Efficient ultrasonic signal processing techniques for aided medical diagnostics

Ultrasound imaging is widely used in medical diagnostics as it is the fastest, least invasive, and least expensive imaging modality. However, ultrasound images are intrinsically difficult to be interpreted. In this scenario, Computer Aided Detection (CAD) systems can be used to support physicians during diagnosis providing them a second opinion. This thesis discusses efficient ultrasound processing techniques for computer aided medical diagnostics, focusing on two major topics: (i) Ultrasound Tissue Characterization (UTC), aimed at characterizing and differentiating between healthy and diseased tissue; (ii) Ultrasound Image Segmentation (UIS), aimed at detecting the boundaries of anatomical structures to automatically measure organ dimensions and compute clinically relevant functional indices. Research on UTC produced a CAD tool for Prostate Cancer detection to improve the biopsy protocol. In particular, this thesis contributes with: (i) the development of a robust classification system; (ii) the exploitation of parallel computing on GPU for real-time performance; (iii) the introduction of both an innovative Semi-Supervised Learning algorithm and a novel supervised/semi-supervised learning scheme for CAD system training that improve system performance reducing data collection effort and avoiding collected data wasting. The tool provides physicians a risk map highlighting suspect tissue areas, allowing them to perform a lesion-directed biopsy. Clinical validation demonstrated the system validity as a diagnostic support tool and its effectiveness at reducing the number of biopsy cores requested for an accurate diagnosis. For UIS the research developed a heart disease diagnostic tool based on Real-Time 3D Echocardiography. Thesis contributions to this application are: (i) the development of an automated GPU based level-set segmentation framework for 3D images; (ii) the application of this framework to the myocardium segmentation. Experimental results showed the high efficiency and flexibility of the proposed framework. Its effectiveness as a tool for quantitative analysis of 3D cardiac morphology and function was demonstrated through clinical validation.

Fault detection, diagnosis and active fault tolerant control for a satellite attitude control system

Modern control systems are becoming more and more complex and control algorithms more and more sophisticated. Consequently, Fault Detection and Diagnosis (FDD) and Fault Tolerant Control (FTC) have gained central importance over the past decades, due to the increasing requirements of availability, cost efficiency, reliability and operating safety. This thesis deals with the FDD and FTC problems in a spacecraft Attitude Determination and Control System (ADCS). Firstly, the detailed nonlinear models of the spacecraft attitude dynamics and kinematics are described, along with the dynamic models of the actuators and main external disturbance sources. The considered ADCS is composed of an array of four redundant reaction wheels. A set of sensors provides satellite angular velocity, attitude and flywheel spin rate information. Then, general overviews of the Fault Detection and Isolation (FDI), Fault Estimation (FE) and Fault Tolerant Control (FTC) problems are presented, and the design and implementation of a novel diagnosis system is described. The system consists of a FDI module composed of properly organized model-based residual filters, exploiting the available input and output information for the detection and localization of an occurred fault. A proper fault mapping procedure and the nonlinear geometric approach are exploited to design residual filters explicitly decoupled from the external aerodynamic disturbance and sensitive to specific sets of faults. The subsequent use of suitable adaptive FE algorithms, based on the exploitation of radial basis function neural networks, allows to obtain accurate fault estimations. Finally, this estimation is actively exploited in a FTC scheme to achieve a suitable fault accommodation and guarantee the desired control performances. A standard sliding mode controller is implemented for attitude stabilization and control. Several simulation results are given to highlight the performances of the overall designed system in case of different types of faults affecting the ADCS actuators and sensors.

Non Invasive Tools for Early Detection of Autism Spectrum Disorders

Autism Spectrum Disorders (ASDs) describe a set of neurodevelopmental disorders. ASD represents a significant public health problem. Currently, ASDs are not diagnosed before the 2nd year of life but an early identification of ASDs would be crucial as interventions are much more effective than specific therapies starting in later childhood. To this aim, cheap an contact-less automatic approaches recently aroused great clinical interest. Among them, the cry and the movements of the newborn, both involving the central nervous system, are proposed as possible indicators of neurological disorders. This PhD work is a first step towards solving this challenging problem. An integrated system is presented enabling the recording of audio (crying) and video (movements) data of the newborn, their automatic analysis with innovative techniques for the extraction of clinically relevant parameters and their classification with data mining techniques. New robust algorithms were developed for the selection of the voiced parts of the cry signal, the estimation of acoustic parameters based on the wavelet transform and the analysis of the infant’s general movements (GMs) through a new body model for segmentation and 2D reconstruction. In addition to a thorough literature review this thesis presents the state of the art on these topics that shows that no studies exist concerning normative ranges for newborn infant cry in the first 6 months of life nor the correlation between cry and movements. Through the new automatic methods a population of control infants (“low-risk”, LR) was compared to a group of “high-risk” (HR) infants, i.e. siblings of children already diagnosed with ASD. A subset of LR infants clinically diagnosed as newborns with Typical Development (TD) and one affected by ASD were compared. The results show that the selected acoustic parameters allow good differentiation between the two groups. This result provides new perspectives both diagnostic and therapeutic.

Robust Adaptive Control with Applications to Multi-Agent Systems and Mobile Robotics

This thesis deals with robust adaptive control and its applications, and it is divided into three main parts. The first part is about the design of robust estimation algorithms based on recursive least squares. First, we present an estimator for the frequencies of biased multi-harmonic signals, and then an algorithm for distributed estimation of an unknown parameter over a network of adaptive agents. In the second part of this thesis, we consider a cooperative control problem over uncertain networks of linear systems and Kuramoto systems, in which the agents have to track the reference generated by a leader exosystem. Since the reference signal is not available to each network node, novel distributed observers are designed so as to reconstruct the reference signal locally for each agent, and therefore decentralizing the problem. In the third and final part of this thesis, we consider robust estimation tasks for mobile robotics applications. In particular, we first consider the problem of slip estimation for agricultural tracked vehicles. Then, we consider a search and rescue application in which we need to drive an unmanned aerial vehicle as close as possible to the unknown (and to be estimated) position of a victim, who is buried under the snow after an avalanche event. In this thesis, robustness is intended as an input-to-state stability property of the proposed identifiers (sometimes referred to as adaptive laws), with respect to additive disturbances, and relative to a steady-state trajectory that is associated with a correct estimation of the unknown parameter to be found.

Solid state micro and nanopore sensors for single entity detection

A general description of the work presented in this thesis can be divided into three areas of interest: micropore fabrication, nanopore modification, and their applications. The first part of the thesis is related to the novel, reliable, cost-effective, potable, mass-productive, robust, and ease of use micropore flowcell that works based on the RPS technique. Based on our first goal, which was finding an alternate materials and processes that would shorten production times while lowering costs and improving signal quality, the polyimide film was used as a substrate to create precise pores by femtosecond laser, and the resulting current blockades of different sizes of the nanoparticles were recorded. Based on the results, the device can detecting nano-sized particles by changing the current level. The experimental and theoretical investigation, scanning electron microscopy, and focus ion beam were performed to explain the micropore's performance. The second goal was design and fabrication of a leak-free, easy-to-assemble, and portable polymethyl methacrylate flowcell for nanopore experiments. Here, ion current rectification was studied in our nanodevice. We showed a self-assembly-based, controllable, and monitorable in situ Poly(l-lysine)- g-poly(ethylene glycol) coating method under voltage-driven electrolyte flow and electrostatic interaction between nanopore walls and PLL backbones. Using designed nanopore flowcell and in situ monolayer PLL-g-PEG functionalized 20±4 nm SiN nanopores, we observed non-sticky α-1 anti-trypsin protein translocation. additionally, we could show the enhancement of translocation events through this non-sticky nanopore, and also, estimate the volume of the translocated protein. In this study, by comparing the AAT protein translocation results from functionalized and non-functionalized nanopore we demonstrated the 105 times dwell time reduction (31-0.59ms), 25% amplitude enhancement (0.24-0.3 nA), and 15 times event’s number increase (1-15events/s) after functionalization in 1×PBS at physiological pH. Also, the AAT protein volume was measured, close to the calculated AAT protein hydrodynamic volume and previous reports.

Drive up electrochemical and electrochemiluminescence signal through biosensing mechanism optimization

Biomarkers are biological indicators of human health conditions. Their ultra-sensitive quantification is of cardinal importance in clinical monitoring and early disease diagnosis. Biosensors are some worldwide simple and easy-to-use analytical devices as a matter of fact, biosensors using electrochemiluminescence (ECL) are one of the most promising biosensors that needs an ever-increasing sensitivity for improving its clinical effectiveness. The principal aspiration of this project is the investigation of the ECL generation mechanisms for enhancing the ECL intensity and the development of an ultrasensitive sensor, the use of metal-oxide materials (Mox) and the substitution of metal-free dyes. Novel dyes such as BODIPY, TADF are used to improve the sensitivity of ECL techniques thanks to their advantageous and tunable properties, enhancing the signal and also the ECL efficiency. Additionally, the use of Mox could be beneficial for the investigation of two different ECL mechanisms, which occur simultaneously. In this thesis, the investigation of size and distance effects on electrochemical (EC) mechanisms was carried out through the innovative combination of a standard detection system using different size of micromagnetic beads (MBs). That allowed the discovery of an unexpected and highly efficient mechanistic path for electrochemical generation at small distances from the electrode’s surface. The smallest MBs (0.1μm) demostrate an enhancement of electrochemical signal than the bigger one (2.8μm) until 4 times of magnitude. Finally, a novel ultrasensitive sensor, based on the coreactant-luminophores mechanism, was developed for the determination of whole viral genome specific for cardiac HBV and COVID-19 virus. In conclusion, the ECL and the use of EC techniques (such as amperometry), improved the understanding of mechanisms responsible for the ECL/EC signal led to a great enhancement in the signal.

Non-invasive detection of acute cell-mediated graft rejection in paediatric heart transplant recipients: the role of cardiovascular magnetic resonance

Background and Aim: Acute cardiac rejection is currently diagnosed by endomyocardial biopsy (EMB), but multiparametric cardiac magnetic resonance (CMR) may be a non-invasive alternative by its capacity for myocardial structure and function characterization. Our primary aim was to determine the utility of multiparametric CMR in identifying acute graft rejection in paediatric heart transplant recipients. The second aim was to compare textural features of parametric maps in cases of rejection versus those without rejection. Methods: Fifteen patients were prospectively enrolled for contrast-enhanced CMR followed by EMB and right heart catheterization. Images were acquired on a 1,5 Tesla scanner including T1 mapping (modified Look-Locker inversion recovery sequence – MOLLI) and T2 mapping (modified GraSE sequence). The extracellular volume (ECV) was calculated using pre- and post-gadolinium T1 times of blood and myocardium and the patient’s hematocrit. Markers of graft dysfunction including hemodynamic measurements from echocardiography, catheterization and CMR were collated. Patients were divided into two groups based on degree of rejection at EMB: no rejection with no change in treatment (Group A) and acute rejection requiring new therapy (Group B). Statistical analysis included student’t t test and Pearson correlation. Results: Acute rejection was diagnosed in five patients. Mean T1 values were significantly associated with acute rejection. A monotonic, increasing trend was noted in both mean and peak T1 values, with increasing degree of rejection. ECV was significantly higher in Group B. There was no difference in T2 signal between two groups. Conclusion: Multiparametric CMR serves as a noninvasive screening tool during surveillance encounters and may be used to identify those patients that may be at higher risk of rejection and therefore require further evaluation. Future and multicenter studies are necessary to confirm these results and explore whether multiparametric CMR can decrease the number of surveillance EMBs in paediatric heart transplant recipients.