Simultaneous use of Individual and Joint Regularization Terms in Compressive Sensing: Joint Reconstruction of Multi-Channel Multi-Contrast MRI Acquisitions

Purpose: A time-efficient strategy to acquire high-quality multi-contrast images is to reconstruct undersampled data with joint regularization terms that leverage common information across contrasts. However, these terms can cause leakage of uncommon features among contrasts, compromising diagnostic utility. The goal of this study is to develop a compressive sensing method for multi-channel multi-contrast magnetic resonance imaging (MRI) that optimally utilizes shared information while preventing feature leakage. Theory: Joint regularization terms group sparsity and colour total variation are used to exploit common features across images while individual sparsity and total variation are also used to prevent leakage of distinct features across contrasts. The multi-channel multi-contrast reconstruction problem is solved via a fast algorithm based on Alternating Direction Method of Multipliers. Methods: The proposed method is compared against using only individual and only joint regularization terms in reconstruction. Comparisons were performed on single-channel simulated and multi-channel in-vivo datasets in terms of reconstruction quality and neuroradiologist reader scores. Results: The proposed method demonstrates rapid convergence and improved image quality for both simulated and in-vivo datasets. Furthermore, while reconstructions that solely use joint regularization terms are prone to leakage-of-features, the proposed method reliably avoids leakage via simultaneous use of joint and individual terms. Conclusion: The proposed compressive sensing method performs fast reconstruction of multi-channel multi-contrast MRI data with improved image quality. It offers reliability against feature leakage in joint reconstructions, thereby holding great promise for clinical use.Comment: 13 pages, 13 figures. Submitted for possible publicatio

Forschungsbericht Universität Mannheim, 2004 / 2005

Die Universität Mannheim gibt in dem vorliegenden Forschungsbericht 2004/2005 Rechenschaft über ihre Leistungen auf dem Gebiet der Forschung. Erstmals folgt diese Dokumentation einer neuen Gliederung, die auf einen Beschluss des Forschungsrates der Universität Mannheim zurückgeht. Wie gewohnt erhalten Sie einen Überblick über die Publikationen und Forschungsprojekte der Lehrstühle, Professuren und zentralen Forschungseinrichtungen. Diese werden ergänzt um Angaben zur Organisation von Forschungsveranstaltungen, der Mitwirkung in Forschungsausschüssen, einer Übersicht zu den für Forschungszwecke eingeworbenen Drittmitteln, zu den Promotionen und Habilitationen, zu Preisen und Ehrungen und zu Förderern der Universität Mannheim. Abgerundet werden diese Daten durch zusammenfassende Darstellungen der Forschungsschwerpunkte und des Forschungsprofils der Fakultäten

Prototypenentwicklung eines oberflächen-integrierten Mikrosensor Systems für 3D Traktionskraftmessungen durch DHM/DIC

In times of a rapid development and growing market in robotics, high-tech protheses and the personalization of medicine, biomimicking natural materials like artificial tissue are of central interest within research and industry. To fully understand the structure-function relations within living systems, comprehensive knowledge about the smallest living block, the cell, and its biomechanics are a central topic in world-wide research. However, there is so far no comprehensive technique established that can measure 3D cell forces simultaneously and quantitatively. In this project, a novel surface-integrated mechano-optical microsensor system has therefore been conceptualized, prototyped and tested, which allows for the record of pico- to micronewton traction forces in three dimensions simultaneously. First, adequate microsensor elements were designed via topology optimization and linear static finite element analysis. These designs were fabricated by micromachining processes of biocompatible thin films of nickel-titanium and amorphous silicon. Furthermore, a plasma etching process was developed to fabricate polydimethylsiloxane sensor elements. For accurate and quantitative traction force measurements, AFM cantilever based calibrations of the out-of-plane and in-plane sensor element spring constants were established. For the first time, a diamagnetic levitation force calibrator was used as an adequate pre-calibration method for the sensor elements with a high accuracy of 1 %. For the cost-efficient, simple, compact, variable and sensitive mechano-optical readout, a setting was conceptualized and tested based on the combination of digital holography and digital image correlation. To control cell adhesion, a high-throughput micro-nano structuring method was developed based on the fusion of ink-jet printing with the established method of diblock-copolymer micelle nanolithography.In Zeiten schneller Entwicklung und wachsender Märkte in der Robotik, der high-tech Prothetik und der personalisierten Medizin ist die Biomimetik natürlicher Materialien wie beispielsweise künstliche Haut von zentralem Interesse in Forschung und Industrie. Um die Struktur-Funktions-Beziehungen in lebenden Systemen umfassend zu verstehen ist die umfangreiche Wissenserweiterung hinsichtlich des kleinsten lebenden Bausteins, der Zelle, und seiner Biomechanik Gegenstand weltweiter Forschungsprojekte. Dennoch gab es bis jetzt keine Methode, die 3D Zellkräfte simultan und quantitativ messen kann. In diesem Projekt wurde ein neuartiges, oberflächen-integriertes, mechano-optisches Mikrosensorsystem konzeptioniert, prototypisiert und getestet, das die Messung piko-bis mikronewton kleiner Zugkräfte gleichzeitig in alle drei Dimensionen ermöglicht. Die Sensorelemente wurden mittels Topologieoptimierung und linear statischer Finite Elementanalyse konzipiert. Diese Designs wurden in Mikromaterialbearbeitungsprozessen aus biokompatiblen Nickel-Titan und amorphen Silizium-Dünnschschichten hergestellt. Desweiteren wurde ein Prozess entwickelt, um Polydimethylsiloxan basierte Sensorelemente herzustellen. Für genaue, quantitative Zugkraftmessungen wurden AFM-Cantilever basierte Kalibrierungen der axialen und lateralen Sensorelement-Federkonsten etabliert. Zum ersten Mal wurde dabei ein diamagnetischer Levitationskraftkalibrator mit einer Genauigkeit von 1% als geeignete Kalibrierungsmethode für die Sensorelemente genutzt. Für eine günstige, einfache, kompakte, variable und im Nanometerbereich empfindliche mechano-optische Datenauslesung wurde ein Aufbau konzeptioniert und getestet, in dem digitale Holographie und digitale Bildkorrelation kombiniert werden. Zur Zell-Adhäsionskontrolle wurde eine Hochdurchsatz-Mikro-Nanostrukturierungsmethode entwickelt, die auf der Kombination von Ink-Jet Drucken mit der etablierten Methode der Diblock-Copolymer Mizellen Nanolithographie basiert

Exploring 3D Shapes through Real Functions

This thesis lays in the context of research on representation, modelling and coding knowledge related to digital shapes, where by shape it is meant any individual object having a visual appareance which exists in some two-, three- or higher dimensional space. Digital shapes are digital representations of either physically existing or virtual objects that can be processed by computer applications. While the technological advances in terms of hardware and software have made available plenty of tools for using and interacting with the geometry of shapes, to manipulate and retrieve huge amount of data it is necessary to define methods able to effectively code them. In this thesis a conceptual model is proposed which represents a given 3D object through the coding of its salient features and defines an abstraction of the object, discarding irrelevant details. The approach is based on the shape descriptors defined with respect to real functions, which provide a very useful shape abstraction method for the analysis and structuring of the information contained in the discrete shape model. A distinctive feature of these shape descriptors is their capability of combining topological and geometrical information properties of the shape, giving an abstraction of the main shape features. To fully develop this conceptual model, both theoretical and computational aspects have been considered, related to the definition and the extension of the different shape descriptors to the computational domain. Main emphasis is devoted to the application of these shape descriptors in computational settings; to this aim we display a number of application domains that span from shape retrieval, to shape classification and to best view selection.Questa tesi si colloca nell\u27ambito di ricerca riguardante la rappresentazione, la modellazione e la codifica della conoscenza connessa a forme digitali, dove per forma si intende l\u27aspetto visuale di ogni oggetto che esiste in due, tre o pi? dimensioni. Le forme digitali sono rappresentazioni di oggetti sia reali che virtuali, che possono essere manipolate da un calcolatore. Lo sviluppo tecnologico degli ultimi anni in materia di hardware e software ha messo a disposizione una grande quantit? di strumenti per acquisire, rappresentare e processare la geometria degli oggetti; tuttavia per gestire questa grande mole di dati ? necessario sviluppare metodi in grado di fornirne una codifica efficiente. In questa tesi si propone un modello concettuale che descrive un oggetto 3D attraverso la codifica delle caratteristiche salienti e ne definisce una bozza ad alto livello, tralasciando dettagli irrilevanti. Alla base di questo approccio ? l\u27utilizzo di descrittori basati su funzioni reali in quanto forniscono un\u27astrazione della forma molto utile per analizzare e strutturare l\u27informazione contenuta nel modello discreto della forma. Una peculiarit? di tali descrittori di forma ? la capacit? di combinare propriet? topologiche e geometriche consentendo di astrarne le principali caratteristiche. Per sviluppare questo modello concettuale, ? stato necessario considerare gli aspetti sia teorici che computazionali relativi alla definizione e all\u27estensione in ambito discreto di vari descrittori di forma. Particolare attenzione ? stata rivolta all\u27applicazione dei descrittori studiati in ambito computazionale; a questo scopo sono stati considerati numerosi contesti applicativi, che variano dal riconoscimento alla classificazione di forme, all\u27individuazione della posizione pi? significativa di un oggetto

A perceptual learning model to discover the hierarchical latent structure of image collections

Biology has been an unparalleled source of inspiration for the work of researchers in several scientific and engineering fields including computer vision. The starting point of this thesis is the neurophysiological properties of the human early visual system, in particular, the cortical mechanism that mediates learning by exploiting information about stimuli repetition. Repetition has long been considered a fundamental correlate of skill acquisition andmemory formation in biological aswell as computational learning models. However, recent studies have shown that biological neural networks have differentways of exploiting repetition in forming memory maps. The thesis focuses on a perceptual learning mechanism called repetition suppression, which exploits the temporal distribution of neural activations to drive an efficient neural allocation for a set of stimuli. This explores the neurophysiological hypothesis that repetition suppression serves as an unsupervised perceptual learning mechanism that can drive efficient memory formation by reducing the overall size of stimuli representation while strengthening the responses of the most selective neurons. This interpretation of repetition is different from its traditional role in computational learning models mainly to induce convergence and reach training stability, without using this information to provide focus for the neural representations of the data. The first part of the thesis introduces a novel computational model with repetition suppression, which forms an unsupervised competitive systemtermed CoRe, for Competitive Repetition-suppression learning. The model is applied to generalproblems in the fields of computational intelligence and machine learning. Particular emphasis is placed on validating the model as an effective tool for the unsupervised exploration of bio-medical data. In particular, it is shown that the repetition suppression mechanism efficiently addresses the issues of automatically estimating the number of clusters within the data, as well as filtering noise and irrelevant input components in highly dimensional data, e.g. gene expression levels from DNA Microarrays. The CoRe model produces relevance estimates for the each covariate which is useful, for instance, to discover the best discriminating bio-markers. The description of the model includes a theoretical analysis using Huber’s robust statistics to show that the model is robust to outliers and noise in the data. The convergence properties of themodel also studied. It is shown that, besides its biological underpinning, the CoRe model has useful properties in terms of asymptotic behavior. By exploiting a kernel-based formulation for the CoRe learning error, a theoretically sound motivation is provided for the model’s ability to avoid local minima of its loss function. To do this a necessary and sufficient condition for global error minimization in vector quantization is generalized by extending it to distance metrics in generic Hilbert spaces. This leads to the derivation of a family of kernel-based algorithms that address the local minima issue of unsupervised vector quantization in a principled way. The experimental results show that the algorithm can achieve a consistent performance gain compared with state-of-the-art learning vector quantizers, while retaining a lower computational complexity (linear with respect to the dataset size). Bridging the gap between the low level representation of the visual content and the underlying high-level semantics is a major research issue of current interest. The second part of the thesis focuses on this problem by introducing a hierarchical and multi-resolution approach to visual content understanding. On a spatial level, CoRe learning is used to pool together the local visual patches by organizing them into perceptually meaningful intermediate structures. On the semantical level, it provides an extension of the probabilistic Latent Semantic Analysis (pLSA) model that allows discovery and organization of the visual topics into a hierarchy of aspects. The proposed hierarchical pLSA model is shown to effectively address the unsupervised discovery of relevant visual classes from pictorial collections, at the same time learning to segment the image regions containing the discovered classes. Furthermore, by drawing on a recent pLSA-based image annotation system, the hierarchical pLSA model is extended to process and representmulti-modal collections comprising textual and visual data. The results of the experimental evaluation show that the proposed model learns to attach textual labels (available only at the level of the whole image) to the discovered image regions, while increasing the precision/ recall performance with respect to flat, pLSA annotation model