de cáncer de próstata (CaP). Por este motivo, los recursos se destinan al diagnóstico en etapas tempranas y al tratamiento efectivo. A pesar de la multitud de estudios basados en biomarcadores y discriminación histológica, es difícil diferenciar con efectividad los casos de CaP con baja agresividad de aquellos que progresarán y acabarán produciendo mortalidad o una disminución en la esperanza de vida del paciente. Con el objetivo de mejorar el diagnostico, localización y gradación de los tumores malignos, las técnicas de imagen por Resonancia Magnética (MRI) son las más adecuadas para el estudio del cáncer, proporcionando métodos de diagnóstico no-invasivos, sensibles y específicos, basados en secuencias morfológicas (T2w) y funcionales (perfusión de la sangre y difusión del agua). Las diferentes características y parámetros extraídos de estas secuencias, conocidos como biomarcadores de imagen, pueden evaluar las diferencias asociadas al desarrollo de los procesos tumorales, como los modelos farmacocinéticos para estudiar angiogénesis (perfusión) y los modelos mono- y bi-exponenciales para estudiar la caída de la señal en difusión con el objetivo de estudiar la celularización. Normalmente, estos biomarcadores de imagen se analizan de forma "univariante", sin aprovechar la información de las estructuras de correlación interna que existen entre ellos. Una manera de mejorar este análisis es mediante la aplicación de las técnicas estadísticas que ofrece el Análisis Multivariante de Imágenes (MIA), obteniendo estructuras (latentes) simplificadas que ayudan a entender la relación entre los parámetros (variables) y sus propios procesos fisiológicos, además de reducir la incertidumbre en la estimación de los biomarcadores. En esta tesis, se han desarrollado nuevos biomarcadores de imagen para perfusión y difusión con la aplicación de alguna de las herramientas de MIA como la Resolución Multivariante de Curvas con Mínimos Cuadrados Alternos (MCR-ALS), obteniendo parámetros que tienen interpretación clínica directa. A continuación, los métodos basados en mínimos cuadrados parciales (PLS) se aplicaron para estudiar la capacidad de clasificación de estos biomarcadores. En primer lugar, los biomarcadores de perfusión se utilizaron para la detección de tumores (control vs lesión). Posteriormente, la combinación de perfusión + difusión + T2 se empleó para estudiar agresividad tumoral con la aplicación de métodos PLS multibloque, en concreto (secuencial) SMB-PLS. Los resultados mostrados indican que los biomarcadores de perfusión obtenidos mediante MCR son mejores que los parámetros farmacocinéticos en la diferenciación de la lesión. Con lo que respecta al estudio de la agresividad tumoral, la combinación de los biomarcadores de difusión (empleando ambos métodos: modelos paramétricos y MCR) y los valores de T2w normalizados proporcionaron los mejores resultados. En conclusión, MIA se puede aplicar a las secuencias morfológicas y funcionales de resonancia magnética para mejorar el diagnóstico y el estudio de la agresividad de los tumores en próstata. Obteniendo nuevos parámetros cuantitativos y combinándolos con los biomarcadores más ampliamente utilizados en el ambiente clínico.[CA] El increment de la esperança de vida en la població per damunt dels 50 anys està generant un major nombre de casos detectats de càncer de pròstata (CaP). Per aquest motiu, els recursos es destinen al diagnòstic en etapes primerenques i al tractament efectiu. Tot i la multitud de estudis basats en biomarcadors y discriminació histològica, es difícil diferenciar amb efectivitat els casos de CaP que tenen baixa agressivitat dels que progressaran y acabaran produint mortalitat o una disminució en la esperança de vida del pacient. Amb el objectiu de millorar el diagnòstic, localització y gradació dels tumors malignes, les tècniques de imatge per Ressonància Magnètica (MRI) son els mètodes més adequats per al estudi del càncer, proporcionant metodologies de diagnòstic no-invasius, sensibles y específiques basades en seqüències morfològiques (T2w) y funcionals (perfusió de la sang y difusió del aigua). Les diferents característiques i paràmetres extrets de aquestes seqüències, coneguts com biomarcadors d'imatge, poden avaluar les diferències associades al desenvolupament dels processos tumorals. Primer, amb els models farmacocinétics per a estudiar angiogènesis (perfusió) y segon, amb els models mono- i bi-exponencials per a estudiar la caiguda de la senyal en difusió amb el objectiu de estudiar la cel·lularització. Normalment, aquests biomarcadors d'imatge s'analitzen de forma "univariant", sense aprofitar la informació de las estructures de correlació interna que existeixen entre ells. Una forma de millorar aquest anàlisis es mitjançant la aplicació de las tècniques estadístiques aportades pel Anàlisis Multivariant de Imatges (MIA), obtenint estructures (latents) simplificades què ajuden a entendre la relació entre els paràmetres (variables) i els seus processos fisiològics, a més de reduir la incertesa en la estimació dels biomarcadors. En aquesta tesis, s'han desenvolupat nous biomarcadors d'imatge per a perfusió i difusió amb la aplicació de alguna de las ferramentes de MIA com la Resolució Multivariant de Corbes i Mínims Quadrats Alterns (MCR-ALS), obtenint paràmetres què tenen interpretació clínica directa. A continuació, els mètodes basats en mínims quadrats parcials (PLS) s'han aplicat per a estudiar la capacitat de classificació d'aquests biomarcadors. En primer lloc, els biomarcadors de perfusió s'han utilitzat per a la detecció de tumors (control contra lesió). Posteriorment, la combinació de perfusió + difusió + T2 s'ha utilitzat per a estudiar agressivitat tumoral amb la aplicació de mètodes PLS multi-bloc, en concret (seqüencial) SMB-PLS. Els resultats mostren què els biomarcadors de perfusió obtinguts mitjançant MCR són millors què els paràmetres farmacocinètics en la diferenciació de la lesió. En lo què es refereix al estudi de la agressivitat tumoral, la combinació dels biomarcadors de difusió (utilitzant els dos mètodes: models paramètrics i MCR) i els valors de T2w normalitzats proporcionaren els millors resultats. En conclusió, MIA es pot aplicar a les seqüències morfològiques i funcionals de ressonància magnètica per a millorar el diagnòstic i el estudi de l'agressivitat dels tumors en pròstata. Obtenint nous paràmetres quantitatius y combinant-los amb els biomarcadors més utilitzats en el ambient clínic.[EN] The increase in life expectancy and population with age higher than 50 years is producing a major number of detected cases of prostate cancer (PCa). For this reason, the resources are focused in the early diagnosis and effective treatment. In spite of multiple studies with histologic discriminant biomarkers, it is hard to clearly differentiate the low aggressiveness PCa cases from those that will progress and produce mortality or rather a decrease in the life expectancy. With the objective of improving the diagnosis, location and gradation of the malignant tumors, Magnetic Resonance Imaging (MRI) has come up as the most appropriate image acquisition technique for cancer studies, which provides a non-invasive, sensitive and specific diagnosis, based on morphological and functional (blood perfusion and water diffusion) sequences. The different characteristics and parameters extracted from these sequences, known as imaging biomarkers, can evaluate the different processes associated to tumor development, like pharmacokinetic modeling for angiogenesis assessment (perfusion) or mono- and bi-exponential signal decay modeling for cellularization (diffusion). Normally, these imaging biomarkers are analyzed in a "univariate" way, without taking advantage of the internal correlation structures among them. One way to improve this analysis is by applying Multivariate Image Analysis (MIA) statistical techniques, obtaining simplified (latent) structures that help to understand the relation between parameters (variables) and the inner physiological processes, moreover reducing the uncertainty in the estimation of the biomarkers. In this thesis, new imaging biomarkers are developed for perfusion and diffusion by applying MIA tools like Multivariate Curve Resolution Alternating Least Squares (MCR-ALS), obtaining parameters with direct clinical interpretation. Partial Least Squares (PLS) based methods are then used for studying the classification capability of these biomarkers. First, perfusion imaging biomarkers have been tested for tumor detection (control vs lesion). Then, diffusion + perfusion have been combined to study tumor aggressiveness by applying PLS-multiblock methods (SMB-PLS). The results showed that MCR-based perfusion biomarkers performed better than state-of-the-art pharmacokinetic parameters for lesion differentiation. Regarding the assessment of tumor aggressiveness, the combination of diffusion-based imaging biomarkers (using both the parametric models and MCR) and normalized T2-weighted measurements provided the best discriminating outcome, while perfusion was not needed as it did not supply additional information. In conclusion, MIA can be applied to morphologic and functional MRI to improve the diagnosis and aggressiveness assessment of prostate tumors by obtaining new quantitative parameters and combining them with state-of-the-art imaging biomarkers.Aguado Sarrió, E. (2019). Application of multivariate image analysis to prostate cancer for improving the comprehension of the related physiological phenomena and the development and validation of new imaging biomarkers [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/134023TESI

Development of 2D- and 3D-BTEM for pattern recognition in higher-order spectroscopic and other data arrays

Ph.DDOCTOR OF PHILOSOPH