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.

Modelling, diagnostics and experimental analysis of plasma assisted processes for material treatment

This work presents results from experimental investigations of several different atmospheric pressure plasmas applications, such as Metal Inert Gas (MIG) welding and Plasma Arc Cutting (PAC) and Welding (PAW) sources, as well as Inductively Coupled Plasma (ICP) torches. The main diagnostic tool that has been used is High Speed Imaging (HSI), often assisted by Schlieren imaging to analyse non-visible phenomena. Furthermore, starting from thermo-fluid-dynamic models developed by the University of Bologna group, such plasma processes have been studied also with new advanced models, focusing for instance on the interaction between a melting metal wire and a plasma, or considering non-equilibrium phenomena for diagnostics of plasma arcs. Additionally, the experimental diagnostic tools that have been developed for industrial thermal plasmas have been used also for the characterization of innovative low temperature atmospheric pressure non equilibrium plasmas, such as dielectric barrier discharges (DBD) and Plasma Jets. These sources are controlled by few kV voltage pulses with pulse rise time of few nanoseconds to avoid the formation of a plasma arc, with interesting applications in surface functionalization of thermosensitive materials. In order to investigate also bio-medical applications of thermal plasma, a self-developed quenching device has been connected to an ICP torch. Such device has allowed inactivation of several kinds of bacteria spread on petri dishes, by keeping the substrate temperature lower than 40 degrees, which is a strict requirement in order to allow the treatment of living tissues.